Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

Abstract

Microsimulation is becoming increasingly important in traffic demand modeling. The major advantage over traditional four-step models is the ability to simulate each traveler individually. Decision-making processes can be included for each individual. Traffic demand is the result of the different decisions made by individuals; these decisions lead to plans that the individuals then try to optimize. Therefore, such microsimulation models need appropriate initial demand patterns for all given individuals. The challenge is to create individual demand patterns out of general input data. In practice, there is a large variety of input data, which can differ in quality, spatial resolution, purpose, and other characteristics. The challenge for a flexible demand-modeling framework is to combine the various data types to produce individual demand patterns. In addition, the modeling framework has to define precise interfaces to provide portability to other models, programs, and frameworks, and it should be suitable for large-scale applications that use many millions of individuals. Because the model has to be adaptable to the given input data, the framework needs to be easily extensible with new algorithms and models. The presented demand-modeling framework for large-scale scenarios fulfils all these requirements. By modeling the demand for two different scenarios (Zurich, Switzerland, and the German states of Berlin and Brandenburg), the framework shows its flexibility in aspects of diverse input data, interfaces to third-party products, spatial resolution, and last but not least, the modeling process itself.

Decentralized control of plug-in electric vehicles under driving uncertainty

“…The driving pattern data is obtained from a transport simulation for Switzerland, with the tool MATSim [15]. With the deterministic data of the transport simulation as reference, 100 random samples of driving patterns are generated for each vehicle.…”

“…From the agent-based transport simulation MATSim [15], we obtain the arrival and departure time of each trip for each vehicle, and the associated energy consumption. Based on that reference sample, we generate different samples of driving patterns for each vehicle, as in [16].…”

Combined smart-charging and frequency regulation for fleets of plug-in electric vehicles

“…Constraints (10) and (11) guarantee that the aggregator can decrease or increase the charging power by the contracted power capacity. Finally (13) and (12) ensure that there is sufficient energy capacity available for the worst-case expected increase or decrease in the energy content of the virtual storage.…”

“…Information on PEV driving patterns, such as arrival and departure times and locations is obtained from a transportation simulation called MATSim [11]. In this agent-based simulation model, each agent has a set of daily activities (e.g.…”

Modeling and Estimation of Combined Route and Activity Location Choice

“…We link flexible but little formalized representations of individual mobility behavior such as agent-based demand generation and microsimulation [8], [9] with mathematically well understood methodologies borrowed from control engineering. More precisely, we consider the problem of estimating agents' route and activity location choice in a Bayesian setting, combining for every agent an a priori activity plan for a given day with anonymous traffic measurements such as flows or densities into a most likely a posteriori plan.…”