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Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems

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Abstract

The advances in sensor technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks of major cities. In this paper, using two real-world transportation datasets: (1) incident and (2) traffic data, we address the problem of predicting and quantifying the impact of traffic incidents. Traffic incidents include any nonrecurring events on road networks, such as accidents, weather hazard or road construction. By analyzing archived incident data, we classify incidents based on their features (e.g., time, location, type of incident). Subsequently, we model the impact of each incident class on its surrounding traffic by analyzing the archived traffic data at the time and location of the incidents. Consequently, in real-time, if we observe a similar incident (from real-time incident feeds), we predict and quantify its impact on the surrounding traffic using our models. This information, in turn, can help drivers to effectively avoid impacted areas in real-time. To be useful for such real-time navigation application, and unlike current approaches, we study the dynamic behavior of incidents and model the impact as a quantitative time varying spatial span. In addition, we study a novel approach that improves our classification method by analyzing traffic density around the incident area and the initial behavior of the incident. We evaluated our approach with very large traffic and incident datasets collected from Los Angeles County and the results show by utilizing our impact prediction approach in the navigation system, precision of the travel time calculation can be improved by up to 67 %.

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Notes

  1. In this study, we focus on the impact on the upstream direction of incident location for incidents occurred on freeways.

  2. Specifically, we choose the maximum number of clusters while constrain \(s\) to stay in the range (0.5, 0.7], which indicating the reasonable evidence for clustering result.

  3. In most cases, 40 % is large enough to distinguish whether the speed changes is due to noisy sensor data or due to traffic incidents. Thereby, it should be a reasonable choice in the calculation of travel time.

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Acknowledgments

This research has been funded in part by NSF Grant IIS-1115153, a contract with Los Angeles Metropolitan Transportation Authority (LA Metro), the USC Integrated Media Systems Center (IMSC), HP Labs and unrestricted cash gifts from Google, Northrop Grumman, Microsoft and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily react the views of any of the sponsors such as the National Science Foundation or LA Metro.

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Authors and Affiliations

  1. Integrated Media System Center, University of Southern California, Los Angeles, CA, USA

    Bei Pan, Ugur Demiryurek & Cyrus Shahabi

  2. Hitachi Americas R&D, Tarrytown, NY, USA

    Chetan Gupta

  3. Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    Bei Pan

Authors
  1. Bei Pan
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  2. Ugur Demiryurek
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  3. Chetan Gupta
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  4. Cyrus Shahabi
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Correspondence to Bei Pan.

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Pan, B., Demiryurek, U., Gupta, C. et al. Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems. Knowl Inf Syst 45, 75–104 (2015). https://doi.org/10.1007/s10115-014-0783-6

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  • DOI: https://doi.org/10.1007/s10115-014-0783-6

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