An Agent-Based Simulation for Destination Choice of Discretionary Tours: Evidence from Qazvin, Iran

Document Type : Research Article


Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran.


Spatial analysis and distribution are of great importance to transportation planners, especially in traffic demand management. Simulation is an important tool in the planning and management of transportation systems to achieve an estimation of real system behavior to evaluate different scenarios. Regarding the aggregate nature and inability to consider heterogeneity among the individuals in a large number of discrete choice models and the high cost of data collection through questionnaires, using a disaggregate and heterogeneous agent approach can be used to evaluate different policies. Since each agent is inherently autonomous and interacts with different agents and the environment to achieve its goals, this paper aims to use the agent-based approach to simulate the destination choice of discretionary tours of Qazvin citizens. Individual socioeconomic characteristics and travel information questionnaires (revealed preference) of 9938 households and 29840 individuals in 12 municipality districts of Qazvin were collected. After extracting 12 types of activity patterns including shopping and recreation trips, the simulation of destination choice in MATLAB has been studied using the Reinforcement Learning algorithm (RL) and reward-punishment functions which are based on the relative attractiveness of districts for various modes and travel times. High correlation (above 0.9) results were achieved among simulated trip destination choice distributions and observed survey data using the RL algorithm which illustrates the algorithm's goodness of fit; also the simulation results and survey data have a similar trend among districts which illustrates that the simulation findings have real-world implications.


Main Subjects

  1. Abbasi, M.H. Hosseinlou, S. JafarzadehFadaki, An investigation of Bus Rapid Transit System (BRT) based on economic and air pollution analysis (Tehran, Iran), Case Studies on Transport Policy, 8(2) (2020) 553-563.
  2. Tayarani Yousefabadi, A. Mahpour, I. Farzin, A. Mohammadian Amiri, The Assessment of the Change in the Share of Public Transportation by Applying Demand Management Policies: Evidence from Extending Traffic Restriction to the Air Pollution Control Area in Tehran, AUT Journal of Civil Engineering, (2020).
  3. Ding, W. Wang, M. Yang, Application of an agent-based modeling to simulate destination choice for shopping and recreation, Procedia-Social and Behavioral Sciences, 96 (2013) 1198-1207.
  4. de Dios Ortúzar, L.G. Willumsen, Modelling transport, John Wiley & sons, 2011.
  5. Kikuchi, J. Rhee, D. Teodorović, Applicability of an agent-based modeling concept to modeling of transportation phenomena, Yugoslav Journal of Operations Research, 12(2) (2002) 141-156.
  6. N. Koushik, M. Manoj, N. Nezamuddin, Machine learning applications in activity-travel behaviour research: a review, Transport reviews, 40(3) (2020) 288-311.
  7. R. Tavares, A.L.C. Bazzan, Reinforcement learning for route choice in an abstract traffic scenario, in: VI Workshop-Escola de Sistemas de Agentes, seus Ambientes e aplicaçoes (WESAAC), 2012, pp. 141-153.
  8. Wei, S. Ma, N. Jia, A day-to-day route choice model based on reinforcement learning, Mathematical Problems in Engineering, 2014 (2014).
  9. Zhang, J.-M. Xu, A dynamic route guidance arithmetic based on reinforcement learning, in: 2005 International Conference on Machine Learning and Cybernetics, IEEE, 2005, pp. 3607-3611.
  10. Charypar, K. Nagel, Generating complete all-day activity plans with genetic algorithms, Transportation, 32(4) (2005) 369-397.
  11. H.R. Hajiagha, S.S. Hashemi, Y. Mohammadi, E.K. Zavadskas, Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria, Transport, 31(1) (2016) 108-118.
  12. Rasaizadi, I. Farzin, F. Hafizi, Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips, Physica A: Statistical Mechanics and its Applications, 586 (2022) 126492.
  13. Alvarez, J.G. Brida, An agent‐based model of tourism destinations choice, International Journal of Tourism Research, 21(2) (2019) 145-155.
  14. J. Vitins, A. Erath, Destination choice modeling with spatially distributed constraints, in: 19th Swiss Transport Research Conference (STRC 2019), STRC, 2019.
  15. L. Bazzan, R. Grunitzki, A multiagent reinforcement learning approach to en-route trip building, in: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, 2016, pp. 5288-5295.
  16. Yang, Y. Yang, W. Wang, H. Ding, J. Chen, Multiagent-based simulation of temporal-spatial characteristics of activity-travel patterns using interactive reinforcement learning, Mathematical Problems in Engineering, 2014 (2014).
  17. Janssens, Y. Lan, G. Wets, G. Chen, Allocating time and location information to activity-travel patterns through reinforcement learning, Knowledge-Based Systems, 20(5) (2007) 466-477.
  18. Zuo, K. Ozbay, A. Kurkcu, J. Gao, H. Yang, K. Xie, Microscopic simulation based study of pedestrian safety applications at signalized urban crossings in a connected-automated vehicle environment and reinforcement learning based optimization of vehicle decisions, Advances in Transportation Studies, (2020).
  19. Chen, W. Chen, Z. Chen, A Multi-Agent Reinforcement Learning approach for bus holding control strategies, Advances in Transportation Studies, (2015).
  20. S. Sutton, A.G. Barto, Reinforcement learning: An introduction, MIT Press, 2018.
  21. Botvinick, S. Ritter, J.X. Wang, Z. Kurth-Nelson, C. Blundell, D. Hassabis, Reinforcement learning, fast and slow, Trends in cognitive sciences, 23(5) (2019) 408-422.
  22. Wilensky, W. Rand, An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo, MIT Press, 2015.
  23. M. Schwartz, Multi-agent machine learning: A reinforcement approach, John Wiley & Sons, 2014.
  24. Qazvin urban transportation master plan (QUTMP) Modeling and calibration of travel demand models, 2012.
  25. Askari, F. Peiravian, N. Tilahun, M. Yousefi Baseri, Determinants of users’ perceived taxi service quality in the context of a developing country, Transportation Letters, 13(2) (2021) 125-137.