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Examining the effect of adverse weather on road transportation using weather and traffic sensors

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  • Yichuan Peng
  • Yuming Jiang
  • Jian Lu
  • Yajie Zou

Abstract

Adverse weather related to reduced visibility caused by fog and rain can seriously affect the mobility and safety of drivers. It is meaningful to develop effective intelligent transportation system (ITS) strategies to mitigate the negative effects of these different types of adverse weather related to reduced visibility by investigating the effect of rain and fog on traffic parameters. A number of previous researches focused on analyzing the effect of adverse weather related to reduced visibility by using simulated traffic and weather data. There are few researchers that addressed the impact of adverse weather instances using real-time data. Moreover, this paper conducts comprehensive investigation to clearly compare the changes of driving behavior and traffic parameters in adverse weather including fog and rain using real-time traffic and weather data collected by advanced vehicle-based traffic sensors and weather sensors. After some preliminary analysis, the analysis of variance method (ANOVA) was applied to further compare the significance of effects of these two kinds of adverse weather on traffic parameters. The conditional regression models were employed finally to explore the relationship between these two types of adverse weather and traffic parameters. The results would be beneficial to develop effective intelligent traffic control countermeasures under these different types of adverse weather conditions related to reduced visibility.

Suggested Citation

  • Yichuan Peng & Yuming Jiang & Jian Lu & Yajie Zou, 2018. "Examining the effect of adverse weather on road transportation using weather and traffic sensors," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    • Handle: RePEc:plo:pone00:0205409
    DOI: 10.1371/journal.pone.0205409
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    References listed on IDEAS

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    Cited by:

    1. Sharaf AlKheder & Abdullah AlOmair, 2022. "Urban traffic prediction using metrological data with fuzzy logic, long short-term memory (LSTM), and decision trees (DTs)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1685-1719, March.

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