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Traffic Simulation with Aimsun

In: Fundamentals of Traffic Simulation

Author

Listed:
  • Jordi Casas

    (TSS – Transport Simulation Systems
    Universitat de Vic)

  • Jaime L. Ferrer

    (TSS – Transport Simulation Systems)

  • David Garcia

    (TSS – Transport Simulation Systems)

  • Josep Perarnau

    (TSS – Transport Simulation Systems)

  • Alex Torday

    (TSS – Transport Simulation Systems)

Abstract

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.

Suggested Citation

  • Jordi Casas & Jaime L. Ferrer & David Garcia & Josep Perarnau & Alex Torday, 2010. "Traffic Simulation with Aimsun," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 173-232, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-6142-6_5
    DOI: 10.1007/978-1-4419-6142-6_5
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    Citations

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    Cited by:

    1. Lv, Wei & Song, Wei-guo & Liu, Xiao-dong & Ma, Jian, 2013. "A microscopic lane changing process model for multilane traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1142-1152.
    2. Itani, Ibrahim MS & Cassidy, Michael J. PhD & Daganzo, Carlos F. PhD, 2021. "Synergies of Combining Demand- and Supply-Side Measures to Manage Congested Streets," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt71j7z3wv, Institute of Transportation Studies, UC Berkeley.
    3. van Lint, J.W.C. & Calvert, S.C., 2018. "A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modelling driver distraction," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 63-86.
    4. Benedikt Schwab & Christof Beil & Thomas H. Kolbe, 2020. "Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems," Sustainability, MDPI, vol. 12(9), pages 1-25, May.
    5. Arsalan Raza & Muhammad Umair Ali & Ubaid Ullah & Muhammad Fayaz & Muhammad Junaid Alvi & Karam Dad Kallu & Amad Zafar & Sarvar Hussain Nengroo, 2022. "Evaluation of a Sustainable Urban Transportation System in Terms of Traffic Congestion—A Case Study in Taxila, Pakistan," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    6. Itani, Ibrahim & Cassidy, Michael J. & Daganzo, Carlos, 2021. "Synergies of combining demand- and supply-side measures to manage congested streets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 172-179.
    7. Treiber, Martin & Kanagaraj, Venkatesan, 2015. "Comparing numerical integration schemes for time-continuous car-following models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 183-195.
    8. Faryal Ali & Zawar Hussain Khan & Khurram Shehzad Khattak & Thomas Aaron Gulliver & Ahmed B. Altamimi, 2023. "A Microscopic Traffic Model Incorporating Vehicle Vibrations Due to Pavement Condition," Mathematics, MDPI, vol. 11(24), pages 1-24, December.
    9. Veronika Harantová & Alica Kalašová & Simona Kubíková, 2021. "Use of Traffic Planning Software Outputs When a New Highway Section Is Put into Operation," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
    10. Mylonas, Chrysostomos & Mitsakis, Evangelos & Kepaptsoglou, Konstantinos, 2023. "Criticality analysis in road networks with graph-theoretic measures, traffic assignment, and simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).

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