Traffic&Transportation Journal
Sign In / Sign Up


An Automatic Calibration Procedure of Driving Behaviour Parameters in the Presence of High Bus Volume
Nima Dadashzadeh, Murat Ergun, Sercan Kesten, Marijan Žura


Most of the microscopic traffic simulation programs used today incorporate car-following and lane-change models to simulate driving behaviour across a given area. The main goal of this study has been to develop an automatic calibration process for the parameters of driving behaviour models using metaheuristic algorithms. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a combination of GA and PSO (i.e. hybrid GAPSO and hybrid PSOGA) were used during the optimization stage. In order to verify our proposed methodology, a suitable study area with high bus volume on-ramp from the O-1 Highway in Istanbul has been modelled in VISSIM. Traffic data have been gathered through detectors. The calibration procedure has been coded using MATLAB and implemented via the VISSIM-MATLAB COM interface. Using the proposed methodology, the results of the calibrated model showed that hybrid GAPSO and hybrid PSOGA techniques outperformed the GA-only and PSO-only techniques during the calibration process. Thus, both are recommended for use in the calibration of microsimulation traffic models, rather than GA-only and PSO-only techniques.


Barcel J, Codina E, Casas J, Ferrer JL, Garcia D. Microscopic traffic simulation: A tool for the design, analysis and evaluation of intelligent transport systems. Journal of Intelligent and Robotic Systems. 2005;41(2–3): 173-203. Available from: doi:10.1007/s10846-005-3808-2

Barcelo J, Fellendorf M, Vortisch P. Fundamentals of Traffic Simulation. Springer; 2010. Available from: doi:10.1007/978-1-4614-1900-6

Dowling R, Skabardonis A, Alexiadis V. Traffic Analysis Toolbox Volume III : Guidelines for Applying Traffic Microsimulation Modeling Software. U.S. DOT, Federal Highway Administration, Washington, D.C. Rep. No. FHWA-HRT-04-040, July 2004; 135 p.

Ciuffo B, Punzo V, Torrieri V. Comparison of Simulation-Based and Model-Based Calibrations of Traffic-Flow Microsimulation Models. Transportation Research Record: Journal of the Transportation Research Board. 2008;2088: 36-44. Available from: doi:10.3141/2088-05

Ma J, Dong H, Zhang H. Calibration of Microsimulation with Heuristic Optimization Methods. Transportation Research Record: Journal of the Transportation Research Board. 2007;1999: 208-217. Available from: doi:10.3141/1999-22

Chiappone S, Giuffrè O, Granà A, Mauro R, Sferlazza A. Traffic simulation models calibration using speed-density relationship: An automated procedure based on genetic algorithm. Expert Systems with Applications. 2016;44: 147-155. Available from: doi:10.1016/j.eswa.2015.09.024

Menneni S, Sun C, Vortisch P. Microsimulation Calibration Using Speed-Flow Relationships. Transportation Research Record: Journal of the Transportation Research Board. 2008;2088: 1-9. Available from: doi:10.3141/2088-01

Strnad I, Žura M. Genetic algorithms application to EVA mode choice model parameters estimation. International Journal of Mathematical Models and Methods in Applied Sciences. 2011;5(3): 533-541.

Hale DK, Antoniou C, Brackstone M, Michalaka D, Moreno AT, Parikh K. Optimization-based assisted calibration of traffic simulation models. Transportation Research Part C: Emerging Technologies. 2015;55: 100-115. Available from: doi:10.1016/j.trc.2015.01.018

Boittin C, Gaud N, Hilaire V, Meignan D. Particle swarm for calibration of land-use and transport integrated models. CUPUM 2015 - 14th International Conference on Computers in Urban Planning and Urban Management, 7-10 July 2015, Cambridge, Massachusetts, USA; 2015.

Yu M, (David) Fan W. Calibration of microscopic traffic simulation models using metaheuristic algorithms. International Journal of Transportation Science and Technology. 2017;6(1): 63-77. Available from: doi:10.1016/j.ijtst.2017.05.001

PTV. PTV VISSIM 10 User Manual. PTV Planug Trasport Verker AG, Germany; 2017. p. 265-297.

Zhang Q, Ogren RM, Kong SC. A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO-GA and basic GA. Applied Energy. 2016;165: 676-684. Available from: doi:10.1016/j.apenergy.2015.12.044

Garg H. A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation. 2016;274: 292-305. Available from: doi:10.1016/j.amc.2015.11.001

Nik AA, Nejad FM, Zakeri H. Hybrid PSO and GA approach for optimizing surveyed asphalt pavement inspection units in massive network. Automation in Construction. 2016;71(Part 2): 325-345. Available from: doi:10.1016/j.autcon.2016.08.004

Katiyar S. A Comparative Study of Genetic Algorithm and the Particle Swarm Optimization. International Journal of Advanced Scientific and Technical Research. 2013;2(3): 98-103.

Kesten AS, Ergün M, Yai T. An Analysis on Efficiency and Equity of Fixed-Time Ramp Metering. Journal of Transportation Technologies. 2013;03(May): 48-56. Available from: doi:10.4236/jtts.2013.32A006

Schaffer JD, Grefenstette JJ. Multi-Objective learning via genetic algorithms. Journal of Chemical Information and Modeling. 1989;53: 160. Available from: doi:10.1017/CBO9781107415324.004

Haupt RL, Haupt SE. Practical genetic algorithms. Studies in Computational Intelligence. 2nd Ed. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2006. p. 7-22. Available from: doi:10.1007/11543138_2

Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 27 Nov - 1 Dec 1995, Perth, WA, Australia. Vol. 4. IEEE; 1995. p. 1942-1948. Available from: doi:10.1109/ICNN.1995.488968

Mezura-Montes E, Coello Coello CA. Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation. 2011;1(4): 173-194. Available from: doi:10.1016/j.swevo.2011.10.001

Lu Z, Fu T, Fu L, Shiravi S, Jiang C. A video-based approach to calibrating car-following parameters in VISSIM for urban traffic. International Journal of Transportation Science and Technology. 2016;5(1): 1-9. Available from: doi:10.1016/j.ijtst.2016.06.001

Ossen S, Hoogendoorn SP. Heterogeneity in car-following behavior: Theory and empirics. Transportation Research Part C: Emerging Technologies. 2011;19(2): 182-195. Available from: doi:10.1016/j.trc.2010.05.006

Abbas MM, Chong L. Car-Following Trajectory Modeling with Machine Learning - A Showcase for the Merits of Artificial Intelligence. 92nd Annual Meeting of the Transportation Research Board, 13-17 Jan 2013, Washington DC, USA; 2013.

Menneni S, Sun C, Vortisch P. Microsimulation Calibration Using Speed-Flow Relationships. Transportation Research Record: Journal of the Transportation Research Board. 2008;2088: 1-9. Available from: doi:10.3141/2088-01

Durrani U, Lee C, Maoh H. Calibrating the Wiedemann’s vehicle-following model using mixed vehicle-pair interactions. Transportation Research Part C: Emerging Technologies. 2016;67: 227-242. Available from: doi:10.1016/j.trc.2016.02.012

Lu X, Lee J, Chen D, Bared J, Dailey D, Shladover SE. Freeway Micro-simulation Calibration: Case Study Using Aimsun and VISSIM with Detailed Field Data. Transportation Research Board 93rd Annual Meeting. 12-16 Jan 2014. Washington, D.C., USA; 2014; p. 1-17.

Hourdakis J, Michalopoulos P, Kottommannil J. Practical Procedure for Calibrating Microscopic Traffic Simulation Models. Transportation Research Record: Journal of the Transportation Research Board. 2003;1852: 130-139. Available from: doi:10.3141/1852-17

Lee J-B, Ozbay K. New Calibration Methodology for Microscopic Traffic Simulation Using Enhanced Simultaneous Perturbation Stochastic Approximation Approach. Transportation Research Record: Journal of the Transportation Research Board. 2009;2124: 233-240. Available from: doi:10.3141/2124-23

Hollander Y, Liu R. The principles of calibrating traffic microsimulation models. Transportation. 2008;35(3): 347-362. Available from: doi:10.1007/s11116-007-9156-2

Barroso ES, Parente E, Cartaxo de Melo AM. A hybrid PSO-GA algorithm for optimization of laminated composites. Structural and Multidisciplinary Optimization. 2017;55(6): 2111-2130. Available from: doi:10.1007/s00158-016-1631-y

Liang Z, Ouyang J, Yang F. A hybrid GA-PSO optimization algorithm for conformal antenna array pattern synthesis. Journal of Electromagnetic Waves and Applications. 2018;32(13): 1601-1615. Available from: doi:10.1080/09205071.2018.1462257

Sheikhalishahi M, Ebrahimipour V, Shiri H, Zaman H, Jeihoonian M. A hybrid GA-PSO approach for reliability optimization in redundancy allocation problem. International Journal of Advanced Manufacturing Technology. 2013;68(1-4): 317-338. Available from: doi:10.1007/s00170-013-4730-6

Dadashzadeh N, Ergun M. Spatial bus priority schemes, implementation challenges and needs: an overview and directions for future studies. Public Transport. 2018;10(3): 545-570. Available from: doi:10.1007/s12469-018-0191-5

Alpkokin P, Black JA, Iyinam S, Kesten AS, Alpkokin P, Black JA, et al. Historical analysis of economic , social and environmental impacts of the Europe-Asia crossings in Istanbul. International Journal of Sustainable Transportation. 2016;10(2): 65-75. Available from: doi:10.1080/15568318.2013.853852

Alpkokin P, Ergun M. Istanbul Metrobüs: first intercontinental bus rapid transit. Journal of Transport Geography. 2012;24: 58-66. Available from: doi:10.1016/j.jtrangeo.2012.05.009

Dadashzadeh N, Ergun M, Kesten AS, Žura M. Improving the calibration time of traffic simulation models using parallel computing technique. In: Kucharski R, Szarata A (eds.) IEEE MT-ITS2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems. Kraków, Poland. IEEE Xplore; 2019. p. 54. Available from: Book of Abstract 4_06.pdf

Copyright (c) 2023 Nima Dadashzadeh, Murat Ergun, Sercan Kesten, Marijan Žura

Published by
University of Zagreb, Faculty of Transport and Traffic Sciences
Online ISSN
Print ISSN
SCImago Journal & Country Rank
Publons logo
© Traffic&Transportation Journal