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Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

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  • Mustafa Attallah

    (Department of Civil, Computer and Electrical Engineering, Saint Louis University, St. Louis, MO 63103, USA)

  • Jalil Kianfar

    (Department of Civil, Computer and Electrical Engineering, Saint Louis University, St. Louis, MO 63103, USA)

  • Yadong Wang

    (Electrical and Computer Engineering Department, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA)

Abstract

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models.

Suggested Citation

  • Mustafa Attallah & Jalil Kianfar & Yadong Wang, 2022. "Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14932-:d:969978
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    References listed on IDEAS

    as
    1. Peng Guo & Yanling Sun & Qiyi Chen & Junrong Li & Zifei Liu, 2022. "The Impact of Rainfall on Urban Human Mobility from Taxi GPS Data," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    2. Gurmesh Sihag & Manoranjan Parida & Praveen Kumar, 2022. "Travel Time Prediction for Traveler Information System in Heterogeneous Disordered Traffic Conditions Using GPS Trajectories," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    3. Timothy Otim & Leandro Dörfer & Dina Bousdar Ahmed & Estefania Munoz Diaz, 2022. "Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    4. Hesham Rakha & Mazen Arafeh, 2010. "Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data," Transportation Science, INFORMS, vol. 44(2), pages 151-168, May.
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