# Output Database Definition¶

Aimsun Next has a generic format that accommodates data from macro, meso, micro, and hybrid experiments. The application generates two sets of tables: one that contains the information about what has been stored (meta information tables) and another with the information itself (information tables). Information tables are always stored in metric units.

Note: Density (veh/km) is calculated per lane, not section. Because a section can contain two or more lanes, please be aware of the impact of the number of lanes on the output value. For example, a single vehicle in a kilometer-long section with three lanes would return a density value of 0.33.

## Meta Information Tables¶

Aimsun Next will store information about the object that has generated the data into the SIM_INFO table and information about the data itself in the META_INFO, META_SUB_INFO, and META_COLS tables.

To see more specific information about a specific type of experiment refer to the following examples:

- Vehicle-Based Simulator Meta Information Table Examples
- Macroscopic Model Meta Information Table Examples
- Travel Demand Model Meta Information Table Examples

#### SIM_INFO Table¶

This table contains the ID of the object that has generated the data (a replication, a simulation result, a static experiment).

SIM_INFO | Type | Description |
---|---|---|

did | INTEGER | ID of the object that generates this data |

didname | VARCHAR (255) | Name of the object that generates this data |

efdid | INTEGER | Effective did of this replication (provided for future developments) |

dideid | VARCHAR(255) | External ID of the object that generates this data |

use_eid | INTEGER | 0: don't use external ID, 1: use external ID |

twhen | VARCHAR(10) (ISO 8601) | Scenario date |

from_time | INTEGER (seconds) | Simulation/Assignment starting time from midnight |

duration | INTEGER (seconds) | Simulation/Assignment duration |

seed | INTEGER | Random seed of the object that generates this data |

type | INTEGER | 1: simulated data, 2: average |

warm_up | INTEGER (seconds) | Warm-up time in seconds |

loading | VARCHAR(64) | Simulation model |

mod_ver | VARCHAR(255) | Aimsun Next version |

iterations | INTEGER | Number of iterations |

exec_data | VARCHAR(10) (ISO 8601) | Execution date |

xid | INTEGER | Experiment ID |

xname | VARCHAR(255) | Experiment Name |

scid | INTEGER | Scenario ID |

scname | VARCHAR(255) | Scenario Name |

simstatintervals | INTEGER | Number of simulated statistics intervals |

totalstatintervals | INTEGER | Number of total statistics intervals |

simdetecintervals | INTEGER | Number of simulated detection intervals |

totaldetecintervals | INTEGER | Number of total detection intervals |

model | VARCHAR(255) | Model unique ID |

trafficdemand | INTEGER | Traffic Demand ID |

ptplan | INTEGER | Transit Plan ID |

masterplan | INTEGER | Master Control Plan ID |

exec_date_end | VARCHAR(32) | Date and time simulation finished |

user_name | VARCHAR(255) | User running the simulation |

#### META_INFO Table¶

This table has the information about the stored tables (for sections, nodes, turns...). More information about which vehicle types have been used is located in the META_SUB_INFO table.

META_INFO | Type | Description |
---|---|---|

did | INTEGER | ID of the object that generates this data |

tname | VARCHAR(128) | Table name |

tyname | VARCHAR (128) | The GKType name (if any) |

nbo | INTEGER | Number of objects in this table |

souse | INTEGER | 0: do not use subobjects, 1: use subobjects |

sob | INTEGER | Number of subobjects (number of vehicle types plus one for vehicle All) |

eiduse | INTEGER | 0: do not use External ID to identify the objects instead of ID, 1: use External ID to identify the objects instead of ID |

sinterval | INTEGER (milliseconds) | The gathering interval duration |

nbkeys | INTEGER | Number of keys in this table |

#### META_SUB_INFO Table¶

This table contains the information about the vehicles types used to gather the data. It lists the object ID, the name and the position at which it appears in the information tables (from 0 for the aggregated data, to N).

META_SUB_INFO | Type | Description |
---|---|---|

did | INTEGER | ID of the object that generates this data |

tname | VARCHAR(128) | Table name |

pos | INTEGER | Position in tname table |

oid | INTEGER | Object ID |

oname | VARCHAR(128) | Object name |

#### META_COLS Table¶

This table lists, for each information table, the fields stored, and its type. The aggregation type details if this *data* has been created as a sum of values (for example *count*) or as a mean of values (speed).

META _COLS | Type | Description |
---|---|---|

did | INTEGER | ID of the object that generates this data |

tname | VARCHAR(128) | Table name |

colname | VARCHAR(128) | Field name |

coltype | INTEGER(QVariant::Type) | Data Type. Usually a double (6) |

aggtype | INTEGER | 0: Direct Mean, 1: Direct Value |

intervalaggtype | INTEGER | 0: No aggregation, 1: Addition, 2: Direct Mean, 3: Weighted by vehicle counts, 4: Maximum, 5: Last interval value |

## Information Tables¶

Information tables vary from model to model but they have a common structure. The name of an information table has a prefix to indicate the model that has generated it:

- MI: Aimsun Next Micro
- ME: Aimsun Next Meso
- MA: Aimsun Next Macro
- HY: Aimsun Next Hybrid

The common structure is:

Attribute name | Type | Description |
---|---|---|

did | INTEGER | ID of the replication/ average /experiment that generates this data |

oid | INTEGER | ID of the objects that generates this data |

eid | VARCHAR(128) | External ID of the object that generates this data |

sid | INTEGER | Subobject position (as set in META_SUB_INFO) |

ent | INTEGER | Interval number, from 1 to N and 0 reserved for the aggregated value |

After these common fields, a list of fields with the effective information appears. Each field is listed twice, once with the value, another one with the standard deviation. The name of the fields are listed in the META_COLS table. The standard deviation field name is created adding the suffix _D to the field name. For example: speed and speed_D. If a field has no standard deviation (its aggregation type is Sum) then the second field does not appear.

Aimsun Next has a generic format that accommodates data from macro, meso, micro, and hybrid experiments. The application generates two set of tables, one that contains the information about what has been stored (meta information tables) and another with the information itself (information tables). Information tables are always stored in metric units.

To see more specific information about a specific type of experiment refer:

- Vehicle-Based Simulator Information Tables Example
- Macroscopic Model Information Tables Example
- Travel Demand Model Information Tables Example

## Vehicle-Based Simulator Tables¶

Aimsun Next has a generic format that accommodates data from micro, meso, and hybrid experiments.

### Meta Information Table Examples¶

The following example shows the contents of all the meta information tables (for sections and system).

#### SIM_INFO Table Example¶

Table for a replication with ID 312 and simulated from midnight and for 1 hour.

did | didname | efdid | dideid | use_eid | twhenime | from_t | duration | seed | type | warm_up | loading | mod_ver | iterations | exec_date | xid | xname | scid | scname | simstatintervals | totalstatintervals | simdetecintervals | totaldetecintervals | model |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

312 | Replication 321 | 312 | 0 | 2010-10-11 | 0 | 3600 | 13775 | 1 | 0 | hybrid | 7.0.0 (R11593) | 1 | 2011-05-10 T15:50:28 | 307 | Experiment 307 | 306 | Dynamic Scenario 306 | 0 | 1 | 2 | 12 | {d42e81f1-b371-4c07-a31d-3970ae1754c8} |

#### META_INFO Table Example¶

Gathering data for the system (MISYS table) and sections (MISECT table) and two vehicle types (sob: 2 + 1). There are two sections (nbo) and the gathering interval is 600 seconds.

did | tname | tyname | nbo | souse | sob | eiduse | sinterval | nbkeys |
---|---|---|---|---|---|---|---|---|

285 | MISYS | GKReplication | 1 | 1 | 3 | 0 | 600000 | 1 |

285 | MISECT | GKSection | 2 | 1 | 3 | 0 | 600000 | 1 |

#### META_SUB_INFO Table Example¶

This table contains information for the vehicle types found in the information tables. The sid field in the information tables corresponds to the pos field in this table and from there the ID and the name of each vehicle type is listed. In this case for MISYS and MISECT tables, position 1 is for car (ID 8), position 2 is for van (ID 12) and the aggregated value (ID 0, shown as All in the UI) is position 0.

did | tname | pos | oid | oname |
---|---|---|---|---|

285 | MISYS | 0 | 0 | |

285 | MISYS | 1 | 8 | Car |

285 | MISYS | 2 | 12 | Van |

285 | MISECT | 0 | 0 | |

285 | MISECT | 1 | 8 | Car |

285 | MISECT | 2 | 12 | Van |

#### META_COLS Table Example¶

The list of all the fields in the information tables is shown below. All the values are stored as double (coltype 6) and are aggregated either as a sum(aggtype 3) or as a mean (aggtype 2).

did | tname | colname | coltype | aggtypes |
---|---|---|---|---|

285 | MISYS | flow | 6 | 3 |

285 | MISYS | ttime | 6 | 2 |

285 | MISYS | density | 6 | 3 |

285 | MISYS | stime | 6 | 2 |

285 | MISYS | dtime | 6 | 2 |

285 | MISYS | speed | 6 | 2 |

285 | MISYS | spdh | 6 | 2 |

285 | MISYS | travel | 6 | 3 |

285 | MISYS | traveltime | 6 | 3 |

285 | MISYS | fuelc | 6 | 3 |

285 | MISYS | batteryc | 6 | 3 |

285 | MISYS | vLostIn | 6 | 2 |

285 | MISYS | vLostOut | 6 | 2 |

285 | MISYS | qvmean | 6 | 3 |

285 | MISYS | qvmax | 6 | 3 |

285 | MISYS | vOut | 6 | 3 |

285 | MISYS | vIn | 6 | 2 |

285 | MISYS | vWait | 6 | 2 |

285 | MISYS | nstops | 6 | 3 |

285 | MISECT | density | 6 | 3 |

285 | MISECT | stime | 6 | 2 |

285 | MISECT | ttime | 6 | 2 |

285 | MISECT | flow | 6 | 3 |

285 | MISECT | qmean | 6 | 3 |

285 | MISECT | dtime | 6 | 2 |

285 | MISECT | speed | 6 | 2 |

285 | MISECT | spdh | 6 | 2 |

285 | MISECT | qmax | 6 | 3 |

285 | MISECT | qvmean | 6 | 3 |

285 | MISECT | nstops | 6 | 3 |

285 | MISECT | traveltime | 6 | 3 |

285 | MISECT | fuelc | 6 | 3 |

285 | MISECT | batteryc | 6 | 3 |

285 | MISECT | qvmax | 6 | 3 |

285 | MISECT | travel | 6 | 3 |

### Information Table Examples¶

Based on the previous tables we know that we have two information tables (MISYS and MISECT) with 6 intervals (the simulation duration is one hour, the gathering interval is 600 seconds) plus an aggregated one and three vehicle types (0 for All, 1 for car, 2 for van). The fields listed as aggtype 2 will have both the value and the standard deviation (speed and speed_D) and the fields as aggtype 3 will have only the value (density).

For the sections, we will list two objects (oid: 265 and 266), for the system just one, the replication (oid 285). For all the tables, the did value will be 285 as this is the ID of the replication that has generated all the tables. An example of the MISECT table showing just density and speed for the first 2 intervals and the aggregated one (ent) and three vehicle types (sid) for section 265 (oid) follows:

did | oid | eid | sid | ent | density | speed | speed_D |
---|---|---|---|---|---|---|---|

285 | 265 | 0 | 0 | 9.12276 | 54.0288 | 4.27335 | |

285 | 265 | 1 | 0 | 9.12276 | 54.0288 | 4.27335 | |

285 | 265 | 2 | 0 | 0 | -1 | -1 | |

285 | 265 | 0 | 1 | 9.37195 | 54.2844 | 3.83625 | |

285 | 265 | 1 | 1 | 9.37195 | 54.2844 | 3.83625 | |

285 | 265 | 2 | 1 | 0 | -1 | -1 | |

285 | 265 | 0 | 2 | 9.22609 | 53.9951 | 4.35469 | |

285 | 265 | 1 | 2 | 9.22609 | 53.9951 | 4.35469 | |

285 | 265 | 2 | 2 | 0 | -1 | -1 |

Note that, for brevity, this table contains fields not listed in the example (ttime, ttime_D, stime, stime_D...).

## Microscopic Database¶

The Tables defined in the Aimsun Next Microscopic Results Database are: MISYS, MISECT, MILANE, MITURN, MINODE, MIDETEC, MICENT_O, MICENT_D, MIPT, MISUBPATH, MISUPERNODETRAJECTORY, MICONTROLTURN, MICONTROLSIGNAL, MICONTROLMETERING, CONTROLPHASE, CONTROLPHASEEVENTS, MITRAFFICMANAGEMENT, MISYSPO, MISECTPO, MITURNPO, MICENTPO_O, MICENTPO_D, MISUBPATHPO, MISUPERNODETRAJECTORYPO, MIPTPO, DETEQUIPVEH, MIVEHTRAJECTORY, MIVEHSECTTRAJECTORY and MIVEHDETAILEDTRAJECTORY.

Additional tables are provided which contain Pedestrian trajectory information from the pedestrian simulator in the Pedestrian Trajectory tables.

#### MISYS Table¶

This table contains statistical information of the whole system for each period Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Network Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Replication or Average identifier |

eid | char | Replication or Average external ID |

sid | integer | Vehicle type (from 0 for all vehicles, to number of vehicle types. See the META_SUB_INFO table for more info about each vehicle type) |

ent | integer | Time interval, from 1 to N, where N is the number of time intervals, and 0 contains the aggregation of all the intervals |

density(_D) | double | Density (veh/km per lane) |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

ttime(_D) | double | Mean travel time (sec/km) |

dtime(_D) | double | Mean delay time (sec/km) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

travel(_D) | double | Total distance traveled of the vehicles that have exited the network(km) |

traveltime(_D) | double | Total travel time experienced of the vehicles that have exited the network (hours) |

totalDistanceTraveledInside | double | Total distance traveled by the vehicles inside the network (km) |

totalTravelTimeInside | double | Total travel time experienced by the vehicles inside the network (hours) |

totalWaitingTime | double | Total time experienced by the vehicles still waiting outside (hours) |

vWait(_D) | double | Number of vehicles waiting to enter the network |

vIn(_D) | double | Number of vehicles inside the network |

vOut(_D) | double | Number of vehicles that have exited the network |

vLostIn(_D) | double | Number of vehicles lost inside the network |

vLostOut(_D) | double | Number of vehicles lost that have exited the network |

qmean(_D) | double | Mean vehicles in queue (veh) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean Stop Time (sec/km) |

fuelc(_D) | double | Total liters of fuel consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

nstops(_D) | double | Number of stops per vehicle per kilometer per lane (#/veh/km) |

totalNStops(_D) | double | Total number of stops of all vehicles in the simulation period in the whole network |

missedTurnings(_D) | double | Total number of missed turns |

lane_changes(_D) | double | Total number of lane changes / km |

total_lane_changes(_D) | double | Total number of lane changes |

#### MISECT Table¶

It contains statistical information of the sections for each period.Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Turns and Roads Sections Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | Vehicle type (from 0 for all vehicles, to number of vehicle types. See the META_SUB_INFO table for more info about each vehicle type) |

ent | integer | Time interval, from 1 to N, where N is the number of time intervals, and 0 contains the aggregation of all the intervals |

flow(_D) | double | Mean flow (veh/h) |

count(_D) | double | Vehicle counts (veh) |

input_count(_D) | double | Number of vehicles in the section |

input_flow(_D) | double | Mean flow (veh/h) in the section |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

dtimeTtime(_D) | double | Delay Time (% of Travel Time) calculated as (delay time / travel time) *100 |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

flow_capacity(_D) | double | Mean flow / section capacity |

density(_D) | double | Density (veh/km per lane) |

qmean(_D) | double | Mean queue length by lane (veh) |

qmax(_D) | double | Maximum queue length (veh) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

qvnbvehs | double | Number of vehicles in virtual queue |

travel(_D) | double | Total number of km traveled in the section |

traveltime(_D) | double | Total travel time experienced in the section (seconds) |

lane_changes(_D) | double | Number of lane changes / Number of veh |

stime(_D) | double | Mean Stop Time (seconds) |

fuelc(_D) | double | Total liters of fuel consumed in the section (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed in the section (This is only provided when the particular model "Energy Consumption" is set to "ON") |

nstops(_D) | double | Number of stops per vehicle |

#### MILANE Table¶

It contains statistical information for the each lane of the sections for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Turns and Roads Sections Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | Vehicle type (from 0 for all vehicles, to number of vehicle types. See the META_SUB_INFO table for more info about each vehicle type) |

ent | integer | Time interval, from 1 to N, where N is the number of time intervals, and 0 contains the aggregation of all the intervals |

lane | double | Lane identifier (from 1 to number of lanes) |

count(_D) | double | Number of vehicles exited |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the lane |

input_flow(_D) | double | Mean flow (veh/h) in the lane |

density(_D) | double | Density (veh/km or veh/mile) |

qmean(_D) | double | Mean queue length (veh) |

qmax(_D) | double | Maximum queue length (veh) |

dtime(_D) | double | Delay time in the lane (seconds) |

wtimeVQ(_D) | double | Waiting time in virtual queue before entering the lane including vehicles inside (seconds/veh) |

speed(_D) | double | Mean speed (km/h) |

hspeed(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

stime(_D) | double | Stop Time (seconds) in the lane |

#### MITURN Table¶

It contains statistical information of the turns and links for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Turns and Roads Sections Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external ID |

sid | integer | |

ent | integer | |

flow(_D) | double | Mean flow (veh/h) |

count(_D) | double | Vehicle counts(veh) |

input_count(_D) | double | Number of vehicles in the turn |

input_flow(_D) | double | Mean flow (veh/h) in the turn |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

qmean(_D) | double | Mean queue length (veh) |

qmax(_D) | double | Maximum queue length (veh) |

travel(_D) | double | Total number of km traveled in the turn |

traveltime(_D) | double | Total travel time experienced in the turn (seconds) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the turn |

batteryc(_D) | double | Total kWh of battery consumed in the turn |

lane_changes(_D) | double | Number of lane changes / Number of veh |

total_lane_changes(_D) | double | Total Number of lane changes |

lostVehicles(_D) | double | Number lost vehicles at the turn |

missedVehicles(_D) | double | Number of vehicles missing this turn |

link_flow(_D) | double | Mean flow (veh/h) |

link_count(_D) | double | Vehicle counts(veh) |

link_ttime(_D) | double | Mean travel time (seconds) |

link_dtime(_D) | double | Mean delay time (seconds) |

link_wtimeVQ(_D) | double | Mean Time in virtual queue including vehicles inside (seconds) |

link_speed(_D) | double | Mean speed (km/h) |

link_spdh(_D) | double | Harmonic mean speed (km/h) |

link_qmean(_D) | double | Mean queue length (veh) |

link_qmax(_D) | double | Maximum queue length (veh) |

link_travel(_D) | double | Total number of km traveled in the link |

link_traveltime(_D) | double | Total travel time experienced in the link (seconds) |

link_stime(_D) | double | Mean Stop Time (seconds) |

link_nstops(_D) | double | Number of stops per vehicle |

link_fuelc(_D) | double | Total liters of fuel consumed in the link |

link_batteryc(_D) | double | Total kWh of battery consumed in the link |

link_lane_changes(_D) | double | Number of lane changes / Number of veh |

Total_link_lane_changes(_D) | double | Total number of lane changes |

effective_green(_D) | double | Amount of time that the turn had green traffic light (seconds) |

green_percentage(_D) | double | Percentage of time that the turn had green traffic light(effective_green /interval time*100) |

effective_red(_D) | double | Amount of time that the turn had red traffic light (seconds) |

red_percentage(_D) | double | Percentage of time that the turn had red light (effective_red interval*100) |

#### MINODE Table¶

It contains statistical information of the nodes for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Node Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external ID |

sid | integer | |

ent | integer | |

approachDelay(_D) | double | Mean approach delay (seconds) |

lostVehicles(_D) | double | Number of lost vehicles in the node |

missedTurnings(_D) | double | Number of vehicles that have missed a turn in the node |

#### MISUPERNODETRAJECTORY Table¶

It contains statistical information of the supernode trajectories for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Turns Section as for turns in a simple node.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Supernode trajectory identifier |

eid | char | Supernode trajectory external ID |

sid | integer | |

ent | integer | |

count(_D) | double | Vehicles completing the supernode trajectory |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle (seconds) |

travel(_D) | double | Total number of km traveled in the supernode trajectory |

traveltime(_D) | double | Total travel time experienced in the supernode trajectory (seconds) |

stime(_D) | double | Mean stop time(seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the supernode trajectory |

batteryc(_D) | double | Total kWh of battery consumed in the supernode trajectory |

#### MICENT_O Table¶

Contains statistical information of the origin centroids for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: OD and Centroids section. When the Outputs to Generate in the Scenario include OD Pair Statistics, the *destination* field in this table being the ID of a centroid (instead of zero) will mean the outputs in that row of the table are the ones for the corresponding OD pair.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | |

destination | integer | Destination Centroid ID |

nbveh(_D) | double | Number of vehicles that have arrived at their destination |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

speed(_D) | double | Mean speed (km/h ) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel(_D) | double | Total number of km traveled |

traveltime(_D) | double | Total travel time experienced (seconds) |

vlost(_D) | double | Number of vehicles lost |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

#### MICENT_D Table¶

Contains statistical information of the destination centroids for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: OD and Centroids section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | |

nbveh(_D) | double | Number of vehicles that have arrived at their destination |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

speed(_D) | double | Mean speed (km/h ) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel(_D) | double | Total number of km traveled |

traveltime(_D) | double | Total travel time experienced (seconds) |

vlost(_D) | double | Number of vehicles lost |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of energy consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

### Microsimulation Transit Tables¶

This contains statistical information of transit lines for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Transit Section .

#### MIPT Table¶

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Transit line identifier |

eid | char | Transit line external ID |

sid | integer | |

ent | integer | |

count(_D) | integer | Vehicle counts that have arrived at the end of the transit line |

flow(_D) | integer | Mean flow (veh/h) |

input_count(_D) | integer | Number of vehicles following the transit Line |

input_flow(_D) | integer | Mean flow (veh/h) following the transit Line |

speed(_D) | double | Mean speed (km/h or mph) |

spdh(_D) | double | Harmonic mean speed (km/h or mph) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

dwelltime(_D) | double | Dwell time |

travel(_D) | double | Total number of km or miles traveled |

traveltime(_D) | double | Total travel time experienced (seconds) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | |

batteryc(_D) | double | Total kWh of battery consumed (This is only provided when the particular model "Energy Consumption" is set to "ON") |

#### MIPTSTOPTIMES¶

This table contains the time spent for each Transit Vehicle at each Transit Stop

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Transit Stop ID |

idveh | integer | Vehicle ID |

starttime | double | Time Sta when the vehicle arrives at the stop in seconds |

endtime | double | Time Sta when the vehicle leaves the stop in seconds (0 value means the vehicle is still at the transit stop) |

idline | integer | Transit Line ID |

#### PTBETWEENSTOPS Table¶

This table contains statistics about two consecutive transit line stops that belong to the same line.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Object identifier |

sid | integer | |

ent | integer | |

ptline | integer | Transit line identifier |

from_stop | integer | Origin transit stop identifier |

to_stop | integer | Destination transit stop identifier |

count | integer | Number of vehicles that have traveled between both stops |

count_D | integer | Count with standard deviation |

ttime | integer | The travel time in seconds between the stops including the dwell time of to stop |

ttime_D | integer | Travel time with standard deviation |

### Microsimulation Detector Tables¶

These tables contain the detection measures for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Detector Data Section .

#### MIDETEC Table¶

This table contains the date collected by a detector in a road section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average Identifier |

oid | integer | Detector identifier |

eid | char | Detector external ID |

sid | integer | |

ent | integer | |

countveh(_D) | integer | Number of vehicles |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Average Speed (km/h ) |

occupancy(_D) | double | Percentage of occupancy |

density(_D) | double | Density (veh/km) |

headway(_D) | double | Average Headway between vehicles (sec) |

#### DETEQUIPVEH Table¶

This contains information about equipped vehicles that cross any detector in the network having the Equipped Vehicles detection capability activated.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Detector Identifier |

timedet | double | Detection time of the Equipped Vehicle |

idveh | integer | Vehicle identifier |

vehtype | integer | |

idptline | integer | Transit Line Identifier, for transit vehicles, -1 otherwise |

speed | double | Detected vehicle speed (km/h) |

headway | double | Headway of this equipped vehicle (seconds) |

### Signal Control Tables¶

The signal control tables present the aggregated signal timings and the individual signal changes for signals in the traffic network. This is only provided when the option to gather statistics from control plans is set to 'ON' in the Scenario Editor.

#### MICONTROLTURN Table¶

This table contains information of the amount of time that a turn remains on each possible traffic light state. T

State value description: 0 (red), 1 (green), 2 (yellow), 3 (flashing green), 4 (flashing red), 5 (flashing yellow), 6 (off), 7(flashing yellow behaving as green), 8 (yellow behaving as green) and 9 (flashing red behaving as green).

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external id |

sid | integer | |

ent | integer | |

state | integer | State index, from 0 to 9 |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds std deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time std deviation |

#### MICONTROLSIGNAL Table¶

This table contains information related to node signal groups. It details the amount of time that each signal group keeps each state. State value description: 0 (red), 1 (green), 2 (yellow), 3 (flashing green), 4 (flashing red), 5 (flashing yellow), 6 (off), 7(flashing yellow behaving as green), 8 (yellow behaving as green), and 9 (flashing red behaving as green).

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external id |

sid | integer | |

ent | integer | |

sg | integer | Signal Group id |

state | integer | State index, from 0 to 9 |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds std deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time std deviation |

#### MICONTROLMETERING Table¶

This table contains information related to control meterings. It details the amount of time that the metering keeps each state.

State value description: 0 (red), 1 (green), 2 (yellow), 3 (flashing green), 4 (flashing red), 5 (flashing yellow), 6 (off), 7(flashing yellow behaving as green), 8 (yellow behaving as green), and 9 (flashing red behaving as green).

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Node identifier |

eid | char | Node external id |

sid | integer | |

ent | integer | |

lane | integer | Metering lane |

state | integer | State index, from 0 to 9 |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds std deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time std deviation |

#### CONTROLPHASE Table¶

This table contains information related to the phases. It details how long each phase remains active during the simulation. It is the same table for micro, meso, and hybrid simulations.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Control Plan identifier |

node_id | integer | Node identifier |

phase | integer | Phase index, from 1 to N, where N is the number of phases |

ent | integer | |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds std deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time std deviation |

#### CONTROLPHASEEVENTS Table¶

This table contains information about the reason why a phase has been activated/deactivated. Note that, a phase cannot be activated and deactivated at the same time. It is the same table for micro, meso, and hybrid simulations.

Activation:

- None: The phase has been deactivated
- Recall: The phase has been activated due to a recall event.
- Preemption: The phase has been activated due to a transit priority management.
- Detection: The phase has been activated due to the detectors associated with the phase have raised a call.
- Default: The phase has been activated due to time reason. Usually in fixed control plans.
- External: The phase has been activated due to an external control.

Deactivation:

- None: The phase has been activated
- Fixed Time: The phase has reached its active time period.
- GapOut: The phase has reached its allowed gap time.
- MaxOut: The phase has reached its maximum green time since the first conflicting call.
- ForceOff: The phase has reached a certain point in the cycle where it needs to be closed.
- MaxDwell: The phase has reached its maximum priority dwell time.
- PriorityEnd: Detectors linked to the phase have measured a priority vehicle.
- PreemptionMinDuration: The phase must be closed because a priority end is calling, but it must be active until it reaches its min duration.
- HoldBarrier: When control plans with two rings, the phase must be closed in order to close the barrier at the same time for the two rings.
- External: The phase has been deactivated due to an external control.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Node identifier |

node_id | integer | Node id |

phase | integer | Phase index, from 1 to N, where N is the total number of phases |

time_sta | double | Simulation time in seconds from midnight |

activation | string | Activation reason |

deactivation | string | Deactivation reason |

### Traffic Management ¶

#### MITRAFFICMANAGEMENT Table¶

This table contains information related to some of traffic management actions; in particular, for Turn Closures, Force Turns, and Destination Change actions.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Traffic management action identifier |

eid | integer | Traffic management action external identifier |

sid | integer | |

ent | integer | |

affected_vehicles | integer | Number of vehicles affected by the traffic management action during the time interval |

affected_vehicles_D | integer | Number of vehicles affected by the traffic management action during the time interval - std deviation |

### Pollution Tables¶

The PO family of tables are used by the Quartet Pollutant Model

#### MISYSPO Table¶

This contains pollution statistical information of the whole system for each period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Replication or Average identifier |

eid | char | Replication or Average external ID |

sid | integer | |

ent | integer | |

npollutant_K | double | Pollutant ID in the MIPOLLS table |

vpollutant | double | Value of pollutant (kg) |

vpollutant_D | double | Value of pollutant (kg) - std deviation |

#### MISECTPO Table¶

This contains pollution statistical information of the sections for each period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | |

ent | integer | |

npollutant_K | double | Pollutant ID in the MIPOLLS table |

vpollutant | double | Value of pollutant (kg) |

vpollutant_D | double | Value of pollutant (kg) - std deviation |

#### MITURNPO Table¶

This contains pollution statistical information of the turns for each period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external ID |

sid | integer | |

ent | integer | |

npollutant_K | double | Pollutant ID in the MIPOLLS table |

vpollutant | double | Value of pollutant (kg) |

vpollutant_D | double | Value of pollutant (kg) - std deviation |

#### MICENTPO_O and MISECTPO_D Tables¶

This contains pollution statistical information of the origin and destination centroids for each period.n

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | |

npollutant_K | double | Pollutant ID in the MIPOLLS table |

vpollutant | double | Value of pollutant (kg) |

vpollutant_D | double | Value of pollutant (kg) - std deviation |

#### MISUBPATHPO Table¶

This contains pollution statistical information of the subpaths for each period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Subpath identifier |

eid | char | Subpath external ID |

sid | integer | |

ent | integer | |

npollutant_K | double | Pollutant ID in the MIPOLLS table |

vpollutant | double | Value of pollutant (kg) |

vpollutant_D | double | Value of pollutant (kg) - std deviation |

#### MIPTPO Table¶

This contains pollution statistical information of the transit lines for each period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Transit line identifier |

eid | char | Transit line external ID |

sid | integer | |

ent | integer | |

npollutant_K | double | Pollutant ID in the MIPOLLS table |

vpollutant | double | Value of pollutant (kg) |

vpollutant_D | double | Value of pollutant (kg) - std deviation |

### Instantaneous Emission Model¶

The IEM family of tables contain emissions outputs for Nodes (MINODEIEM), OD pairs (MIODPAIIEM), Sections (MISECTIEM) Streams (MISTREAMIEM) Turns (MITURNIEM) and the system as a whole (MISYIEM). All tables have the same format and all output quantities have a mean and a standard deviation ( _D). They are used by the Panis Pollutant Model. The 4 major outputs are Carbon Dioxide, Nitorus Oxides, Volatile Organic Compounds, and Particulate Materials. Two values are provided for each emission, one giving the total output, the other (labeled as the "interurban" value) giving the value per km.

The table format is:

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication, or Average identifier |

oid | integer | Replication, Average, Section or Turn identifier depending on table |

eid | char | External ID |

sid | integer | |

ent | integer | |

CO2 | double | CO2 (g) |

CO2_D | double | |

NOx | double | Nitrous Oxides (g) |

NOx_D | double | |

VOC | double | Volatile Organic Compounds (g) |

VOC_D | double | |

PM | double | Particulates(g) |

PM_D | double | |

CO2_interurban | double | CO2 (g/km) |

CO2_D_interurban | double | |

NOx_interurban | double | Nitrous Oxides (g/km) |

NOx_D_interurban | double | |

VOC_interurban | double | Volatile Organic Compounds (g/km) |

VOC_D_interurban | double | |

PM_interurban | double | Particulates(g/km) |

PM_D_interurban | double |

### London Emission Model ¶

The LEM family of tables contains the emissions outputs for CO_{2} and NO_{x} for sections (g/km), links (g/h) and the replication (g).

The replication value (g) is the sum of the emissions (g/km) of every vehicle in each traveled section multiplied by section length (km).

This is because the model only considers the pollution emitted by a vehicle in some point with a certain speed, hence turns are not taken into consideration in the total sum of emissions.

The LEM model is documented in the Environmental Models: London Emission Model section.

#### SECTLEM¶

Contains LEM Emissions for sections.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | |

ent | integer | |

nox | double | NOx (g/km) |

nox_D | double | Not used |

co2 | double | CO2 (g/km) |

co2_D | double | Not used |

#### LINKLEM¶

Contains LEM Emissions for links.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Link identifier |

eid | char | Link external ID |

sid | integer | |

ent | integer | |

nox | double | NOx (g/h) |

nox_D | double | Not used |

co2 | double | CO2 (g/h) |

co2_D | double | Not used |

#### SYSLEM¶

Contains LEM Emissions for the whole network simulated in this replication.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | |

ent | integer | |

nox | double | NOx (g) |

nox_D | double | Not used |

co2 | double | CO2 (g) |

co2_D | double | Not used |

### Microsimulation Vehicle Trajectory Tables¶

A Microsimulation model can output vehicle details by trip, or by section and can also output detailed positions.

#### MIVEHTRAJECTORY Table¶

This contains vehicle trajectory information. The trajectories during the warm up period are not stored unless the exit time occurs after the warm up period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Vehicle ID |

sid | integer | Vehicle type ID |

origin | integer | Origin centroid ID for vehicles in the demand. First section ID for transit vehicles |

destination | integer | Destination centroid ID for vehicles in the demand. Last section ID for transit vehicles |

entranceSection | integer | First section ID. |

generationTime | double | Vehicle generation simulation time |

entranceTime | double | Vehicle entrance simulation time. In seconds, relative to the beginning of the warm up period. |

exitTime | double | Vehicle exit simulation time. In seconds, relative to the beginning of the warm up period. |

expectedTravelTime | double | Vehicle expected travel time in seconds coming from a previous run. Value read from the input APA file, zero in case of no input APA file. |

delayTime | double | Vehicle total delay time in seconds |

travelledDistance | double | Vehicle distance traveled in meters |

pathType | integer | Vehicle type of path (0:RC, 1:APA, 2:OD, 3:PT, 4:LOST, 5:P2P). |

#### MIVEHSECTTRAJECTORY Table¶

This contains vehicle trajectory information for each section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Vehicle ID |

ent | integer | Vehicle section index in vehicle's path |

sectionId | integer | Section ID |

exitTime | double | Vehicle exit section simulation time |

travelTime | double | Vehicle section travel time in seconds |

delayTime | double | Vehicle section delay time in seconds |

#### MIVEHDETAILEDTRAJECTORY¶

This contains vehicle positions throughout each section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Vehicle ID |

ent | integer | Record Sequence ID in vehicle trajectory |

sectionId | integer | Section ID |

laneIndex | integer | Vehicle section lane |

xCoord | double | Vehicle x coordinate |

yCoord | double | Vehicle y coordinate |

time | double | Time, in seconds, relative to the beginning of the warm up period |

speed | double | Vehicle speed in km/h |

travelledDistance | double | Vehicle distance traveled |

acceleration | double | Vehicle acceleration |

### Microscopic Pedestrian Database¶

The Tables defined in the Microscopic Pedestrian Database are: PEDESTRIANTRAJECTORY and PEDESTRIANDETAILEDTRAJECTORY.

#### PEDESTRIANTRAJECTORY¶

This table provides general information about the pedestrian trips in the simulation.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Pedestrian ID |

sid | integer | Pedestrian type |

origin | integer | Origin centroid ID |

destination | integer | Destination centroid ID |

entranceTime | double | Simulation time when the pedestrian enters |

exitTime | double | Simulation time when the pedestrian exits the network. This is -1 if the pedestrian is still in the model when the simulation finishes. |

#### PEDESTRIANDETAILEDTRAJECTORY¶

This table provides information about the pedestrian for each simulation step.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Pedestrian ID |

ent | integer | Statistics interval |

xCoord | double | Pedestrian global x coordinate |

yCoord | double | Pedestrian global y coordinate |

time | double | Time, in seconds, relative to the beginning of the warm up period |

speed | double | Pedestrian speed in km/h |

travelledDistance | double | Distance traveled by the pedestrian in meters. |

pedestrianCrossing | integer | Pedestrian crossing id. Set to 0 when the pedestrian is not over any pedestrian crossing. |

#### MISYSPEDESTRIAN¶

This table contains statistical information of the whole system for each period Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Network Section

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Pedestrian ID |

eid | integer | Statistics interval |

sid | double | Pedestrian global x coordinate |

ent | double | Pedestrian global y coordinate |

pedestrianFlow_D | double | Mean flow (ped/h) |

pedestrianIn_D | double | Number of pedestrian entered the network |

pedestrianOut_D | double | Number of pedestrian exited the network |

travelTime_D | double | Total travel time experienced of the pedestrians that have exited the network (hours) |

walkingTime_D | double | Total travel time experienced of the pedestrians that have exited the network walking (hours) |

totalDistance_D | double | Total distance traveled by the pedestrians inside the network (km) |

totalTravelTime_D | double | Total travel time experienced by the pedestrians inside the network (hours) |

speed_D | double | Mean speed (km/h) |

hspeed_D | double | Harmonic mean speed (km/h) |

meanStopTime_D | double | mean stop time (h) |

## Mesoscopic Database¶

The Tables defined in the Aimsun Next Mesoscopic Database are: MESYS, MESECT, MELANE, METURN, MENODE, MESUBPATH, MESUPERNODETRAJECTORY, MECENT_O, MECENT_D, MEPT, MEVEHTRAJECTORY,MEVEHSECTTRAJECTORY, MEDETECT, MECONTROLTURN, MECONTROLSIGNAL, MECONTROLMETERING, CONTROLPHASE, CONTROLPHASEEVENTS, and METRAFFICMANAGEMENT.

Tables (MESYS, METURN, and MESECT) have the following format

Attribute Name | Type | Size | Description |
---|---|---|---|

did | integer | Replication identifier (Origin Data ID) | |

oid | integer | Object ID | |

eid | char | 2 | External ID (optional) |

sid | integer | Subobject Pos (optional) to store data of an object disaggregated by a criteria (vehicle type in sections for example) | |

ent | integer | Entry number | |

attr | double | Value for one of the collected attributes | |

attr_d | double | Deviation for one of the collected attributes |

#### MESYS Table¶

This contains statistical information of the whole system for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. How the system statistics are calculated is documented in the Calculation of Traffic Statistics: Network Statistics Section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Object ID |

eid | char | External ID (optional) |

sid | integer | Subobject Pos (optional) to store data of an object disaggregated by a criteria (vehicle type in sections for example). |

ent | integer | Entry number |

density(_D) | double | Density (veh/km per lane) |

flow(_D) | integer | Mean flow (veh/h) |

flow_meso(_D) | integer | Mean meso flow (veh/h) |

flow_macro(_D) | integer | Mean macro flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

ttime(_D) | double | Mean travel time (sec/km) |

dtime(_D) | double | Mean delay time (sec/km) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed(km/h) |

travel(_D) | double | Total distance traveled (km) |

traveltime(_D) | double | Total travel time experienced (hours) |

totalDistanceTraveledInside | double | Total distance traveled by the vehicles inside the network (km) |

totalTravelTimeInside | double | Total travel time experienced by the vehicles inside the network (hours) |

totalWaitingTime | double | Total time experienced by the vehicles still waiting outside(hours) |

vWait(_D) | integer | Number vehicles waiting to enter into the system (vehs) |

vIn(_D) | integer | Number vehicles in system (vehs) |

vOut(_D) | integer | Number vehicles out system (vehs) |

vLostIn(_D) | integer | Number vehicles lost in system (vehs) |

vLostOut(_D) | integer | Number vehicles lost out system (vehs) |

qmean(_D) | integer | Mean vehicles in queue (vehs) |

qvmean(_D) | integer | Mean virtual queue length (vehs) |

qvmax(_D) | integer | Maximum virtual queue length (vehs) |

missedTurnings(_D) | integer | Number of missed turns |

lane_changes(_D) | integer | Number of lane changes/km |

total_lane_changes(_D) | integer | Number of lane changes |

##### MESECT Table¶

This contains statistical information of the sections for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. How the section statistics are aggregated from the vehicles exiting is documented in the Calculation of Traffic Statistics: Node Statistics Section but note that in a mesoscopic model, the speeds are derived from mesoscopic simulation algorithms.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Object ID |

eid | char | External ID (optional) |

sid | integer | Subobject Pos (optional) to store data of an object disaggregated by a criteria (vehicle type in sections for example) |

ent | integer | Entry number, from 1 to N, where N is the number of intervals, and 0 contains the aggregation of all the intervals. |

flow(_D) | double | Mean flow (veh/h) |

count(_D) | double | Number of vehicles |

ttime(_D) | double | Mean travel time (sec) |

dtime(_D) | double | Mean delay time (sec) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed(km/h) |

flow_capacity(_D) | double | Flow/Section Capacity (%) |

density(_D) | double | Density (veh/km per lane) |

qmean(_D) | double | Mean Queue Length by lane (veh) |

qvmax(_D) | integer | Maximum number of vehicles in this section virtual queue (veh) |

qvmean(_D) | double | Mean virtual queue length(veh) |

travel(_D) | double | Total distance traveled (km) |

traveltime(_D) | double | Total travel time experienced in the section (seconds) |

lane_changes(_D) | double | Number of lane changes / Number of veh |

total_lane_changes(_D) | double | Number of lane changes |

#### MELANE Table¶

This contains statistical information for the each lane of the sections for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | |

ent | integer | |

lane | double | Lane identifier (from 1 to number of lanes) |

flow(_D) | double | Mean flow (veh/h) |

density(_D) | double | Density (veh/km or veh/mile) |

qmean(_D) | double | Mean queue length (veh) |

qmax(_D) | double | Maximum queue length (veh) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

#### METURN Table¶

This contains statistical information of the turns and links for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. How the turn and node statistics are aggregated from the vehicles exiting is documented in the Calculation of Traffic Statistics: Node Statistics Section but note that in a mesoscopic model, the turn speeds and gap acceptance are derived from mesoscopic simulation algorithms.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external ID |

sid | integer | |

ent | integer | |

flow(_D) | double | Mean flow (veh/h) |

count(_D) | double | Vehicle counts(veh) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

qmean(_D) | double | Mean queue length (veh) |

qmax(_D) | double | Maximum queue length (veh) |

travel(_D) | double | Total number of km traveled in the turn |

traveltime(_D) | double | Total travel time experienced in the turn (seconds) |

link_flow(_D) | double | Mean flow (veh/h) |

link_count(_D) | double | Vehicle counts(veh) |

link_ttime(_D) | double | Mean travel time (seconds) |

link_dtime(_D) | double | Mean delay time (seconds) |

link_speed(_D) | double | Mean speed (km/h) |

link_spdh(_D) | double | Harmonic mean speed (km/h) |

link_qmean(_D) | double | Mean queue length (veh) |

link_qmax(_D) | double | Maximum queue length (veh) |

link_travel(_D) | double | Total number of km traveled in the link |

link_traveltime(_D) | double | Total travel time experienced in the link (seconds) |

#### MENODE Table¶

This contains statistical information of the nodes for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external ID |

sid | integer | |

ent | integer | |

approachDelay(_D) | double | Mean delay to calculate level of service (seconds) |

lostVehicles(_D) | integer | Number of lost vehicles in the node |

missedTurnings(_D) | integer | Number of vehicles that have missed a turn in the node |

#### MESUPERNODETRAJECTORY Table¶

It contains statistical information of the supernode trajectories for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Supernode trajectory identifier |

eid | char | Supernode trajectory external ID |

sid | integer | |

ent | integer | |

count(_D) | double | Vehicles completing the supernode trajectory |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle (seconds) |

travel(_D) | double | Total number of km traveled in the supernode trajectory |

traveltime(_D) | double | Total travel time experienced in the supernode trajectory (seconds) |

#### MECENT_O Table¶

This contains statistical information of the origin centroids for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: OD and Centroids section. When the Outputs to Generate in the Scenario include OD Pair Statistics, the *destination* field in this table being the ID of a centroid (instead of zero) will mean the outputs in that row of the table are the ones for the corresponding OD pair.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | |

destination | integer | Destination Centroid ID |

nbveh_(D) | double | Number of vehicles that have arrived at their destination |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

speed_(D) | double | Mean speed (km/h ) |

spdh_(D) | double | Harmonic mean speed (km/h) |

ttime_(D) | double | Mean travel time (seconds) |

dtime_(D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel_(D) | double | Total number of km traveled |

traveltime_(D) | double | Total travel time experienced (seconds) |

vlost_(D) | integer | Number of vehicles lost |

qvmean_(D) | double | Mean virtual queue (veh) |

qvmax_(D) | double | Maximum virtual queue (veh) |

#### MECENT_D Table¶

This contains statistical information of the destination centroids for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: OD and Centroids section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | |

nbveh_(D) | double | Number of vehicles that have arrived at their destination |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the networkinput_flow(_D) |

spdh_(D) | double | Harmonic mean speed (km/h) |

ttime_(D) | double | Mean travel time (seconds) |

dtime_(D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel_(D) | double | Total number of km traveled |

traveltime_(D) | double | Total travel time experienced (seconds) |

vlost_(D) | integer | Number of vehicles lost |

qvmean_(D) | double | Mean virtual queue (veh) |

qvmax_(D) | double | Maximum virtual queue (veh) |

### Mesoscopic Transit Tables¶

#### MEPT Table¶

This contains statistical information of transit lines for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. How the transit statistics are calculated is documented in the Calculation of Traffic Statistics: Road and Turn Statistics Section but note that, in a mesoscopic simulation, the delay due to dwell time at stops, and the stop time, are incurred at the end of the road section and so might not be in the same timer interval as in the corresponding microsimulation model.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Transit line identifier |

eid | char | Transit line external ID |

sid | integer | |

ent | integer | |

count(_D) | double | Number of vehicles that have arrived at the end of the transit line |

flow(_D) | double | Flow of vehicles arriving at the end of the line |

input_count(_D | double | Number of vehicles starting on the transit line |

input_flow(_D) | double | Flow of vehicles starting on the transit line |

speed(_D) | double | Mean speed (km/h or mph) |

spdh(_D) | double | Harmonic mean speed (km/h or mph) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

dwelltime(_D) | double | Dwell time at stops |

travel(_D) | double | Total number of km or miles traveled |

traveltime(_D) | double | Total travel time experienced (seconds) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

### Mesoscopic Detector Tables ¶

These tables contain the detection measures for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Detector Data Section . Note that in a mesoscopic model, detector data is gathered at the time a vehicle leaves a section and hence results will differ from the corresponding microsimulation run for detectors sited in the middle of a road section rather than at the end, at the stop-line.

#### MEDETEC Table¶

This contains the detection measures for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Detector identifier |

eid | char | Detector external ID |

sid | integer | |

ent | integer | |

countveh(_D) | integer | Number of vehicles |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Average Speed (km/h) |

occupancy(_D) | double | Flow/Jam density *100 |

density(_D) | double | Density (veh/km) |

### Mesoscopic Signals tables ¶

#### MECONTROLTURN Table¶

This table contains information of the amount of time that a turn remains on each possible traffic light state. This is only provided when control statistics are set to 'ON'.

State value description: 0 (red), 1 (green), 2 (yellow), 3 (flashing green), 4 (flashing red), 5 (flashing yellow), 6 (off), 7(flashing yellow behaving as green), 8 (yellow behaving as green), and 9 (flashing red behaving as green).

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external id |

sid | integer | |

ent | integer | |

state | integer | State index, from 0 to 9 |

active_time | double | active time in seconds |

active_time_D | double | active time in seconds: Std deviation |

active_time_percentage | double | percentage of active time |

active_time_percentage_D | double | percentage of active time: Std deviation |

#### MECONTROLSIGNAL Table¶

This table contains information related to node signal groups. It details the amount of time that each signal group keeps each state. State value description: 0 (red), 1 (green), 2 (yellow), 3 (flashing green), 4 (flashing red), 5 (flashing yellow), 6 (off), 7(flashing yellow behaving as green), 8 (yellow behaving as green), and 9 (flashing red behaving as green).

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external id |

sid | integer | |

ent | integer | |

state | integer | State index, from 0 to 9 |

active_time | double | active time in seconds |

active_time_D | double | active time in seconds: Std deviation |

active_time_percentage | double | percentage of active time |

active_time_percentage_D | double | percentage of active time: Std deviation |

#### MECONTROLMETERING Table¶

This table contains information related to control meterings. It details the amount of time that the metering keeps each state.

State value description: 0 (red), 1 (green), 2 (yellow), 3 (flashing green), 4 (flashing red), 5 (flashing yellow), 6 (off), 7(flashing yellow behaving as green), 8 (yellow behaving as green), and 9 (flashing red behaving as green).

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external id |

sid | integer | |

ent | integer | |

lane | integer | Metering lane |

state | integer | State index, from 0 to 9 |

active_time | double | active time in seconds |

active_time_D | double | active time in seconds: Std deviation |

active_time_percentage | double | percentage of active time |

active_time_percentage_D | double | percentage of active time: Std deviation |

### Mesoscopic Traffic Management¶

#### METRAFFICMANAGEMENT Table¶

This table contains information related to some of traffic management actions; in particular, for Turn Closures, Force Turns, and Destination Change actions.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Traffic management action identifier |

eid | integer | Traffic management action external identifier |

sid | integer | |

ent | integer | |

affected_vehicles | integer | Number of vehicles affected by the traffic management action during the time interval |

affected_vehicles_D | integer | Number of vehicles affected by the traffic management action during the time interval |

### Mesoscopic Vehicle Trajectories ¶

#### MEVEHTRAJECTORY Table¶

These tables contain vehicle trajectory information for the simulation and for each road section. The trajectories during the warm up are not stored unless the exit time occurs after the warm up period.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Vehicle ID |

sid | integer | Vehicle type ID |

origin | integer | Origin centroid ID for vehicles in the demand. First section ID for Transit vehicles |

destination | integer | Destination centroid ID for vehicles in the demand. Last section ID for transit vehicles |

generationTime | double | Vehicle generation simulation time |

entranceTime | double | Vehicle entrance simulation time. In seconds, relative to the beginning of the warm up period. |

exitTime | double | Vehicle exit simulation time. In seconds, relative to the beginning of the warm up period. |

expectedTravelTime | double | Vehicle expected travel time in seconds coming from a previous run. Value read from the input APA file, zero in case of no input APA file. |

delayTime | double | Vehicle total delay time in seconds |

travelledDistance | double | Vehicle distance traveled in meters |

pathType | integer | Vehicle type of path (0:RC, 1:APA, 2:OD, 3:PT, 4:LOST, 5:P2P). |

speed | double | Vehicle speed in m/s |

#### MEVEHSECTTRAJECTORY Table¶

This contains vehicle trajectory information for each section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Vehicle ID |

ent | integer | Vehicle section index in vehicle's path |

sectionId | integer | Section ID |

exitTime | double | Vehicle exit section simulation time |

travelTime | double | Vehicle section travel time in seconds |

delayTime | double | Vehicle section delay time in seconds |

## Hybrid Database¶

The Tables defined in the Aimsun Next Microscopic Results Database are: HYSYS, HYSECT, HYLANE, HYTURN, HYNODE, HYDETEC, HYCENT_O, HYCENT_D, HYPT, HYSUBPATH, HYSUPERNODETRAJECTORY, HYCONTROLTURN, HYCONTROLSIGNAL, HYCONTROLMETERING, CONTROLPHASE, CONTROLPHASEEVENTS, HYTRAFFICMANAGEMENT, and HYPTSTOPTIMES.

#### HYSYS Table¶

This contains statistical information about the whole system for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D", being the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Network Section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Replication or Average identifier |

eid | char | Replication or Average external ID |

sid | integer | |

ent | integer | |

density(_D) | double | Density (veh/km per lane) |

flow(_D) | double | Mean flow (veh/h) |

input_flow(_D) | double | Flow rate of vehicles entering the simulation |

input_count(_D) | double | Number of vehicles entering the simulation |

ttime(_D) | double | Mean travel time (sec/km) |

dtime(_D) | double | Mean delay time (sec/km) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

travel(_D) | double | Total distance traveled(km) |

traveltime(_D) | double | Total travel time experienced (hours) |

totalDistanceTraveledInside | double | Total distance traveledby the vehicles inside the network (km) |

totalTravelTimeInside | double | Total travel time experienced by the vehicles inside the network (hours) |

totalWaitingTime | double | Total time experienced by the vehicles still waiting outside (hours) |

vWait(_D) | double | Number of vehicles waiting to enter the network |

vIn(_D) | double | Number of vehicles inside the network |

vOut(_D) | double | Number of vehicles that have exited the network |

vLostIn(_D) | double | Number of vehicles lost inside the network |

vLostOut(_D) | double | Number of vehicles lost that have exited the network |

qmean(_D) | double | Mean vehicles in queue (veh) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean Stop Time in the micro areas (sec/km) |

fuelc(_D) | double | Total liters of fuel consumed in the micro areas (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed in the micro areas (This is only provided when the particular model "Energy Consumption" is set to "ON") |

nstops(_D) | double | Number of stops per vehicle in the micro areas (#/veh/km) |

totalNStops(_D) | double | Total number of stops in the micro areas |

missedTurnings(_D) | double | Total number of missed turns in the micro areas |

lane_changes(_D) | double | Total number of lane changes / km |

total_lane_changes(_D) | double | Total number of lane changes |

nstops(_D) | double | Number of stops per vehicle |

#### HYSECT Table¶

This contains statistical information of the sections for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations:Turn and Road Sections Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | |

ent | integer | |

flow(_D) | double | Mean flow (veh/h) |

count(_D) | integer | Vehicle counts (veh) |

input_flow(_D) | double | Flow rate of vehicles entering the section |

input_count(_D) | double | Number of vehicles entering the section |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

dtimeTtime(_D) | double | Delay Time (% of Travel Time) calculated as (delay time / travel time) *100 |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

flow_capacity(_D) | double | Mean flow / section capacity |

density(_D) | double | Density (veh/km per lane) |

qvnbvehs | double | Virtual queue number of vehicles |

qmean(_D) | double | Mean queue length by lane (veh) |

qmax(_D) | double | Maximum queue length (veh) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

travel(_D) | double | Total number of km traveled in the section |

traveltime(_D) | double | Total travel time experienced in the section (seconds) |

lane_changes(_D) | double | Number of lane changes / Number of veh |

total_lane_changes(_D) | double | Total number of lane changes |

stime(_D) | double | Mean Stop Time (seconds) |

fuelc(_D) | double | Total liters of fuel consumed in the section (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed in the section (This is only provided when the particular model "Energy Consumption" is set to "ON") |

nstops(_D) | double | Number of stops per vehicle |

#### HYLANE Table¶

This contains statistical information for the each lane of the sections for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Section identifier |

eid | char | Section external ID |

sid | integer | |

ent | integer | |

lane | double | Lane identifier (from 1 to number of lanes) |

count(_D) | double | Count of vehicles leaving the lane |

flow(_D) | double | Mean flow leaving the lane (veh/h) |

input_count(_D) | double | Count of vehicles entering the lane |

input_flow(_D) | double | Mean flow entering the lane (veh/h |

density(_D) | double | Density (veh/km or veh/mile) |

qmean(_D) | double | Mean queue length (veh) |

qmax(_D) | double | Maximum queue length (veh) |

speed(_D) | double | Mean speed (km/h) |

hspeed(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

stime(_D) | double | Mean Stop Time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

#### HYTURN Table¶

This contains statistical information of the turns and links for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external ID |

sid | integer | |

ent | integer | |

flow(_D) | double | Mean flow (veh/h) |

count(_D) | double | Vehicle counts(veh) |

input_count(_D) | double | Count of vehicles entering the turn |

input_flow(_D) | double | Mean flow entering the turn (veh/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

qmean(_D) | double | Mean queue length (veh) |

qmax(_D) | double | Maximum queue length (veh) |

travel(_D) | double | Total number of km traveled in the turn |

traveltime(_D) | double | Total travel time experienced in the turn (seconds) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the turn (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total liters of kWh consumed in the turn (This is only provided when the particular model "Energy Consumption" is set to "ON") |

lane_changes(_D) | double | Number of lane changes / Number of veh |

total_lane_changes(_D) | double | Total number of lane changes |

link_flow(_D) | double | Mean flow (veh/h) |

link_count(_D) | double | Vehicle counts(veh) |

link_ttime(_D) | double | Mean travel time (seconds) |

link_dtime(_D) | double | Mean delay time (seconds) |

link_stime(_D) | double | Mean stopped time (seconds) |

link_nstops(_D) | double | Number of stops |

link_wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

link_speed(_D) | double | Mean speed (km/h) |

link_spdh(_D) | double | Harmonic mean speed (km/h) |

link_qmean(_D) | double | Mean queue length (veh) |

link_qmax(_D) | double | Maximum queue length (veh) |

link_travel(_D) | double | Total number of km traveled in the link |

link_traveltime(_D) | double | Total travel time experienced in the link (seconds) |

link_lane_changes(_D) | double | Number of lane changes on approaching link / Number of veh |

link_total_lane_changes(_D) | double | Total number of lane changes |

link_fuelc(\D) | double | Total liters of fuel consumed in the link |

link_batteryc(\D) | double | Total kWh of battery consumed in the link |

lostvehicles(_D) | double | Number of lost vehicles at this turn |

missedVehicles(_D) | double | Number of vehicles missing this turn |

effective_green(_D) | double | Amount of time that the turn had green traffic light (seconds) |

green_percentage(_D) | double | Percentage of time that the turn had green traffic light(effective_green /interval time*100)effective_red(_D) |

red_percentage(_D) | double | Percentage of time that the turn had red light (effective_red interval*100) |

#### HYNODE Table¶

This contains statistical information of the nodes for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: Nodes Section .

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external ID |

sid | integer | |

ent | integer | |

approachDelay(_D) | double | Mean delay to calculate level of service (seconds) |

lostVehicles(_D) | double | Number of lost vehicles in the node |

missedTurnings(_D) | double | Number of vehicles that have missed a turn in the node |

#### HYSUPERNODETRAJECTORY Table¶

It contains statistical information of the supernode trajectories for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Supernode trajectory identifier |

eid | char | Supernode trajectory external ID |

sid | integer | |

ent | integer | |

count(_D) | double | Vehicles completing the supernode trajectory |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle (seconds) |

travel(_D) | double | Total number of km traveled in the supernode trajectory |

traveltime(_D) | double | Total travel time experienced in the supernode trajectory (seconds) |

stime(_D) | double | Mean stop time(seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the supernode trajectory |

batteryc(_D) | double | Total kWh of battery consumed in the supernode trajectory |

#### HYCENT_O Table¶

This contains statistical information of the origin centroids (HYCENT_O) for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: OD and Centroids section. When the Outputs to Generate in the Scenario include OD Pair Statistics, the *destination* field in this table being the ID of a centroid (instead of zero) will mean the outputs in that row of the table are the ones for the corresponding OD pair.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | Time interval, from to N, where N is the number of time intervals, and 0 contains the aggregation of all the intervals |

destination | double | Destination Centroid ID |

nbveh(_D) | double | Number of vehicles that have arrived at their destination |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

speed(_D) | double | Mean speed (km/h ) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel(_D) | double | Total number of km traveled |

traveltime(_D) | double | Total travel time experienced (seconds) |

vlost(_D) | double | Number of vehicles lost |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the turn (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed in the turn (This is only provided when the particular model "Energy Consumption" is set to "ON") |

#### HYCENT_D Table¶

This contains statistical information of the destination centroids (HYCENT_D) for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively. The derivation of the measurements is described in the Statistics Calculations: OD and Centroids Section.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Centroid identifier |

eid | char | Centroid external ID |

sid | integer | |

ent | integer | Time interval, from to N, where N is the number of time intervals, and 0 contains the aggregation of all the intervals |

nbveh(_D) | double | Number of vehicles that have arrived at their destination |

flow(_D) | double | Mean flow (veh/h) |

input_count(_D) | double | Number of vehicles in the network |

input_flow(_D) | double | Mean flow (veh/h) in the network |

speed(_D) | double | Mean speed (km/h ) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel(_D) | double | Total number of km traveled |

traveltime(_D) | double | Total travel time experienced (seconds) |

vlost(_D) | double | Number of vehicles lost |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the turn (This is only provided when the particular model "Energy Consumption" is set to "ON") |

batteryc(_D) | double | Total kWh of battery consumed in the turn (This is only provided when the particular model "Energy Consumption" is set to "ON") |

### Hybrid Transit Tables¶

#### HYPT Table¶

This contains statistical information of transit lines for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Transit line identifier |

eid | char | Transit line external ID |

sid | integer | |

ent | integer | |

count(_D) | double | Number of vehicles that have arrived at the end of the transit line |

flow(_D) | double | Flow of vehicles that have arrived at the end of the transit line ( veh/hr) |

input_count(_D) | double | Count of vehicles starting on this transit line |

input_flow(_D) | double | Flow of vehicles starting on this transit line (veh/h) |

speed(_D) | double | Mean speed (km/h or mph) |

spdh(_D) | double | Harmonic mean speed (km/h or mph) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

dwelltime(_D) | double | Dwell time at stops |

travel(_D) | double | Total number of km or miles travele |

traveltime(_D) | double | Total travel time experienced (seconds) |

qvmean(_D) | double | Mean virtual queue (veh) |

qvmax(_D) | double | Maximum virtual queue (veh) |

### Hybrid Detector Tables¶

#### HYDETEC Table¶

This contains the detection measures for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Detector identifier |

eid | char | Detector external ID |

sid | integer | |

ent | integer | |

countveh(_D) | double | Number of vehicles |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Average Speed (km/h ) |

occupancy(_D) | double | Percentage of occupancy |

density(_D) | double | Density (veh/km) |

headway(_D) | double | Average Headway between vehicles (sec) |

### Hybrid Signals Tables¶

#### HYCONTROLTURN Table¶

This table contains information of the amount of time that a turn remains on each possible traffic light state. This is only provided when control statistics are set to 'ON'.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Turn identifier |

eid | char | Turn external id |

sid | integer | |

ent | integer | |

state | integer | State index, from 0 to 9 |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds: Std Deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time: Std Deviation |

#### HYCONTROLSIGNAL Table¶

This table contains information related to node signal groups. It details the amount of time that each signal group keeps each state.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external id |

sid | integer | |

ent | integer | |

state | integer | State index, from 0 to 9 |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds: Std Deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time: Std Deviation |

#### HYCONTROLMETERING Table¶

This table contains information related to control meterings. It details the amount of time that the metering keeps each state.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Node identifier |

eid | char | Node external id |

sid | integer | |

ent | integer | |

lane | integer | Metering lane |

state | integer | State index, from 0 to 9 |

active_time | double | Active time in seconds |

active_time_D | double | Active time in seconds: Std Deviation |

active_time_percentage | double | Percentage of active time |

active_time_percentage_D | double | Percentage of active time: Std Deviation |

### Hybrid Traffic Management ¶

#### HYTRAFFICMANAGEMENT¶

This table contains information related to some of traffic management actions; in particular, for Turn Closures, Force Turns, and Destination Change actions.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Traffic management action identifier |

eid | integer | Traffic management action external identifier |

sid | integer | |

ent | integer | |

affected_vehicles | integer | Number of vehicles affected by the traffic management action during the time interval |

affected_vehicles_D | integer | Number of vehicles affected by the traffic management action during the time interval: Std Deviation |

#### HYPTSTOPTIMES¶

This table contains the time spent for each Transit Vehicle at each Transit Stop

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier |

oid | integer | Transit Stop ID |

idveh | integer | Vehicle ID |

starttime | double | Time Sta when the vehicle arrives at the stop in seconds |

endtime | double | Time Sta when the vehicle leaves the stop in seconds (0 value means the vehicle is still at the transit stop) |

idline | integer | Transit Line ID |

## HCM Tables¶

The HCM tables contain information about HCM areas The HCMHY family of tables give the output for hybrid simulations while the HCMMI family of tables give the output for microsimulation. The algorithms used to calculate the statistics are described in the HCM Algorithms Section

#### HCM APPROACH Table¶

The HCMHYAPPROACH and HCMMIAPPROACH tables give the statistics on approaches to junctions. Two types of analysis are contained here In terms of output calculation during the simulation:

- Longitudinal analysis computes measures for the vehicles on the sections to the approach and aggregates these
- Spatial analysis computes measures for the sections and lane and aggregates these for the time interval.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Vehicle type identifier |

eid | VARCHAR(128) | Vehicle type External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

density(_D) | double | Density of vehicles [PCU/mi per lane] (Spatial) |

los(_D) | double | Level of Service (Spatial) |

queue_delay(_D) | double | Queue delay [s/veh] (Longitudinal) |

queue_length(_D) | double | Average queue length [veh] (Spatial) |

avg_back_queue(_D) | double | Average back of queue [m] (Spatial) |

max_back_queue(_D) | double | Maximum back of queue [m] (Spatial) |

queue_overflow(_D) | double | Overflow rate [between 0 and 1] (Spatial) |

queued_vehicles(_D) | double | Percent queued vehicles [%] (Spatial) |

segment_delay(_D) | double | Segment delay [s/veh] (Longitudinal) |

stopped_delay(_D) | double | Stopped delay [s/veh] (Longitudinal) |

nbstops(_D) | double | Number of stops (Longitudinal) |

slow_vehicles(_D) | double | Percent slow vehicles [%] (Spatial) |

#### HCM AREA Tables¶

The HCMHYAREA and HCMMIAREA give the statistics for merge and weave areas

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Vehicle type identifier |

eid | VARCHAR(128) | Vehicle type External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

density(_D) | double | Density of vehicles [PCU/mi per lane] |

Los(_D) | double | Level of Service |

#### HC NODE Tables¶

he HCMHYAREA and HCMMIAREA give the Level of Service measure for nodes

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Vehicle type identifier |

eid | VARCHAR(128) | Vehicle type External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

node_approach_queue_delay | double | Average Approach Delay: Queue Delay |

Los | double | Level of Service |

#### HCMHYSECTION Table¶

The HCMHYSECTION and HCMMISECTION tables give the statistics on sections

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Vehicle type identifier |

eid | VARCHAR(128) | Vehicle type External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

los(_D) | double | Level of Service (Spatial) |

density(_D) | double | Density of vehicles [PCU/mi per lane] (Spatial) |

queue_delay(_D) | double | Queue delay [s/veh] (Longitudinal) |

segment_delay(_D) | double | Segment delay [s/veh] (Longitudinal) |

stopped_delay(_D) | double | Stopped delay [s/veh] (Longitudinal) |

nbstops(_D) | double | Number of stops |

slow_vehicles(_D) | double | Percent slow vehicles [%] (Spatial) |

queue_length(_D) | double | Average queue length [veh] (Spatial) |

mean_back_queue(_D) | double | Average back of queue [m] (Spatial) |

max_back_queue(_D) | double | Maximum back of queue [m] (Spatial) |

queue_overflow(_D) | double | Percent overflow [%] (Spatial) |

## Vehicle Subpaths Database ¶

These tables gather data related to individual vehicles on subpaths and the aggregated data for each subpath. The derivation of the measurements is described in the Statistics Calculations:Subpaths Section and the subpaths summaries.

#### SUBPATH_VEHICLES¶

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Simulation Vehicle ID |

vehtype | integer | Vehicle Type |

idsubpath | integer | Subpath identifier |

ttime | float | Travel time |

speed | float | Average speed on subpath |

delay | float | The difference between achieved time and freeflow time |

wtimeVQ | float | Waiting time in a virtual queue including vehicles inside (seconds) |

dwelltime | float | Time at stops |

travel | float | Travel distance (m) |

stoptime | float | Time spent at a stop in the subpath |

nbstops | float | Number of stops in the subpath |

#### MISUBPATH Table¶

It contains statistical information of the subpaths for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Subpath identifier |

eid | char | Subpath external ID |

sid | integer | |

ent | integer | |

count(_D) | double | Vehicles completing the subpath |

flow(_D) | double | Mean flow (veh/h) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel(_D) | double | Total number of km traveled in the subpath |

traveltime(_D) | double | Total travel time experienced in the subpath (seconds) |

stime(_D) | double | Mean stop time(seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the subpath |

batteryc(_D) | double | Total kWh of battery consumed in the subpath |

#### MESUBPATH Table¶

This contains statistical information of private vehicles. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Object identifier (subpath, centroid, transit line...) |

eid | char | External ID |

sid | integer | |

ent | integer | Time interval |

count(_D) | double | Number of vehicles |

flow_(D) | double | Flow of vehicles |

speed_(D) | double | Mean speed (km/h) |

spdh_(D) | double | Harmonic mean speed (km/h) |

ttime_(D) | double | Mean travel time (seconds) |

dtime_(D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel_(D) | double | Total number of km traveled |

traveltime_(D) | double | Total travel time experienced (seconds) |

#### HYSUBPATH Table¶

This contains statistical information of the subpaths for each period. Where a variable has a mean and a standard deviation from multiple replications, the variable is represented as "var" and "var_D"; the mean and standard deviation respectively.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication or Average identifier |

oid | integer | Subpath identifier |

eid | char | Subpath external ID |

sid | integer | |

ent | integer | Time interval |

count(_D) | double | Number of vehicles entering the subpath |

flow(_D) | double | Flow of vehicles entering the subpath (veh/h) |

speed(_D) | double | Mean speed (km/h) |

spdh(_D) | double | Harmonic mean speed (km/h) |

ttime(_D) | double | Mean travel time (seconds) |

dtime(_D) | double | Mean delay time (seconds) |

wtimeVQ(_D) | double | Mean waiting time in a virtual queue per vehicle including vehicles inside (seconds) |

travel(_D) | double | Total number of km traveled in the subpath |

traveltime(_D) | double | Total travel time experienced in the subpath (seconds) |

stime(_D) | double | Mean Stop Time (seconds) |

nstops(_D) | double | Number of stops per vehicle |

fuelc(_D) | double | Total liters of fuel consumed in the subpath |

batteryc(_D) | double | Total kWh of battery consumed in the subpath |

## Dynamic Traffic Assignment Database¶

This table contains the costs calculated as a part of the Dynamic Traffic Assignment processes. The algorithms used to gather these costs are documented in the Theory: Link Cost Functions Section.

#### LINKCOSTS¶

LinkCosts This table contains statistics of each path.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Object ID |

eid | char | External ID (optional) |

sid | integer | Subobject Pos (optional) to store data of an object disaggregated by a criteria (vehicle type in sections for example) |

ent | integer | Entry number, from 1 to N where is the number of intervals, and 0 contains the aggregation of all the intervals |

cost | double | Link cost used to calculate the paths |

cost_D | double | Link cost deviation from an average |

defaultCost | double | Link cost using the default cost function |

defaultCost_D | double | Default link cost deviation from an average |

## DUE Convergence Tables¶

There are three tables defined in the DUE result for the convergence data: DUERGAP, DUEITERDATA, DUELINKDATA. These tables give information on the level of convergence reached in Dynamic User Equilibrium results.

#### DUEITERDATA¶

This table gives information on calculation times and the levels of vehicles in/out and waiting to enter the network by iteration.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Replication ID |

eid | char | Replication external ID (optional) |

iteration | integer | number of iteration |

dnl | integer | Time for the Dynamic Network Loading. This is the time of the simulation |

sp | float | Time for the shortest path calculation |

msa | float | Time for the Method of Successive Averages algorithm |

rgap | float | Time for the calculation of the Relative Gap |

waitout | integer | Number of vehicles that are waiting to enter the network |

inside | integer | Number of vehicles that are currently in the network |

goneout | integer | Number of vehicles that have left the network |

#### DUERGAP¶

This table gives information on the RGap, flow convergence and cost convergence reached in each iteration with respect to the targets set in the Dynamic Traffic Assignment tab.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Replication ID |

eid | char | Replication external ID (optional) |

iteration | integer | number of iteration |

slice | char | Interval |

rgap | float | Relative Gap |

flowConvg | float | Global flow convergence |

costConvg | float | Global cost convergence |

#### DUELINKDATA¶

This table gives information on the count and cost of every link in the network by iteration.

Attribute Name | Type | Description |
---|---|---|

did | integer | Replication identifier (Origin Data ID) |

oid | integer | Turn or exit section ID |

eid | char | Section external ID (optional) |

iteration | integer | number of iteration |

slice | char | Interval |

sid | integer | Vehicle Type data ID |

count | integer | Count |

cost | float | Cost |

## Macroscopic Modeling Tables¶

### Meta Information Table Examples¶

The following example shows the contents of all the meta information tables (for sections and system).

#### SIM_INFO Table Example¶

Table for a Static Assignment Experiment with ID 650 and simulated from 08:00AM for a 1 hour period.

did | didname | efdid | dideid | use_eid | twhen | from_time |
---|---|---|---|---|---|---|

650 | Static Assignment Experiment 650 | 650 | 0 | 2011-02-24 | 28800 |

duration | seed | type | warm_up | loading | mod_ver |
---|---|---|---|---|---|

3600 | 0 | 1 | 0 | macro | FrankWolfe 7.0.0 (R12367) |

iterations | exec_date | xid | xname | scid |
---|---|---|---|---|

1 | 2013-10-23T10:40:51 | 650 | Static Assignment Experiment 650 | 648 |

scname | simstat intervals | totalstat intervals | simdetec intervals | totaldetec intervals | model |
---|---|---|---|---|---|

Static Assignment Scenario 648 | 1 | 1 | 1 | 0 | {3ca15671-7e3b-46b5-a791-b2967d8750e9} |

trafficdemand | ptplan | masterplan | exec_date_end | user_name |
---|---|---|---|---|

327 | 281 | 0 | 2013-10-23T10:40:52 | aimsun_user |

#### META_INFO Table Example¶

The traffic assignment will gather data for the sections (MASECT table), turns (MATURN), detectors (MADET), centroid connections (MACON), and two vehicle types (sob: 2 + 1). The gathering interval is 3600 seconds, that is, one hour.

did | tname | tyname | nbo | souse | sob | eiduse | sinterval | nbkeys |
---|---|---|---|---|---|---|---|---|

650 | MASECT | GKSection | 78 | 1 | 3 | 0 | 3600000 | 1 |

650 | MATURN | GKTurning | 100 | 1 | 3 | 0 | 3600000 | 1 |

650 | MADET | GKDetector | 6 | 1 | 3 | 0 | 3600000 | 1 |

650 | MACON | GKCenConnection | 20 | 1 | 3 | 0 | 3600000 | 1 |

650 | MATRAJ | GKSuperNodeTrajectory | 0 | 1 | 3 | 0 | 3600000 | 1 |

#### META_SUB_INFO Table Example¶

This table contains information for the user classes. In this case, for car-any (id 639), and the aggregated values All (PCUs) (id 0) and All (vehs) (id 2147483647).

did | tname | pos | oid | oname |
---|---|---|---|---|

650 | MASECT | 0 | 0 | |

650 | MASECT | 1 | 2147483647 | |

650 | MASECT | 2 | 639 | car |

650 | MATURN | 0 | 0 | |

650 | MATURN | 1 | 2147483647 | |

650 | MATURN | 2 | 639 | car |

#### META_COLS Table Example¶

Here is an example for the META_COLS table.

did | tname | colname | coltype | aggtype | intervalaggtype |
---|---|---|---|---|---|

650 | MASECT | volume | 6 | 1 | 0 |

650 | MASECT | flow | 6 | 1 | 0 |

650 | MASECT | occupancy | 6 | 1 | 0 |

650 | MASECT | cost | 6 | 1 | 0 |

650 | MATURN | volume | 6 | 1 | 0 |

650 | MATURN | flow | 6 | 1 | 0 |

650 | MATURN | percentage | 6 | 1 | 0 |

650 | MATURN | cost | 6 | 1 | 0 |

### Information Table Examples¶

Based on the previous tables we know that we have information tables (MASECT, MATURN, etc.) with 1 interval (the assignment duration) and one vehicle type (0 for All (PCUs), 1 for All (vehs), and 2 for car).

For the sections table, we will list two objects (oid: 106 and 107), as an example. For all the tables, the did value will be 650 as this is the ID of the replication that has generated all the tables:

did | oid | eid | sid | ent | volume | flow | occupancy | cost |
---|---|---|---|---|---|---|---|---|

650 | 106 | 0 | 1 | 2172.04 | 2172.04 | 80.45 | 0.8692 | |

650 | 107 | 0 | 1 | 2276.16 | 2276.16 | 84.3 | 0.9957 | |

650 | 106 | 1 | 1 | 2157.04 | 2157.04 | -1 | 0.8692 | |

650 | 107 | 1 | 1 | 2276.16 | 2276.16 | -1 | 0.9957 | |

650 | 106 | 2 | 1 | 2147.04 | 2147.04 | 79.5 | 0.8692 | |

650 | 107 | 2 | 1 | 2276.16 | 2276.16 | 84.3 | 0.9957 |

## Static Assignment Model Tables¶

The Tables defined in the Aimsun Next Static Assignment Results Database are: MASECT, MATURN, MACON, MATRAJ, MADET, MAROU and PLCONV. As these tables are derived from a single experiment with no replications and no random variation, there are no "_D" table attributes giving the standard deviation of the results. The calculations made to find the link costs are coded in the Static Macro VDF (Volume Delay Functions), the Static Macro TPF (Turn Penalty Functions), the Static Macro JDF (Junction Delay Functions), and the Static Macro Function Components. The calculations made to estimate the corresponding flows depend on the type of assignment chosen. This is documented in the Static Traffic Assignment: User Equilibrium Models Section

#### MASECT Table¶

This contains statistical information of the sections for the static traffic assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Section identifier |

eid | VARCHAR(128) | Section External ID |

sid | INTEGER | Vehicle type (from 0 for All vehicles, to number of vehicle types. See the META_SUB_INFO table for more info about each vehicle type) |

ent | INTEGER | Time intervals, always 1 |

volume | FLOAT | Volume (PCUs and vehs for All, vehs for each vehicle type) |

flow | FLOAT | Flow (PCUs/h and vehs/h for All, vehs/h for each vehicle type) |

occupancy | FLOAT | Section occupancy (%) |

cost | FLOAT | Cost (VDF units) |

#### MATURN Table¶

This contains statistical information of the turns for the static traffic assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Turn identifier |

eid | VARCHAR(128) | Turn external id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

volume | FLOAT | Volume (PCUs and vehs for All, vehs for each vehicle type) |

flow | FLOAT | Flow (PCUs/h and vehs/h for All, vehs/h for each vehicle type) |

percentage | FLOAT | Percentage from origin section using the turn |

cost | FLOAT | Cost in JDF & TPF units |

#### MACON Table¶

This contains statistical information of the centroid connections for the static traffic assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Centroid connection identifier |

eid | VARCHAR(128) | Centroid connection External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

volume | FLOAT | Volume (PCUs and vehs for All, vehs for each vehicle type) |

flow | FLOAT | Flow (PCUs/h and vehs/h for All, vehs/h for each vehicle type) |

cost | FLOAT | Cost in JDF & TPF units |

#### MATRAJ Table¶

This contains statistical information of the supernode trajectories for the static traffic assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Supernode trajectory identifier |

eid | VARCHAR(128) | Supernode trajectory External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

volume | FLOAT | Volume (PCUs and vehs for All, vehs for each vehicle type) |

flow | FLOAT | Flow (PCUs/h and vehs/h for All, vehs/h for each vehicle type) |

percentage | FLOAT | Percentage from origin section using the supernode trajectory |

cost | FLOAT | Cost in JDF & TPF units |

#### MADET Table¶

This contains statistical information of the detectors for the static traffic assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Detector identifier |

eid | VARCHAR(128) | Detector External id |

sid | INTEGER | Vehicle type (from 0 for all vehicles to number of vehicle types. See the META_SUB_INFO table for more info about each vehicle type) |

ent | INTEGER | Time intervals always 1 |

volume | FLOAT | Volume (PCUs and vehs for All vehs for each vehicle type) |

flow | FLOAT | Flow (PCUs/h and vehs/h for All vehs/h for each vehicle type) |

#### MAROU Table¶

This contains statistical information of the subpaths for the static traffic assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Subpath identifier |

eid | VARCHAR(128) | Subpath External id |

sid | INTEGER | Vehicle type (from 0 for all vehicles to number of vehicle types. See the META_SUB_INFO table for more info about each vehicle type) |

ent | INTEGER | Time intervals |

volume | FLOAT | Volume (PCUs and vehs for All |

flow | FLOAT | Flow (PCUs/h and vehs/h for All |

cost | FLOAT | Cost in JDF & TPF units |

#### PLCONV Table¶

This contains information about the convergence of the static traffic assignment for each vehicle.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Vehicle type identifier |

eid | VARCHAR(128) | Vehicle type External id |

iteration | INTEGER | ID of the assignment iteration (from 0 to number of iterations calculated) |

relGap | FLOAT | Relative gap achieved |

lambda | FLOAT | Lambda |

itime | FLOAT | Iteration time (seconds) |

ttime | FLOAT | Total time (seconds) |

## Static OD Adjustment Model Tables¶

When an Adjustment is executed, the corresponding Assignment Results for the Adjusted Demand can be saved in the Database with the structure explained in the previous paragraphs. Also, Adjustment information (the matrices, the convergence, etc.) can be stored in a binary file. The reference to this file is kept in a table in the Database.

#### OUTPUTFILES Table¶

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static OD Adjustment Experiment identifier |

type | VARCHAR(32) | Type of Output file (adj, apa, etc.) |

path | VARCHAR(256) | File Path |

file | VARCHAR(128) | File name |

## Travel Demand Modeling Tables¶

The Travel Demand modeling tables provide data from the Generation and Attraction process and the Trip Distribution process.

### Meta Information Table Examples¶

The following example shows the contents of all the meta information tables (for sections and system).

#### SIM_INFO Table Example¶

Table for an Transit Assignment Experiment with ID 12060 and assigned from 08:00AM for a 1 hour period.

did | didname | efdid | dideid | use_eid | twhen | from_time |
---|---|---|---|---|---|---|

2572 | GA Exp | 2572 | 6011 | 0 | 25200 | |

10263 | Distribution Experiment 2012 | 10263 | 0 | 25200 | ||

12060 | Transit Assignment Experiment PT | 12060 | 0 | 2012-10-26 | 61200 |

duration | seed | type | warm_up | loading | mod_ver |
---|---|---|---|---|---|

10800 | 0 | 0 | 0 | generation/attraction | Generation/Attraction-8.0.2 (R25639) |

10800 | Distribution | Distribution-8.0.4 (R28905) | |||

3600 | 0 | 1 | 0 | macro | PTFrequencyBased 8.0.0 (R21518) |

iterations | exec_date | xid | xname | scid |
---|---|---|---|---|

0 | 2013-10-28T14:07:47 | 2572 | GA Exp | 2571 |

0 | 2014-05-21T17:35:28 | 10263 | Distribution Experiment 2012 | 10262 |

1 | 2013-01-07T11:28:08 | 12060 | Transit Assignment Experiment PT | 12059 |

scname | simstat intervals | totalstat intervals | simdetec intervals | totaldetec intervals | model |
---|---|---|---|---|---|

Generation/Attraction Scenario 2571 | 0 | 0 | 0 | 0 | {7b84027d-f98a-4fce-b448-a1f3ea2bc474} |

Distribution Scenario 2012 | 0 | 0 | 0 | 0 | {ab4edd07-4828-4dd3-8e16-10637d83b137} |

Transit Assignment Scenario PT | 1 | 1 | 1 | 0 | {7b84027d-f98a-4fce-b448-a1f3ea2bc474} |

traffic demand | ptplan | masterplan | exec_date_end | user_name |
---|---|---|---|---|

0 | 0 | 0 | 2013-10-28T14:07:47 | Aimsun_user |

0 | 0 | 0 | 2014-05-21T17:35:28 | Aimsun_user |

12092 | 11732 | 0 | 2013-01-07T11:28:09 | Aimsun_user |

#### META_INFO Table Example¶

A Transit Assignment will produce tables MAPTLINE, MAPTSECTION, MAPTSTATION, and MAPTSTOP. In the following example, there are 57 transit lines and the gathering interval is 3600 seconds, that is, one hour.

did | tname | tyname | nbo | souse | sob | eiduse | sinterval | nbkeys |
---|---|---|---|---|---|---|---|---|

12060 | MAPTSTOP | GKBusStop | 334 | 1 | 57 | 0 | 3600000 | 1 |

12060 | MAPTSTATION | GKPTStation | 0 | 1 | 57 | 0 | 3600000 | 1 |

12060 | MAPTSECTION | GKSection | 367 | 1 | 57 | 0 | 3600000 | 1 |

12060 | MAPTLINE | GKPublicLine | 57 | 0 | 1 | 0 | 3600000 | 1 |

#### META_SUB_INFO Table Example¶

This table contains information for the transit lines.

did | tname | pos | oid | oname |
---|---|---|---|---|

12060 | MAPTSTOP | 0 | 0 | |

12060 | MAPTSTOP | 1 | 11637 | Line 1 N-S |

12060 | MAPTSTOP | 2 | 11635 | Line 1 S-N |

¦ | ¦ | ¦ | ¦ | ¦ |

12060 | MAPTSTATION | 0 | 0 | |

12060 | MAPTSTATION | 1 | 11637 | Line 1 N-S |

12060 | MAPTSTATION | 2 | 11635 | Line 1 S-N |

¦ | ¦ | ¦ | ¦ | ¦ |

12060 | MAPTSECTION | 0 | 0 | |

12060 | MAPTSECTION | 1 | 11637 | Line 1 N-S |

12060 | MAPTSECTION | 2 | 11635 | Line 1 S-N |

¦ | ¦ | ¦ | ¦ | ¦ |

#### META_COLS Table Example¶

Here is the list of all the fields in the information tables. All the values are stored as double (coltype 6).

did | tname | colname | coltype | aggtype | intervalaggtype |
---|---|---|---|---|---|

12060 | MAPTSTOP | alighting_to_centroid | 6 | 1 | 0 |

12060 | MAPTSTOP | alighting_for_transfer | 6 | 1 | 0 |

12060 | MAPTSTOP | alighting_for_external_transfer | 6 | 1 | 0 |

12060 | MAPTSTOP | alighting_for_station_transfer | 6 | 1 | 0 |

12060 | MAPTSTOP | boarding_from_centroid | 6 | 1 | 0 |

12060 | MAPTSTOP | boarding_for_transfer | 6 | 1 | 0 |

12060 | MAPTSTOP | boarding_for_external_transfer | 6 | 1 | 0 |

12060 | MAPTSTOP | boarding_for_station_transfer | 6 | 1 | 0 |

12060 | MAPTSTATION | alighting_to_centroid | 6 | 1 | 0 |

12060 | MAPTSTATION | alighting_for_external_transfer | 6 | 1 | 0 |

12060 | MAPTSTATION | alighting_for_station_transfer | 6 | 1 | 0 |

12060 | MAPTSTATION | boarding_from_centroid | 6 | 1 | 0 |

12060 | MAPTSTATION | boarding_for_external_transfer | 6 | 1 | 0 |

12060 | MAPTSTATION | boarding_for_station_transfer | 6 | 1 | 0 |

12060 | MAPTSECTION | ptsegment | 6 | 1 | 0 |

12060 | MAPTSECTION | ptload | 6 | 1 | 0 |

12060 | MAPTSECTION | frequency | 6 | 1 | 0 |

12060 | MAPTSECTION | capacity | 6 | 1 | 0 |

12060 | MAPTLINE | Capacity | 6 | 1 | 0 |

12060 | MAPTLINE | Frequency | 6 | 1 | 0 |

### Information Table Examples¶

Based on the previous example tables we know that a Generation/Attraction experiment was executed and the results are stored in three information tables:

#### GAPARAMETERS Table Example¶

did | aggregated_modes | car_mode_id | period_id | centroid_ configuration |
---|---|---|---|---|

2572 | -1 | 2497 | 615 |

#### GATOTALS Table Example¶

did | car_availability | mode_id | purpose_id | generated_before_balancing | attracted_before_balancing | generated_after_balancing | attracted_after_balancing |
---|---|---|---|---|---|---|---|

2572 | NoDistinction | 2662 | 2562 | 2056.3 | 2148.9 | 2148.9 | 2148.9 |

2572 | NoDistinction | 2662 | 2563 | 1197.76 | 1808 | 1080 | 1080 |

2572 | NoDistinction | 2662 | 2564 | 533.64 | 456.08 | 494.86 | 494.86 |

#### GAVALUES Table Example¶

did | car_availability | purpose_id | mode_id | centroid_id | generation_attraction | value |
---|---|---|---|---|---|---|

2572 | NoDistinction | 2562 | 2662 | 37 | attraction | 100 |

2572 | NoDistinction | 2562 | 2662 | 38 | attraction | 48 |

... | ... | ... | ... | ... | ... | ... |

We also executed a Distribution+Modal Split experiment:

#### OUTPUTFILES Table Example¶

did | type | path | file |
---|---|---|---|

130 | Dis | C:/tmp | DistributionResult_10263.dis |

... | ... | ... | ... |

We also know that we executed a Transit Assignment and that we have three information tables (MAPTLINE, MAPTSECTION, MAPTSTATION, and MAPTSTOP) with 1 interval (the assignment duration).

#### MAPTLINE Table Example¶

did | oid | eid | sid | ent | Capacity | Frequency |
---|---|---|---|---|---|---|

12060 | 11725 | 70 | 0 | 1 | 60 | 12 |

... | ... | ... | ... | ... | ... | ... |

#### MAPTSECTION Table Example¶

did | oid | eid | sid | ent | ptsegment | load | frequency | capacity |
---|---|---|---|---|---|---|---|---|

12060 | 11262 | 22 | 0 | 1 | 0 | 14 | 12 | 120 |

... | ... | ... | ... | ... | ... | ... | ... | ... |

#### MAPTSTATION Table Example¶

(Column names shortened for practicality, see MAPTSTATION Table for complete names)

did | oid | eid | sid | ent | al_to_cent | al_for_ext_tr | al_for_st_tr | bo_from_cent | bo_for_ext_tr | bo_for_st_tr |
---|---|---|---|---|---|---|---|---|---|---|

12060 | 10847 | 1340 | 0 | 1 | 3 | 0 | 0 | 5 | 3 | 0 |

... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

#### MAPTSTOP Table Example¶

(Column names shortened for practicality, see MAPTSTOP Table for complete names)

did | oid | eid | sid | ent | al_to_cent | al_for_tr | al_for_ext_tr | al_for_st_tr | bo_from_cent | bo_for_tr | bo_for_ext_tr | bo_for_st_tr |
---|---|---|---|---|---|---|---|---|---|---|---|---|

12060 | 10847 | 1340 | 0 | 1 | 3 | 0 | 0 | 0 | 5 | 3 | 3 | 0 |

... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

### Generation/Attraction Tables¶

The tables defined for the Generation/Attraction Results are: GAPARAMETERS, GATOTALS, and GAVALUES. The algorithms that define how these values are derived are document in the Trip Generation Theory Section

#### GAPARAMETERS Table¶

This contains the information on the Generation/Attraction parameters.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Generation/Attraction Experiment identifier |

aggregated_modes | VARCHAR(128) | List of aggregated modes (if any) |

car_mode_id | INTEGER | Transportation Mode marked as Car mode |

period_id | INTEGER | Time Period ID |

centroid_configuration_id | INTEGER | Centroid Configuration ID |

#### GATOTALS Table¶

This contains Generation/Attraction global result values.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Generation/Attraction Experiment identifier |

car_availability | VARCHAR(32) | Type of car availability distinction considered (CA |

mode_id | INTEGER | Transportation Mode ID |

purpose_id | INTEGER | Trip Purpose ID |

generated_before_balancing | FLOAT | Total number of trips generated before balancing |

attracted_before_balancing | FLOAT | Total number of trips attracted before balancing |

generated_after_balancing | FLOAT | Total number of generated tripsattracted_after_balancing |

#### GAVALUES Table¶

This contains the Generation/Attraction Vectors information.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Generation/Attraction Experiment identifier |

car_availability | VARCHAR(32) | Type of car availability distinction considered (CA |

purpose_id | INTEGER | Trip Purpose ID |

mode_id | INTEGER | Transportation Mode ID |

centroid_id | INTEGER | Centroid ID |

generation_attraction | VARCHAR(32) | Whether the value is Generated or Attracted |

value | FLOAT | Number of trips |

#### OUTPUTFILES Table¶

This contains the path to other files for retrieving results.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Generation/Attraction Experiment identifier |

type | VARCHAR(32) | Type of experiment that produced the file |

path | VARCHAR(256) | File path |

file | VARCHAR(128) | File name |

### Loop Controller Results Tables¶

#### LOOPRESULTS Table¶

This contains the results of the loop controller as shown in the UI.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Loop Controller identifier |

iteration | INTEGER | Number of iterations |

dataObjectType | INTEGER | If 0: Maximum Demand Supply Gap (%), if 1: refers to an OD Matrix, if 2: Skim Matrix |

valueType | INTEGER | If 0: Maximum S.S.I. Distance to Unit, if 1: Maximum Relative Gap (%), if 2: Maximum Demand Supply Gap (%) |

value | FLOAT | value |

## Transit Assignment Tables¶

The Tables defined in the Aimsun Next Static Assignment Results Database are: MASECT, MATURN, MACON, MATRAJ, MADET, MAROU, and PLCONV. In addition, for Transit Assignment results, there are the following tables:

#### MAPTLINE Table¶

This contains statistical information on the Transit Lines for the Transit assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Section identifier |

eid | VARCHAR(128) | Section External id |

sid | INTEGER | Vehicle type (from 0 for All vehicles |

ent | INTEGER | Time intervals |

Capacity | FLOAT | Transit Line Capacity |

Frequency | FLOAT | Transit Line Frequency |

#### MAPTSECTION Table¶

This contains statistical information on the Transit Sections for the Transit assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Turn identifier |

eid | VARCHAR(128) | Turn external id |

sid | INTEGER | Vehicle type (from 0 for all vehicles |

ent | INTEGER | Time intervals |

transitsegment | FLOAT | Section segment (each transit stop divides the section in two segments) |

ptload | FLOAT | Transit Section Load |

frequency | FLOAT | Section Frequency adding up the lines that go through |

capacity | FLOAT | Section Capacity adding up the capacities of the transit vehicles that go through |

#### MAPTSTATION Table¶

This contains statistical information on the Transit Stations for the Transit assignments performed.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Turn identifier |

eid | VARCHAR(128) | Turn external id |

sid | INTEGER | Vehicle type (from 0 for all vehicles |

ent | INTEGER | Time intervals |

alighting_to_centroid | FLOAT | Number of passengers that alight directly for destination |

alighting_for_external_transfer | FLOAT | Number of passengers that alight for transfer to a stop in another station |

alighting_for_station_transfer | FLOAT | Number of passengers that alight for transfer to a stop in the same station |

boarding_from_centroid | FLOAT | Number of passengers that board directly from origin |

boarding_for_external_transfer | FLOAT | Number of passengers that board from transfer from a stop in another station |

boarding_for_station_transfer | FLOAT | Number of passengers that board from transfer from a stop in the same station |

#### MAPTSTOP Table¶

This contains statistical information on the Transit Stops.

Attribute Name | Type | Description |
---|---|---|

did | INTEGER | Static Assignment Experiment identifier |

oid | INTEGER | Vehicle type identifier |

eid | VARCHAR(128) | Vehicle type External id |

sid | INTEGER | |

ent | INTEGER | Time intervals, always 1 |

alighting_to_centroid | FLOAT | Number of passengers that alight directly for destination |

alighting_for_transfer | FLOAT | Number of passengers that alight for transfer |

alighting_for_external_transfer | FLOAT | Number of passengers that alight for transfer to a stop in another station |

alighting_for_station_transfer | FLOAT | Number of passengers that alight for transfer to a stop in the same station |

boarding_from_centroid | FLOAT | Number of passengers that board directly from origin |

boarding_for_transfer | FLOAT | Number of passengers that board from transfer |

boarding_for_external_transfer | FLOAT | Number of passengers that board from transfer from a stop in another station |

boarding_for_station_transfer | FLOAT | Number of passengers that board from transfer from a stop in the same station |