MATSim-T: Architecture and Simulation Times

MATSim-T: Architecture and Simulation Times

Michael Balmer (ETH Zürich, Switzerland), Marcel Rieser (TU Berlin, Germany), Konrad Meister (ETH Zürich, Switzerland), David Charypar (ETH Zürich, Switzerland), Nicolas Lefebvre (ETH Zürich, Switzerland) and Kai Nagel (TU Berlin, Germany)
Copyright: © 2009 |Pages: 22
DOI: 10.4018/978-1-60566-226-8.ch003
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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, 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.
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By tradition, transport planning simulation models tend to be macroscopic or mesoscopic (e.g. de Palma & Marchal, 2002; PTV, 2008). Reasons for this are access to aggregated data only (e.g. traffic counts, commuter matrices, etc.) and limitations in computational hardware to calculate and store detailed computations. These limitations have changed in the last few decades. Performance of computer hardware has been continually growing—and still grows—while the cost of machines stays fixed. For transport planning, the relevant developments in computer hardware are:

  • The capacities of fast random access memory (RAM) have increased dramatically.

  • Multi processor hardware allows one to perform parallel computation without using (maintenance intensive) computer clusters.

  • Shared memory architectures allow fast on-demand access to the physical memory for an arbitrary amount of processes.

In the same manner, the available data used in transport planning are getting more detailed and complex. Good examples for that are person diary surveys (e.g. Hanson & Burnett, 1982; Axhausen et al, 2002; Schönfelder et al, 2002), and analysis of individual transport behavior based on GPS data (e.g. Wolf et al, 2004).

Therefore, the demands made to transport planning software are getting more complex, too. Micro-simulation is becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. Some advantages over conventional models are:

  • Computational savings when compared to the calculation and storage of large multidimensional probability arrays necessary in other methods.

  • Larger range of output options, from overall statistics to information about each synthetic traveler in the simulation.

  • Explicit modeling of the individuals' decision-making processes.

The last point is important since it is not a vehicle that produces traffic; it is the person that drives the vehicle. Persons do not just produce traffic; instead they try to manage their day (week, life) in a satisfying way. They go to work to earn money, they go hiking for their health and pleasure, they visit their relatives for pleasure or because they feel obliged to do so, they shop to cook a nice dinner at home, and so on. Since not all of this can be done at the same location, they travel, which produces traffic. To plan an efficient day, many decisions have to be made by each person. They decide where to perform activities, which mode to choose to get from one location to another, in which order and at which time activities should be performed, with whom to perform certain activities, and so on. Some decisions are made hours (days, months) in advance while others are made spontaneously as reactions to specific circumstances. Furthermore, many decisions induce other decisions. Therefore, it is important to model the complete time horizon of the decision makers.

Transport simulation models should be able to implement (at least part of) such an individual decision horizon and assign the outcome to a traffic model, since it is the complete daily schedules (and the decisions behind that) that produce traffic. This chapter presents such a micro-simulation, called MATSim-T (Multi Agent Transport Simulation Toolkit), implemented as a Java application, usable on any operating system. The work is part of the research project MATSim ( In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. On the basis of integrated (daily) individual demand optimization in MATSim-T, the system is extended such that it provides flexible handling of a large variety of input data; extensibility of models and algorithms; a simple interface for new models and algorithms; (dis)aggregation for different spatial resolutions; robust interfaces to third party models, programs, and frameworks; unlimited number of individuals; and an easily usable interface to handle new input data elements.

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List of Reviewers
Table of Contents
Ana Bazzan, Franziska Klügl
Ana Bazzan, Franziska Klügl
Chapter 1
Takeshi Takama
This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both... Sample PDF
Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems: Concepts and a Case Study of the Road User Charging Scheme at the Upper Derwent
Chapter 2
Qi Han, Theo Arentze, Harry Timmermans, Davy Janssens, Geert Wets
Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling... Sample PDF
A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns
Chapter 3
Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, Kai Nagel
Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In... Sample PDF
MATSim-T: Architecture and Simulation Times
Chapter 4
Ulf Lotzmann
In this chapter an agent-based traffic simulation approach is presented which sees agents as individual traffic participants moving in an artificial... Sample PDF
TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications
Chapter 5
Paulo A.F. Ferreira, Edgar F. Esteves, Rosaldo J.F. Rossetti, Eugénio C. Oliveira
Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors... Sample PDF
Applying Situated Agents to Microscopic Traffic Modelling
Chapter 6
Andreas Schadschneider, Hubert Klüpfel, Tobias Kretz, Christian Rogsch, Armin Seyfried
Multi-Agent Simulation is a general and powerful framework for understanding and predicting the behaviour of social systems. Here the authors... Sample PDF
Fundamentals of Pedestrian and Evacuation Dynamics
Chapter 7
Rex Oleson, D. J. Kaup, Thomas L. Clarke, Linda C. Malone, Ladislau Bölöni
The “Social Potential”, which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in... Sample PDF
"Social Potential" Models for Modeling Traffic and Transportation
Chapter 8
Sabine Timpf
In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show... Sample PDF
Towards Simulating Cognitive Agents in Public Transport Systems
Chapter 9
Kurt Dresner, Peter Stone, Mark Van Middlesworth
Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this... Sample PDF
An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles
Chapter 10
Heiko Schepperle, Klemens Böhm
Current intersection-control systems lack one important feature: They are unaware of the different valuations of reduced waiting time of the... Sample PDF
Valuation-Aware Traffic Control: The Notion and the Issues
Chapter 11
Charles Desjardins, Julien Laumônier, Brahim Chaib-draa
This chapter studies the use of agent technology in the domain of vehicle control. More specifically, it illustrates how agents can address the... Sample PDF
Learning Agents for Collaborative Driving
Chapter 12
Kagan Tumer, Zachary T. Welch, Adrian Agogino
Traffic management problems provide a unique environment to study how multi-agent systems promote desired system level behavior. In particular, they... Sample PDF
Traffic Congestion Management as a Learning Agent Coordination Problem
Chapter 13
Matteo Vasirani, Sascha Ossowski
The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context... Sample PDF
Exploring the Potential of Multiagent Learning for Autonomous Intersection Control
Chapter 14
Tomohisa Yamashita, Koichi Kurumatani
With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the... Sample PDF
New Approach to Smooth Traffic Flow with Route Information Sharing
Chapter 15
Denise de Oliveira, Ana L.C. Bazzan
In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in... Sample PDF
Multiagent Learning on Traffic Lights Control: Effects of Using Shared Information
Chapter 16
Tamás Máhr, F. Jordan Srour, Mathijs de Weerdt, Rob Zuidwijk
While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from... Sample PDF
The Merit of Agents in Freight Transport
Chapter 17
Lawrence Henesey, Jan A. Persson
In analyzing freight transportation systems, such as the intermodal transport of containers, often direct monetary costs associated with... Sample PDF
Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation
Chapter 18
Shawn R. Wolfe, Peter A. Jarvis, Francis Y. Enomoto, Maarten Sierhuis, Bart-Jan van Putten
Today’s air traffic management system is not expected to scale to the projected increase in traffic over the next two decades. Enhancing... Sample PDF
A Multi-Agent Simulation of Collaborative Air Traffic Flow Management
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