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Development of a Machine-Learning-Based Novel Framework for Travel Time Distribution Determination Using Probe Vehicle Data

Author

Listed:
  • Gurmesh Sihag

    (Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India)

  • Praveen Kumar

    (Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India)

  • Manoranjan Parida

    (CSIR-Central Road Research Institute (CRRI), New Delhi 110025, India)

Abstract

Investigating travel time variability is critical for pre-trip planning, reliable route selection, traffic management, and the development of control strategies to mitigate traffic congestion problems cost-effectively. Hence, a large number of studies are available in the literature which determine the most suitable distribution to fit the travel time data, but these studies recommend different distributions for the travel time data, and there is a disagreement on the best distribution option for fitting to the travel time data. The present study proposes a novel framework to determine the best distribution to represent the travel time data obtained from probe vehicles by using the modern machine learning technique. This study employs vast travel time data collected by fitting GPS tracking units on the probe vehicles and offers a comprehensive investigation of travel time distribution in different scenarios generated due to spatiotemporal variation of the travel time. The study also considers the effect of weather and uses the three most commonly used non-parametric goodness-of-fit tests (namely, Kolmogorov–Smirnov test, Anderson–Darling test, and chi-squared test) to fit and rank a comprehensive set of around 60 unimodal statistical distributions. The framework proposed in the study can determine the travel time distribution with 91% accuracy. Additionally, the distribution determined by the framework has an acceptance rate of 98.4%, which is better than the acceptance rates of the distributions recommended in existing studies. Because of its robustness and applicability in many different traffic situations, the proposed framework can also be used in developing countries with heterogeneous disordered traffic conditions to evaluate the road network’s performance in terms of travel time reliability.

Suggested Citation

  • Gurmesh Sihag & Praveen Kumar & Manoranjan Parida, 2023. "Development of a Machine-Learning-Based Novel Framework for Travel Time Distribution Determination Using Probe Vehicle Data," Data, MDPI, vol. 8(3), pages 1-18, March.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:3:p:60-:d:1096345
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    References listed on IDEAS

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    1. 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.
    2. van Loon, Ruben & Rietveld, Piet & Brons, Martijn, 2011. "Travel-time reliability impacts on railway passenger demand: a revealed preference analysis," Journal of Transport Geography, Elsevier, vol. 19(4), pages 917-925.
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    4. Li, Hao & Tu, Huizhao & Hensher, David A., 2016. "Integrating the mean–variance and scheduling approaches to allow for schedule delay and trip time variability under uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 151-163.
    5. Bhat, Chandra R. & Sardesai, Rupali, 2006. "The impact of stop-making and travel time reliability on commute mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 709-730, November.
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