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

Large Scale Microscopic Evacuation Simulation

The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. A robust and flexible simulation framework for such large-scale disasters helps to predict the evacuation process. Existing methods are either geared towards smaller problems (e.g. Cellular Automata techniques or methods based on differential equations) or are not microscopic (e.g. methods based on dynamic traffic assignment). This paper presents a technique that is both microscopic and capable to process large problems.BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

Converting a Static Macroscopic Model Into a Dynamic Activity-Based Model to Analyze Public Transport Demand in Berlin

Transport models demanded by public transport companies today should not only deliver the basis for future planning of the regional transport system, but also provide detailed information about passenger flows of different user groups. This paper presents the successful transformation of a static macroscopic model (built using PTV VISUM) into an integrated activity based demand and dynamic assignment model (MATSim) performed for a real application in the Berlin/Brandenburg metropolitan region. While the two models clearly differ in their methodology, overall key values can be reproduced showing similar results. It is shown that by the use of the activity chain distributions and their timing activity based demand can be reproduced with respect to the trip distribution of the origin-destination matrices from the macroscopic model. The process flow defined in this paper allows to use both models for planning purpose, case studies and effect analysis, enabling public transport companies to analyze effects on the macroscopic level of detail as well as on the agent based level to capture specific customer groups and/or time ranges during the day. The microscopic model is then used for further analyses, of which a selection is presented in this paper. Notably, the model allows for researching effects generated by the interaction of public transport vehicles and regular private car traffic, or for researching user-group specific behavior

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

MATSim Data Containers

The network container was already described in Section 4.1.1. An important additional feature of the network module is using time-dependent network attributes. Network state changes can thus be considered, as e.g., implied by accidents, or adaptive traffic control, with varying speed limits or driving directions of lanes on multi-lane roads with heavily unbalanced loads over the course of a day. Attributes that can be adapted are “free speed”, “number of lanes” and “flow capacity”

Organization: Development Process, Code Structure and Contributing to MATSim

This chapter describes how new functionality enters MATSim. It describes the MATSim team and community, the different roles existing in the MATSim project, the development drivers and processes, and the tools used for integration. The goal is to provide an overview of the development process so that one quickly finds access to the MATSim community and is able to efficiently contribute to MATSim, based on one role or another

Let’s Get Started

This chapter explains how to set up and run MATSim and describes the requirements for building a basic scenario. Updated information may be available from http://matsim.org, in particular from http://matsim.org/docs

Scenarios Overview

This last book part summarizes MATSim scenarios, as located on the map in Figure 52.1 and listed at http://matsim.org/scenarios

MATSim-T : Architecture and Simulation Times

Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In the last decades the shift from using typically aggregated data to more detailed, individual based, complex data (e.g. GPS tracking) andthe continuously growing computer performance on fixed price level leads to the possibility of using microscopic models for large scale planning regions. This chapter presents such a micro-simulation. The work is part of the research project MATSim (Multi Agent Transport Simulation, http://matsim.org). In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. Based on a study of Swiss daily traffic – ca. 2.3 million individuals using motorized individual transport producing about 7.1 million trips, assigned to a Swiss network model with about 60,000 links, simulated and optimized completely time-dynamic for a complete workday – it is shown that the system is able to generate those traffic patterns in about 36 hours computation time