Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation

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 consequence, it goes far beyond DTA (which just does route adaptation). This paper reports on the status of the current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to the authors' knowledge, this is the first time traveler-based information (and not just O-D matrices) is taken from an ABDG and used in a MATSim. The simulation results are compared with real-world traffic counts from about 100 measurement stations

Truly agent-oriented coupling of an activity-based demand generation with a multi-agent traffic simulation

The typical method to couple activity-based demand generation (ABDG) and dynamic traffic assignment (DTA) are time-dependent origin destination matrices. With that coupling method, the individual traveler's information gets lost. Delays at one trip do not affect later trips. It is, however, possible to retain the full agent information from the ABDG by writing out all agents' ?plans?, instead of the OD matrix. A plan is a sequence of activities, connected by trips. Since that information is typically already available inside the ABDG, this is fairly easy to achieve. 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 re-sequencing or activity dropping are conceptually clear but not yet implemented. Such a system will react to a time-dependent toll by possibly re-arranging the complete day; in consequence, it goes far beyond DTA (which just does route adaptation). Our paper will report the status of our current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to our knowledge, this is the first time that traveler-based information (and not just OD matrices) is taken from an ABDG and used in a multi-agent simulation. The simulation results are compared against real world traffic counts from about 100 measurement stations

Agent-oriented coupling of an activity-based demand generation with a multi-agent traffic simulation

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 consequence, it goes far beyond DTA (which just does route adaptation). This paper reports on the status of the current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to the authors' knowledge, this is the first time traveler-based information (and not just O-D matrices) is taken from an ABDG and used in a MATSim. The simulation results are compared with real-world traffic counts from about 100 measurement stations.ISSN:0361-1981ISSN:2169-405