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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