Abstract
Heavy rainfall events are becoming more common in many areas with escalating climate change, and these events can considerably affect travel speed and road safety. It is critical to understand when and how rainfall events affect congestion in the transportation network to help improve decision making for infrastructure planning and real-time operations. This study incorporates high-resolution rainfall and wind data into a travel speed prediction model, along with other data related to weather conditions, incidents, and real-time speeds, to assess if localized rainfall data can inform travel speed prediction during light and heavy rainfall events, and how this compares with the classical method of using a single city-wide rain gauge data point. The travel speed prediction model holistically selects the most related features from a high-dimensional feature space by modeling by wind direction, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) to overcome overfitting issues and is applied to two urban arterials for case studies located in Pittsburgh, Pennsylvania. The results indicate that high-resolution rainfall features in many instances are better predictors of future rainfall on the target segments, leading to overall better prediction results (in lag time), when compared with models that use a single city-wide rain gauge. This has implications for other cities that are interested in improving travel speed prediction modeling and traffic modeling under increasing impacts from climate change and extreme weather.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Items include the following:
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Weather data from the Dark Sky API (Dark Sky 2020).
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Rainfall data from the 3RWW API (3 Rivers Wet Weather 2020).
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Incident data from the Pennsylvania DOT Road Conditioning Reporting System (Pennsylvania DOT 2020).
Speed data were provided by a third party, INRIX (INRIX 2020). Direct requests for these materials may be made to the provider.
Acknowledgments
This research was supported by a USDOT University Transportation Center Grant (No. DTRT12GUTC11); by the Provost’s Office and the Civil and Environmental Engineering Department at Carnegie Mellon University, both of which provide funding for the Presidential Postdoctoral Research Program; and by the Hillman Foundation’s support of the Traffic 21 Institute at Carnegie Mellon University. We thank INRIX for the travel speed data used in this paper.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 3 • March 2021
Copyright
© 2021 American Society of Civil Engineers.
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Received: Jun 12, 2020
Accepted: Nov 9, 2020
Published online: Jan 12, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 12, 2021
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