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Vehicle based Simulators

Vehicle based simulators simulate the interactions of individual vehicles as they move on the road and are therefore ideal for studying the effect of detailed changes to the network. They have proven to be very useful for testing new traffic control systems and management policies, based either on traditional technologies or as implementation of Intelligent Transport Systems. Vehicle-based simulators in Aimsun Next can simulate adaptive traffic control systems such as SCATS, SCATS-RMS, VS-PLUS, UTOPIA, Yunex UTC with SCOOT (This requires the Adaptive Software Interfaces license extension); vehicle actuated, control systems that give priority to transit, Advanced Traffic Management Systems (using VMS, traffic calming strategies, ramp metering policies, etc.), Vehicle Guidance Systems, Transit Vehicle Scheduling, and Control Systems or applications aimed at estimating the environmental impact of pollutant emissions, and energy consumption.

Aimsun Next has three modes for simulating individual vehicles, the Microscopic Simulator, the Mesoscopic Simulator, and the Hybrid Simulator. Which modes are available with which licenses is shown below:

Pro Advanced Expert
Micro y (Pro Micro) y y
Meso y (Pro Meso) y y
Hybrid n y y

The documentation of vehicle based simulation is covered in 5 sections:

In Microsimulation, time is quantized into short fixed intervals and the actions of each and every vehicle are calculated at every time step. The behavior of each vehicle in the network is therefore modeled throughout the simulation time as it travels through the traffic network, interacting with the other vehicles in the network, interacting with the control systems in the network and reacting to incidents programmed into the simulation. Different types of vehicles are modeled, from small cars to large good vehicles with different driving dynamics. Different drivers are modeled with changes to characteristics such as reaction times and aggressiveness. The microsimulator can also simulate the interactions between vehicles and pedestrians moving in the same area. The pedestrians are simulated by using an embedded pedestrian simulator.

In a Mesoscopic simulation, the vehicle is also modeled as an individual entity, exactly the same as the microscopic approach but the behavioral models (e.g., car following, lane changing, etc.) are modified to predict the speed and lane choice of a vehicle only at the start and end of a road section and not at every time step in the simulation. The simulation is therefore event based rather than discrete time based, the events being the arrival of a vehicle at a node or at the start or end of a road section. The vehicle is not explicitly simulated while it is inside a road section but the prediction of when it will appear at the end of the section will take into account the traffic conditions(i.e. congestion, flow, bus stops ...) in that section. Therefore, not all vehicles are updated at each time, only those where an event is scheduled are considered and hence mesoscopic simulation runs much faster than microscopic simulation for the same number of vehicles in the network.

In the Hybrid Meso-Micro approach, the simulation concurrently applies the microscopic model in selected areas and the mesoscopic in the rest. The hybrid model is recommended for large-scale networks which also contain specific areas where the level of detail needs to be microscopic (for example, for actuated control, transit priority, pedestrian modeling, detection or adaptive control systems) but with a global network evaluation. The use of the mesoscopic model in the other areas means that the simulation requires less computational time.

In the Hybrid Macro-Meso approach, the simulation is vehicle based and concurrently vehicles are assigned to macro sections by adding 1 in the assigned volume and the mesoscopic network loading is applied in the mesoscopic sections. This hybrid model is also recommended for even larger networks like whole region or country based models where you need to have some zones with higher details that they are very difficult to achieve when using a full macroscopic model. The use of this hybrid macro-meso approach is appropriated to decrease the computational time of the model or the detailed geometry of some areas are missing or the control plan definition is missing in some parts of the model.