Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation:
TL;DR: To the authors' knowledge, this is the first time traveler-based information is taken from an ABDG and used in a MATSim, and the results are compared with real-world traffic counts from about 100 measurement stations.
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Abstract: The typical method to couple activity-based demand generation (ABDG) and dynamic traffic assignment (DTA) is time-dependent origin-destination (O-D) matrices. With that coupling method, the individual traveler's information gets lost. Delays at one trip do not affect later trips. However, it is possible to retain the full agent information from the ABDG by writing out all agents' plans, instead of the O-D matrix. A plan is a sequence of activities, connected by trips. Because that information typically is already available inside the ABDG, this is fairly easy to achieve. Multiagent simulation (MATSim) takes such plans as input. It iterates between the traffic flow simulation (sometimes called network loading) and the behavioral modules. The currently implemented behavioral modules are route finding and time adjustment. Activity resequencing or activity dropping are conceptually clear but not yet implemented. Such a system will react to a time-dependent toll by possibly rearranging the complete day; in con...
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Figures
FIGURE 3 Number of trip departures over course of day in (a) Iteration 0 and (b) Iteration 80, differentiated by type of primary activity of the corresponding plan. 
FIGURE 2 Agents’ average score of first 80 iterations. 
FIGURE 1 Structure of activity chains generated by Kutter model. 
FIGURE 4 Time-of-day-dependent analysis of simulation outcome: (a) comparison of average volume-to-capacity ratios over the course of a day and (b) average relative error when comparing real-world counts with simulated volumes.
Citations
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Integration of Activity-Based Modeling and Dynamic Traffic Assignment
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