# OD Adjustment¶

An accurate OD matrix, disaggregated by vehicle type and by departure time is essential to replicating the traffic demand in a transport model and without a well calibrated pattern of demand represented by a set of OD matrices, it will be difficult to reproduce the congestion observed in the physical network and the route path choices made by travelers.

The initial travel demand can be derived from the first three stages of a four step model, it can be derived from an OD survey made using mobile phone data or ANPR data,or it can be imported from a pre-existing transport model. In all cases though, these prior matrices should be adjusted to match the flows and turn counts observed on the road network to refine them and provide a set of demand matrices that are both based on the outputs of the demand model or the demand survey and also in agreement with the observed flows.

There are Four OD adjustment experiment types in Aimsun Next:

### Static OD Adjustment¶

A Static OD Adjustment experiment adjusts the demand matrices using a static assignment to provide the route paths used by vehicles between OD pairs. Adjustment is iterative and at every N^th^ iteration, the route paths are recomputed, using a static assignment experiment, to account for the changes in paths and path use due to the changes in the adjusted demand. A static OD adjustment experiment modifies the matrices used in a static assignment which are uniform for the assignment period.

### Static OD Departure Adjustment¶

A Static OD Departure Adjustment takes the matrices as used in a static assignment, which represent the demand over the entire modeled period and splits them into a set of matrices each covering a shorter time slice in the modeled period. The demand profile of these split matrices is then adjusted to assign the demand over the whole period into the time slices such that the profile of departures in across the time slices will then, in a dynamic assignment, reproduce the profiles of the traffic flows in the observed data.

This departure adjustment experiment refines the matrices derived from the static adjustment, the data for both experiments must therefore be consistent between the two. The OD matrix totals in the Static OD Adjustment and the Static OD Departure Adjustment are not constrained to be equal, but should not vary significantly assuming the detector counts are consistent.

### Dynamic OD Adjustment¶

A Dynamic OD Adjustment adjusts the demand matrices using a dynamic assignment to provide the route paths used by vehicles between OD pairs. In a dynamic OD adjustment, the matrices are adjusted using a rolling horizon where time interval 1 is iteratively adjusted first which then sets the congestion conditions to adjust time series 2, etc., until the full period is covered. A dynamic OD adjustment requires observed data at the same time intervals as the matrices used in the dynamic assignment scenario, typically at several intervals per hour to reflect the changing demand over a peak period.

### Dynamic OD Departure Time Rescheduling¶

A Dynamic OD Departure Time Rescheduling Experiment adjusts the traffic demand to change the departure time for trips. This simulates the responses made by travelers who have a preferred arrival time and must change their departure time to maintain that preference as congestion rises in future scenarios and journey times become longer. The dynamic departure time adjustment process makes changes in departure time similar to the OD matrix time segmentation used in the modeled scenario. The departure time rescheduling is based on the HADES (Heterogeneous Arrival and Departure Times with Equilibrium Scheduling)algorithm which models micro time choice for arrival time at a destination where time shifts are typically measured in tens of minutes.