RoadRank: Traffic diffusion and influence estimation in dynamic urban road networks

Tarique Anwar, Chengfei Liu, Hai L. Vu, Md Saiful Islam

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

11 Citations (Scopus)


With the rapidly growing population in urban areas, these days the urban road networks are expanding at a faster rate. The frequent movement of people on them leads to traffic congestions. These congestions originate from some crowded road segments, and diffuse towards other parts of the urban road networks 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 work, we propose RoadRank, an algorithm to compute the influence scores of each road segment in an urban road network, and rank them based on their overall influence. It is an incremental algorithm that keeps on updating the influence scores with time, by feeding with the latest traffic data at each time point. The method starts with constructing a directed graph called influence graph, which is then used to iteratively compute the influence scores using probabilistic diffusion theory. We show promising preliminary experimental results on real SCATS traffic data of Melbourne.

Original languageEnglish
Title of host publicationCIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781450337946
Publication statusPublished - 17 Oct 2015
Externally publishedYes
EventACM International Conference on Information and Knowledge Management 2015 - Melbourne, Australia
Duration: 19 Oct 201523 Oct 2015
Conference number: 24th


ConferenceACM International Conference on Information and Knowledge Management 2015
Abbreviated titleCIKM 2015
Internet address


  • Influential roads
  • Road networks
  • Traffic diffusion

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