Preliminary Results of a Multiagent Traffic Simulation for Berlin

This paper provides an introduction to multi-agent traffic simulation. Metropolitan regions can consist of several million inhabitants, implying the simulation of several million travelers, which represents a considerable computational challenge. We reports on our recent case study of a real-world Berlin scenario. The paper explains computational techniques necessary to achieve results. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the re-unification, are considerably larger than in previous scenarios that we have treated

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

Emergent effects in multi-agent simulations of road pricing

Road pricing is debated as an option of transportation policy. Especially in metropolitan areas congestion pricing is promising to reduce congestion and to protect the environment. In order to reach the promised results the choice and design of a policy is very important, especially in a ”second-best” context. Therefore it is worth to attempt detailed predictions of the effects and implications of the planned pricing scheme. Most if not all state-of-thepractice methodologies forecasting those effects are • aggregate and in consequence do not consider social and economic characteristics of individual travelers. • static in time and in consequence do not consider temporal effects such as toll avoidance In order to bridge this gap, multi-agent microsimulations can be used. Our large-scale multi-agent traffic simulation is capable to simulate a complete day-plan of up to seven million individuals (agents). In contrast to other approaches, our simulation truly traces the synthetic travelers through their day, thus enabling us (at least in principle) to model emergent effects such as complex re-scheduling across the whole day. This paper describes the implementation of a toll-scheme for the bigger Zurich area and presents the results of the simulation. We point out how agents (population) react to changed prices of transportation by modifying their consumption patterns. The analysis of the policy is based on the performance of simulated day-plans of the agents. This performance is directly given by a utility function, which is used to measure gains and losses of different groups of inhabitants in the research area. Based on these measurements we provide an economic interpretation of the policy and highlight emergent phenomena like changes in route choice and time reactions

Multi-agent transport simulations and economic evaluation

Tolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects: – Demographic attributes like income and time constraints – Time reactions to the policy – Schedule changes of population’s individuals during the whole day Our approach uses multi-agent simulations to model and simulate full daily plans. Each of our agents has a utility function that appraises the performance of a typical, microscopically simulated day. The sum of all utility changes to a policy change can be interpreted as the change in the system’s welfare thus the economic evaluation of a measure straightforward. The approach is tested with travel behavior of the Zurich metropolitan region in Switzerland. Several tolling schemes are investigated. It is shown that the simulation can be used to model travelers’ reactions to time-dependent tolls in a way most existing transportation planning tools are not able to do. It is demonstrated that route adjustment only, as is done in many traditional transport planning packages, results in no economic gains from the tolls. As time-dependent tolls are a much-debated subject in transportation politics, the ability to fully model such tolls and the reactions of travelers may help to find better toll schemes. In a world where individuals have more and more freedom to schedule their daily plans, agent-based simulations offer an intuitive way to research complex topics with lots of interdependencies

Application of the VISEVA demand generation software to Berlin using publicly available behavioral data

Mittels des in diesem Artikel beschriebenen EVA-Ansatzes lassen sich wegzweckspezifische und zeitvariante Quelle-Ziel-Matrizen auf der Basis allgemeiner Inputdaten zur Raumstruktur und zum Verkehrsverhalten erzeugen. Die differenzierten Matrizen liefern einen Hinweis darauf, wo und wann, die im Modell abgebildeten Aktivitäten stattfinden. Dieser Output kann als Startlösung für die Erstellung von Aktivitätenplänen in Mulit-Agenten-Simulationen dienen. Der Artikel erläutert die Grundzüge des EVA-Ansatzes, beschreibt die Datenaufbereitung und vergleicht die ermittelten Ergebnisse mit dem bestehenden Personenverkehrsmodell der Stadtverwaltung Berlins sowie realen Zählwerten aus dem Straßennetz. Es kann aufgezeigt werden, dass mit einem minimalen Dateneinsatz geeignete Matrizen erzeugt werden konnten, die als Startlösung für die Bildung von Aktivitätenplänen herangezogen werden können

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

PRELIMINARY RESULTS OF A MULTIAGENT TRAFFIC SIMULATION FOR BERLIN

This paper provides an introduction to multiagent traffic simulation. It includes some description of where we are with respect to the implementation of a real-world Berlin scenario. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the reunification, are considerably larger than in previous scenarios that we have treated.Traffic simulation, multiagent simulation, large-scale real-world scenario