Skip to main content
SpringerLink
Log in

Influence ranking of road segments in urban road traffic networks

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method.

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

Similar content being viewed by others

Notes

  1. It can be done based on experimental results and inputs from the experienced traffic management people.

  2. Lower than the relatively less significant road segments in the CBD areas.

  3. https://www.vicroads.vic.gov.au.

  4. Performed on a computer with Intel Core i5-4570 CPU 320 GHz, 8GB RAM. Our implementation is done in Java.

References

  1. Ali SS, Anwar T, Rizvi SAM (2020) A revisit to the infection source identification problem under classical graph centrality measures. Online Soc Netw Media 17:100,061

    Article  Google Scholar 

  2. Anwar T, Liu C, Vu HL, Islam MS (2015) Roadrank: traffic diffusion and influence estimation in dynamic urban road networks. In: Proceedings of the CIKM, pp 1671–1674

  3. Anwar T, Liu C, Vu HL, Islam MS (2016) Tracking the evolution of congestion in dynamic road networks. In: Proceedings of the CIKM

  4. Anwar T, Liu C, Vu HL, Islam MS, Sellis T (2018) Capturing the spatiotemporal evolution in road traffic networks. IEEE Trans Knowl Data Eng 30(8):1426–1439

    Article  Google Scholar 

  5. Anwar T, Liu C, Vu HL, Leckie C (2014) Spatial partitioning of large urban road networks. In: EDBT, pp 343–354

  6. Anwar T, Liu C, Vu HL, Leckie C (2017) Partitioning road networks using density peak graphs: efficiency vs. accuracy. Inf Syst 64:22–40

    Article  Google Scholar 

  7. Anwar T, Vu HL, Liu C, Hoogendoorn SP (2016) Temporal tracking of congested partitions in dynamic urban road networks. In: Proceedings of the TRB annual meeting

  8. Bouyahia Z, Haddad H, Jabeur N, Yasar A (2019) A two-stage road traffic congestion prediction and resource dispatching toward a self-organizing traffic control system. Pers Ubiquitous Comput 23:909–920

    Article  Google Scholar 

  9. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117

    Article  Google Scholar 

  10. Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings WWW, pp 721–730

  11. Chin WCB, Wen TH (2015) Geographically modified PageRank algorithms: identifying the spatial concentration of human movement in a geospatial network. PLoS ONE 10(10):e0139509

    Article  Google Scholar 

  12. Comin CH, da Fontoura Costa L (2011) Identifying the starting point of a spreading process in complex networks. Phys Rev E 84:056,105

    Article  Google Scholar 

  13. Fagin R, Kumar R, Sivakumar D (2003) Comparing top k lists. In: Proceedings of the SODA, pp 28–36

  14. Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the WSDM, pp 241–250

  15. Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the WWW, pp 491–501

  16. Herzig J, Mass Y, Roitman H (2014) An author-reader influence model for detecting topic-based influencers in social media. In: Proceedings of the HT, pp 46–55

  17. Hu H, Li G, Bao Z, Cui Y, Feng J (2016) Crowdsourcing-based real-time urban traffic speed estimation: from trends to speeds. In: 32nd IEEE international conference on data engineering, ICDE 2016, Helsinki, Finland, May 16–20, 2016, pp 883–894

  18. Kempe D, Kleinberg J, Tardos E (2005) Influential nodes in a diffusion model for social networks. In: Proceedings of the ICALP, pp 1127–1138

  19. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632

    Article  MathSciNet  Google Scholar 

  20. Lee C, Kim Y, Jin SM, Kim D, Maciejewski R, Ebert D, Ko S (2019) A visual analytics system for exploring, monitoring, and forecasting road traffic congestion. IEEE Trans Vis Comput Graph, pp 1–1. https://doi.org/10.1109/TVCG.2019.2922597

  21. Mitra S, Saraf P, Bhattacharya A (2019) Tips: mining top-k locations to minimize user-inconvenience for trajectory-aware services. IEEE Trans Knowl Data Eng, pp 1–1. https://doi.org/10.1109/TKDE.2019.2935448

  22. Qu L, Li L, Zhang Y, Hu J (2009) Ppca-based missing data imputation for traffic flow volume: a systematical approach. Trans Intell Transp Syst 10(3):512–522

    Article  Google Scholar 

  23. Silva A, Guimarães S, Meira Jr, W, Zaki M (2013) Profilerank: finding relevant content and influential users based on information diffusion. In: Proceedings of the SNAKDD

  24. Song X, Chi Y, Hino K, Tseng B (2007) Identifying opinion leaders in the blogosphere. In: Proceedings of the CIKM, pp 971–974

  25. Xie G, Zhang R, Li Y, Huang L, Wang C, Yang H, Liang J (2020) Attractrank: district attraction ranking analysis based on taxi big data. IEEE Trans Ind Inform, pp 1–1. https://doi.org/10.1109/TII.2020.2994038

  26. Zhang P, Bao Z, Li Y, Li G, Zhang Y, Peng Z (2018) Trajectory-driven influential billboard placement. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, KDD’18, pp 2748–2757

Download references

Acknowledgements

This work was funded by Commonwealth Scientific and Industrial Research Organisation (AU), Australia Research Council (Grant No. Discovery Project DP140103499) and Australian Research Council (Grant No. Discovery Project DP160102412).

Author information

Authors and Affiliations

  1. Macquarie University, Sydney, Australia

    Tarique Anwar

  2. Swinburne University of Technology, Melbourne, Australia

    Chengfei Liu

  3. Monash University, Melbourne, Australia

    Hai L. Vu & Nam Hoang

  4. Griffith University, Brisbane, Australia

    Md. Saiful Islam

  5. Hangzhou Dianzi University, Hangzhou, China

    Dongjin Yu

Authors
  1. Tarique Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengfei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai L. Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Md. Saiful Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongjin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nam Hoang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tarique Anwar.

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

Anwar, T., Liu, C., Vu, H.L. et al. Influence ranking of road segments in urban road traffic networks. Computing 102, 2333–2360 (2020). https://doi.org/10.1007/s00607-020-00839-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-020-00839-0

Keywords

Mathematics Subject Classification

Search

Navigation