Probabilistic Prediction of Trip Travel Time and Its Variability Using Hierarchical Bayesian Learning
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 2
Abstract
This paper proposes a probabilistic machine learning methodology to predict travel time and its variability for trips between locations in New York City. First, a hierarchical Bayesian generalized linear regression model was trained to estimate predictive distribution of the trip’s unit-distance travel time conditional on trip features. This intermediate step isolates the effect of trip distance to capture the impact of traffic conditions and other covariates on trip travel time. Then, trip travel time and its variability were obtained given the predictive distribution of normalized travel time and the trip’s fastest path distance. Specifically, a Bayesian regression model with varying coefficients was fitted to capture the effect of temporal variations of traffic conditions and spatial effect of geographic regions on model parameters and in turn on trip travel time. The model was trained using New York City ambulance and taxi trip data and its performance was verified with benchmarks methods. Although it shows promising performance, the proposed methodological framework estimates the posterior distributions of the model’s interpretable parameters and eventually estimates the predictive distributions for trip travel times. The estimated distributions are required for uncertainty quantification to make more reliable decisions in traffic management and traffic control strategies and achieve proactive enhancement in the transportation systems. The proposed approach emphasizes the potential use of origin-destination trip data for travel time prediction, quantifying its spatiotemporal variations, and capturing the predictive uncertainties, which are central to operational transportation systems’ design and performance evaluation.
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Data Availability Statement
All models and code used during the study are available from the corresponding author upon reasonable request. Ambulance and DCAS data are restricted and not available for the public. The taxi data are publicly available.
Acknowledgments
The authors acknowledge the Bureau of Management Analysis and Planning at the FDNY and the support from Google and the Tides Foundation under the grant “EMS Resource Deployment Modeling” and the Columbia University Urban Technology Pilot Award.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 2 • June 2023
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© 2023 American Society of Civil Engineers.
History
Received: Aug 12, 2022
Accepted: Dec 4, 2022
Published online: Feb 16, 2023
Published in print: Jun 1, 2023
Discussion open until: Jul 16, 2023
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