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STIMF: a smart traffic incident management framework

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Abstract

Non-recurrent congestion, which is mainly due to traffic incidents, may seriously impact the performance and operation of a traffic system. Reacting quickly and in a uniform and structured way is vital. In particular, choosing the appropriate response strategy with only a short delay may mitigate the impact of incidents, improve traffic efficiency, and increase safety in the transportation system. This paper proposes STIMF: a smart traffic incident management framework to reduce the burden on traffic incident operators by assisting them in selecting the most appropriate response strategy when an incident occurs. STIMF includes two software systems: (a) a simulation environment used to evaluate traffic incident management strategies and (b) a fuzzy-logic inference system that allows the traffic operator to get prompt recommendations on the best response strategies based on the current context and conditions. Moreover, the STIMF framework also describes the process of preparing and building the simulation environment. To evaluate the proposed framework, we tested it on a section of the Muscat expressway in Oman.

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Acknowledgements

The authors acknowledge the research support provided by IMOB Hasselt University Belgium, the Natural Sciences and Engineering Research Council of Canada (NSERC), Middle East College-Oman, German University of technology in Oman, Directorate General of Traffic, Royal Oman Police (ROP), Supreme Council for Planning, and Muscat Municipality for their support and providing the data that makes this research viable and effective.

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The authors declare that no funds, grants, or other support was received for this work.

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Authors and Affiliations

  1. Transportation Research Institute, Universiteit Hasselt, Hasselt, Belgium

    Siham G. Farrag, Youssef El-Hansali & Ansar Yasar

  2. Computer Science Department, German University of Technology in Oman, Muscat, Oman

    Nabil Sahli

  3. Jodrey School of Computer Science, Acadia University, Wolfville, NS, Canada

    Elhadi M. Shakshuki

  4. College of Engineering and Computer Sciences, Marshall University, Huntington, USA

    Haroon Malik

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  1. Siham G. Farrag
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Correspondence to Siham G. Farrag.

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Farrag, S.G., Sahli, N., El-Hansali, Y. et al. STIMF: a smart traffic incident management framework. J Ambient Intell Human Comput 12, 85–101 (2021). https://doi.org/10.1007/s12652-020-02853-8

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