Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ‘‘freeze by heating’’? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well. CONTENTS

Knowledge of fundamental traffic flow characteristics of traffic simulation models is an essential requirement when using these models for the planning, design, and operation of transportation systems. In this paper we discuss the following: a description of how features relevant to traffic flow are currently under implementation in the TRANSIMS microsimulation, a proposition for standardized traffic flow tests for traffic simulation models, and the results of these tests for two different versions of the TRANSIMS microsimulation.

This paper describes the parallel implementation of the TRANSIMS traffic micro-simulation.

In a multi-agent transportation simulation, travelers are represented as individual “agents,” who make independent decisions about their actions. We are implementing such a simulation for all of Switzerland, which is composed of modules that model those decisions for each agent, such as: (i) Activities generator, which generates a complete 24-hour day-plan, with each major activity (sleep, eat, work, shop, drink beer), their times, and their locations. (ii) Route planner, which determines the mode of transportation, as well as the actual route plan taken, for each leg of the agent’s chosen activity plan. (iii) Mobility simulation, which executes all plans simultaneously and in consequence computes the interaction between different travelers, leading e.g. to congestion. (iv) Feedback and learning, which resolves the interdependence between the above modules. For example, plans depend on congestion but congestion depends on plans. This is resolved via an iterative method, where an initial plans set is slowly adapted until it is consistent with the resulting travel conditions. This technique has similarities to day-to-day human learning and can also be interpreted that way. – Besides these modules, one also needs input data, such as the road network, or (synthetic) populations. In the future, further modules need to be added,

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