Efficient Urban Structure Inference by Traffic Flow Dynamics using Learning Algorithms

Document Type : Scientific - Research

Authors
hamid yasinian 1
Saeed yasinian 1
amir Abdi moghadam 2
1 Department of computer engineering, Ha.C. islamic azad university, hamedan, iran
2 M.Sc, Department of Industrial Engineering, Malayer Branch, Islamic Azad University, Malayer, Iran
10.22119/jte.2025.374391.2651
Abstract
An optimal connection between important areas of cities is highly important in urban planning. This kind of planning is assumed as an optimization problem, where latent links will be discovered solving it. It can take advantage of the traffic flow. The time varying traffic dynamics and the current structure of a city can contain valuable information. Formulating the problem to use the information is very critical. After formulation, two LA-based algorithms are presented in this paper to infer optimal structure: an approach based on distributed learning automata (DLA), and another based on cellular learning automata. The DLA-based algorithm solves the problem by a collectively cooperating network of automata. The CLA-based algorithm addresses the problem as a computational task and solves it using a lattice of cells working together. Favorite structure of a city or optimal structure is estimated utilizing two signals from the environment. We need a test bed to evaluate the performance of the algorithms, therefore synthetic and real data are utilized. The CLA-based method outperforms others in most cases comparing the fitness value. The result is an optimal connectivity matrix which contains the current urban structure and some new necessary links.

Keywords

Subjects

  • Ahangaran, M., Taghizadeh, N., & Beigy, H. (2017). Associative cellular learning automata and its applications. Applied Soft Computing, 53, 1-18.

 

  • Angius, F., Reineri, M., Chiasserini, C.-F., Gerla, M., & Pau, G. (2012). Towards a realistic optimization of urban traffic flows. 2012 15th International IEEE Conference on Intelligent Transportation Systems.

 

  • Avila, A., & Mezić, I. (2020). Data-driven analysis and forecasting of highway traffic dynamics. Nature communications, 11(1), 1-16.

 

  • Baskar, B. (2018). Effective Urban Structure Inference from Traffic Flow Dynamics. Software Engineering and Technology, 10(2), 35-38.

 

  • Beigy, H., & Meybodi, M. R. (2004). A mathematical framework for cellular learning automata. Advances in Complex Systems, 7(03n04), 295-319.

 

  • Beigy, H., & Meybodi, M. R. (2008). Asynchronous cellular learning automata. Automatica, 44(5), 1350-1357.

 

  • Black, J. (2018). Urban transport planning: Theory and practice. Routledge.

 

  • Ding, R., Ujang, N., Hamid, H. B., Abd Manan, M. S., Li, R., Albadareen, S. S. M., Nochian, A., & Wu, J. (2019). Application of complex networks theory in urban traffic network researches. Networks and Spatial Economics, 19(4), 1281-1317.

 

  • Ding, R., Yin, J., Dai, P., Jiao, L., Li, R., Li, T., & Wu, J. (2019). Optimal Topology of Multilayer Urban Traffic Networks. Complexity, 2019.

 

  • Djouadi, A., Snorrason, O., & Garber, F. (1990). The quality of training sample estimates of the bhattacharyya coefficient. IEEE Transactions on Pattern analysis and machine intelligence, 12(1), 92-97.

 

  • Kamw, F., Shamal, A.-D., Zhao, Y., Eynon, T., Sheets, D., Yang, J., Ye, X., & Chen, W. (2019). Urban structure accessibility modeling and visualization for joint spatiotemporal constraints. IEEE Transactions on Intelligent Transportation Systems.

 

  • Krugman, P. (1991). Geography and trade MIT Press. Cambridge, MA.

 

  • Long, Y., & Shen, Z. (2015). Geospatial analysis to support urban planning in Beijing. Springer.

 

  • Masoumi, B., & Meybodi, M. R. (2012). Learning automata based multi‐agent system algorithms for finding optimal policies in Markov games. Asian Journal of Control, 14(1), 137-152.

 

  • moghadam, a. (2022). مسئله مسیریابی-موجودی کالاهای فاسدشدنی. civilica. https://civilica.com/doc/1553437/
  • Moosavi, V., & Hovestadt, L. (2013). Modeling urban traffic dynamics in coexistence with urban data streams. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing.

 

  • Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., & Zhang, J. (2019). Urban traffic prediction from spatio-temporal data using deep meta learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

 

  • Poco, J., Doraiswamy, H., Vo, H. T., Comba, J. L., Freire, J., & Silva, C. T. (2015). Exploring Traffic Dynamics in Urban Environments Using Vector‐Valued Functions. Computer Graphics Forum.

 

  • poor arab, m., & pahlevani, p. (2019). مدلسازی تعقیب خودرو با استفاده از روش رگرسیون اسپیلاین تطبیقی چندگانه در حوزه بزرگراهی. فصلنامه مهندسی حمل و نقل, -.

 

  • Sarkar, S., Chawla, S., Ahmad, S., Srivastava, J., Hammady, H., Filali, F., Znaidi, W., & Borge-Holthoefer, J. (2017). Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), 181-193.

 

  • Solé-Ribalta, A., Gómez, S., & Arenas, A. (2016). A model to identify urban traffic congestion hotspots in complex networks. Royal Society open science, 3(10), 160098.

 

  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

 

  • Thathachar, M. A., & Sastry, P. S. (2002). Varieties of learning automata: an overview. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 32(6), 711-722.

 

  • Tonguz, O. K., Viriyasitavat, W., & Bai, F. (2009). Modeling urban traffic: a cellular automata approach. IEEE Communications Magazine, 47(5), 142-150.

 

  • Torkestani, J. A., & Meybodi, M. R. (2011). A cellular learning automata-based algorithm for solving the vertex coloring problem. Expert Systems with Applications, 38(8), 9237-9247.

 

 

  • Wang, P., Fu, Y., Zhang, J., Li, X., & Lin, D. (2018). Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs. ACM Transactions on Intelligent Systems and Technology (TIST), 9(6), 63.

 

  • Yasinian, H., & Esmaeilpour, M. (2021). Distributed learning automata based approach to inferring urban structure via traffic flow. Applied Intelligence, 1-13.

 

  • Zhao, X. (2017). Modeling transportation networks and urban traffic dynamics: A Markovian framework Johns Hopkins University].
Quarterly Journal of Transportation Engineering
Volume 16, Issue 4 - Serial Number 65
Spring 2025
Pages 4797-4814

  • Receive Date 04 February 2023
  • Revise Date 12 January 2025
  • Accept Date 19 February 2025