Fundamentals of Traffic Simulation

This introductory chapter to a book on traffic simulation fundamentals is aimed at setting up a comprehensive framework for simulation as a well-established and grounded OR technique and its specificities when applied to traffic systems; the main approaches to traffic simulation and the principles of traffic simulation model building; the fundamentals of traffic flow theory and its application to traffic simulation from macroscopic, mesoscopic, or microscopic approaches. The chapter also provides a basic overview on the principles of dynamic traffic assignment and its application to traffic simulation and the calibration and validation of traffic simulation models, two key topics to establish the validity and credibility for traffic simulation models being used in the decision-making processes.

After two decades of academic research the microscopic, behavior-based multi-purpose traffic flow simulator VISSIM had been introduced in 1994 to analyze and optimize traffic flows. It offers a wide variety of urban and highway applications, integrating public and private transportation. A large part of this chapter is devoted to modeling principles of VISSIM, core traffic flow models consisting of longitudinal and lateral movements of vehicles on multilane streets, a conflict resolution model at areas with overlapping trajectories, dynamic assignment and the social force model applied to pedestrians. Techniques to calibrate the core traffic flow models are discussed briefly.

In this chapter, AVENUE (Advanced & Visual Evaluator for road Networks in Urban arEas), the traffic simulation model which was developed in Japan, is described in terms of its traffic modeling and applications. AVENUE is a hybrid traffic simulation in the sense that the flow model is based on the fluid dynamics but the images of displayed vehicles are discrete for the convenience to deal with lane-wise details. AVENUE is normally applied to a small- to middle-size network such as one with 100 intersections. The flow model in AVENUE is detailed as in microscopic models, and it has a route choice function for several different user categories. Since 1992, it has been continuously upgraded so as to include additional functions for some special applications explained in this chapter.

S-Paramics is a microsimulation modelling package that has been developed by SIAS in Scotland over a period of 20 years. It has been available commercially since 2000 and is used to model road traffic at a scale ranging from single junction analysis to wide area networks.

S-Paramics simulates the intentions, decisions and subsequent actions of each driver in the road network as they move towards their destination. Each driver chooses their best route based on the basic network characteristics and their knowledge of the likely congestion they will encounter. As they move through the network, each driver prioritises a set of decisions over which lane to use, what speed to select, when to change lane and when to cross or merge with traffic in another lane.

Calibration in S-Paramics is driven by correctly describing the network topology and the travel demand in it. For example, values such as saturation flows or lane use proportions are regarded as outputs from S-Paramics to examine the performance of the road network and may not be used as inputs to assist calibration. By modelling the cause of an action rather than prescribing the effect in the model, the predictive power of the simulation is preserved in subsequent changes to the model to test proposed road network changes.

This chapter is dedicated to the Aimsun transport simulation software, with particular emphasis on its dynamic simulation capabilities. The main topics discussed are the modelling of section dynamics using microscopic and mesoscopic approaches, and algorithms for solving the dynamic traffic assignment problem. The introductory section provides background information together with a discussion of the development principles behind Aimsun: integration, modularity, scalability, interoperability, and extensibility. Section 5.1 provides an overview of the project development process covering model building, verification, calibration and validation, and analysis of outputs. Section 5.3 outlines the logic of the microscopic and mesoscopic simulation processes along with information about the behavioural models at each level. Solving the dynamic traffic assignment problem using Aimsun is the focus of Section 5.4. We cover three different methods for tackling the problem, based on dynamic user equilibrium (DUE) and stochastic route choice models with and without memory. In Section 5.5 we turn to the subject of calibration and validation of Aimsun models. This section describes different Aimsun tools which can be used for verification and validation, and provides guidelines or examples relating to the calibration of behavioural models and dynamic traffic assignment algorithms. Section 5.6 looks at the methods that can be used to extend Aimsun’s modelling capabilities. It covers both working with external applications and the use of various programming tools. Α selection of advanced case studies and applications is the focus of Section 5.7. It describes how Aimsun has been used to solve transportation engineering problems with reference to three real-world examples.In the final section, we describe Aimsun Online and discuss its implementation in Madrid as an advanced case study. In the latter part of the section, we comment on some challenges related to such applications and the development needs that such projects give rise to.

MITSIMLab (MIcroscopic Traffic SIMulation Laboratory) is a microscopic traffic simulation model that evaluates the impacts of alternative traffic management system designs at the operational level and assists in their subsequent refinement. MITSIMLab models the travel and driving behavior of individual vehicles, the detailed movement of transit vehicles, and the various control and information provision strategies through a generic controller. A calibration methodology for important parameters and inputs was also developed. The model has been extended to address the special driving behavior evidenced in urban networks and has been used as a test bed for the evaluation of advanced traveler information systems (ATIS). Calibration and validation results from networks in the United States and Europe are discussed.

SUMO is a microscopic road traffic simulation made available as open source under the GPL license. The complete suite includes tools for importing road networks, generating routes from different sources, and two versions of the traffic simulation itself, one started from the command line and one including a graphical user interface. The simulation uses the microscopic, space-continuous and time-discrete car-following model developed by S. Krauß and a lane-changing model developed within the work on the simulation. Traffic assignment is normally performed using the iterative approach formulated by C. Gawron, but further methods, such as a one-shot assignment method, exists. The traffic simulation offers a socket-based interface to external applications, allowing to interact with a running simulation online. Values and states of objects the simulation consists of can be both retrieved and changed. SUMO has been used within different projects both by the DLR and by external organizations. The software and documentation can be accessed at http://sumo.sf.net.

This chapterprovides an updated overview on the main functions of DRACULA model. The recent focus of our research on the issues of model calibration and validation is discussed. Some of the extended features of the software, on modelling the overtaking behaviour on two-lane rural roads and the fully integration with a microscopic model of public transport operations and demand, are represented.

Dynameq is a simulation-based dynamic traffic assignment (DTA) model. This model employs an iterative solution method to find the user-optimal assignment of time-varying origin–destination demands to paths through a road network where the path travel times – which depend on the assigned path flows – are time-varying and determined using a detailed traffic simulation model. Increasing congestion and the use of increasingly sophisticated measures to manage it – such as adaptive traffic control, reserved, reversible and tolled lanes, and time-varying congestion pricing – have created a need for models that are more detailed and realistic than static assignment models traditionally used in transportation planning. DTA models have begun to fill that need and have been successfully applied on real-world networks of significant size. This chapter provides a description of the assignment and simulation models that comprise the software, a discussion of fundamental concepts such as user-equilibrium and stability, an introduction to calibration methodology for simulation-based DTA, and a brief description of a typical project.

DynaMIT (Dynamic Network Assignment for the Management of Information to Travelers) is a dynamic traffic assignment model system that estimates and predicts traffic. DynaMIT is also a real-time system for decision support at traffic management centers for generation of predictive traffic information. A planning version also exists. DynaMIT captures the dynamic performance of the network (e.g., lane-based queuing and spillback effects), travel behavior, its sensitivity to traffic conditions and available traffic information, and consistency between demand and supply. DynaMIT consists of a demand simulator, a supply simulator, and algorithms that capture demand and supply interactions. Methodologies for the online and offline estimation of OD flows and the offline and online calibration of various inputs and parameters (such as network performance parameters) have been developed as well. Several case studies from the United States, Europe, and Asia are discussed, and a distributed version of DynaMIT is also presented.

This chapter presents the macroscopic simulation tool METANET along with several options, variations, and extensions. METANET simulates complex traffic flow phenomena on motorway networks under all possible traffic conditions (free flow, critical, congested) including the possible presence of incidents. Modeling of the drivers’ routing behavior is enabled and a number or related options are offered to the user. The simulator is readily connected to potential control strategies for a variety of control measures including ramp metering, motorway-to-motorway control, driver information and route guidance, variable speed limits, mainline metering, and shoulder lane opening. Model validation procedures and selected application examples are presented. Extension of METANET includes online METANET (for real-time decision support), METANET-DTA (for exact dynamic traffic equilibrium), AMOC (for optimal control including any combination of control measures), and RENAISSANCE (for advanced real-time traffic surveillance).