Skip to main content
SpringerLink
Log in

Distributed learning automata based approach to inferring urban structure via traffic flow

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Traffic flow can be used as a reference for knowledge generation, which is highly important in urban planning. One of the significant applications of traffic data is decision making about the structure of roads connecting zones of a city. It leads us to an optimal connection between important areas like business centers, shopping malls, construction sites, residential complexes, and other parts of a city which is the motivation of this research. The main question is how to infer the optimal connectivity network considering the current structure of an urban area and time-varying traffic dynamics. Therefore a novel formulation is created in this paper to solve the optimization problem using available data. A proposed algorithm is presented to infer the optimal structure that is a distributed learning automata-based approach. A matrix called estimated optimal connectivity represents the favorite structure and it is optimized utilizing signals about the current system and traffic dynamics from the environment. Two types of data, including synthetic and real-world, are used to show the algorithm’s ability. After many experiments, the algorithm showed capability of optimizing the structure by finding new paths connecting the most correlated areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Alexander L, Jiang S, Murga M, González MC (2015) Origin–destination trips by purpose and time of day inferred from mobile phone data. Transport res part c: emerg technol 58:240–250

    Article  Google Scholar 

  2. Baskar B (2018) Effective urban structure inference from traffic flow dynamics. Softw Eng Technol 10:35–38

    Google Scholar 

  3. Batty M (2013) The new science of cities: Mit Press

  4. Chen Z, Yu B, Song W, Liu H, Wu Q, Shi K, Wu J (2017) A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing. IEEE Trans Geosci Remote Sens 55:6305–6319

    Article  Google Scholar 

  5. Chinchali S, Hu P, Chu T, Sharma M, Bansal M, Misra R et al. (2018) Cellular network traffic scheduling with deep reinforcement learning, in Thirty-Second AAAI Conference on Artificial Intelligence

  6. Crawford F, Watling D, Connors R (2017) A statistical method for estimating predictable differences between daily traffic flow profiles. Transp Res B Methodol 95:196–213

    Article  Google Scholar 

  7. Dance A (2018) Sane in the city: 5 tips to manage the pressures of life in the urban jungle, in USC News, ed. University of Southern California

  8. Djouadi A, Snorrason O, Garber F (1990) The quality of training sample estimates of the bhattacharyya coefficient. IEEE Trans Pattern Anal Mach Intell 12:92–97

    Article  Google Scholar 

  9. Du B, Peng H, Wang S, Bhuiyan MZA, Wang L, Gong Q et al (2019) Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans Intell Transp Syst

  10. Gandia R (2012) City outlines travel diary plan to determine future transportation needs, Calgary Sun

  11. Genders W, Razavi S (2018) Evaluating reinforcement learning state representations for adaptive traffic signal control. Proced Comput Sci 130:26–33

    Article  Google Scholar 

  12. Hanson S, Hanson P (1980) Gender and urban activity patterns in Uppsala, Sweden. Geogr Rev 70:291–299

    Article  Google Scholar 

  13. Hong L, Zheng Y, Yung D, Shang J, Zou L (2015) Detecting urban black holes based on human mobility data, in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems 35

  14. Jiang S, Ferreira Jr J, Gonzalez MC (2012) Discovering urban spatial-temporal structure from human activity patterns, in Proceedings of the ACM SIGKDD international workshop on urban computing, 95–102

  15. Kamw F, Shamal A-D, Zhao Y, Eynon T, Sheets D, Yang J et al (2019) Urban structure accessibility modeling and visualization for joint spatiotemporal constraints. IEEE Trans Intell Transp Syst

  16. Li Z, Liu P, Xu C, Duan H, Wang W (2017) Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks. IEEE Trans Intell Transp Syst 18:3204–3217

    Article  Google Scholar 

  17. Lin Y, Dai X, Li L, Wang F-Y (2018) An efficient deep reinforcement learning model for urban traffic control, arXiv preprint arXiv:1808.01876

  18. Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16:865–873

    Google Scholar 

  19. Manual HC (2010) US Transportation Research Board, 2010

  20. Masoumi B, Meybodi MR (2012) Learning automata based multi-agent system algorithms for finding optimal policies in Markov games. Asian J Control 14:137–152

    Article  MathSciNet  Google Scholar 

  21. Narendra KS, Thathachar MA (2012) Learning automata: an introduction: Courier corporation

  22. Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transport Res Part C: Emerg Technol 79:1–17

    Article  Google Scholar 

  23. 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 Trans Big Data 3:181–193

    Article  Google Scholar 

  24. Schultz L, Sokolov V (2018) Deep Reinforcement Learning for Dynamic Urban Transportation Problems, arXiv preprint arXiv:1806.05310

  25. Sutton RS, Barto AG (2018) Reinforcement learning: An introduction: MIT press

  26. 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 Trans Intell Syst Technol (TIST) 9:63

    Google Scholar 

  27. Wei H, Zheng G, Yao H, Li Z (2018) Intellilight: A reinforcement learning approach for intelligent traffic light control, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2496–2505

  28. Xingyi R, Meina S, Haihong E, Junde S (2016) Textual-geographical-social aware point-of-interest recommendation. J China Univ Posts Telecommun 23:24–67

    Article  Google Scholar 

  29. Yau K-LA, Qadir J, Khoo HL, Ling MH, Komisarczuk P (2017) A survey on reinforcement learning models and algorithms for traffic signal control. ACM Computing Surveys (CSUR) 50:34

    Google Scholar 

  30. Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs, in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 186–194

  31. Yuan C, Yu X, Li D, Xi Y (2018) Overall traffic mode prediction by VOMM approach and AR mining algorithm with large-scale data. IEEE Trans Intell Transp Syst 20:1508–1516

    Article  Google Scholar 

  32. Zheng Y (2013) Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing: ACM

  33. Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol (TIST) 5:38

    Google Scholar 

  34. C. Zhong, X. Huang, S. M. Arisona, and G. Schmitt (2013) Identifying Spatial Structure of Urban Functional Centers Using Travel Survey Data: A Case Study of Singapore, in COMP@ SIGSPATIAL 28–33

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Hamid Yasinian

  2. Computer Engineering Department, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Mansour Esmaeilpour

Authors
  1. Hamid Yasinian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mansour Esmaeilpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mansour Esmaeilpour.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yasinian, H., Esmaeilpour, M. Distributed learning automata based approach to inferring urban structure via traffic flow. Appl Intell 52, 1338–1350 (2022). https://doi.org/10.1007/s10489-021-02465-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02465-5

Keywords

Search

Navigation