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Travel Time Prediction for Traveler Information System in Heterogeneous Disordered Traffic Conditions Using GPS Trajectories

Author

Listed:
  • Gurmesh Sihag

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

  • Manoranjan Parida

    (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)

Abstract

Precise travel time prediction allows travelers and system controllers to be aware of the future conditions on roadways and helps in pre-trip planning and traffic control strategy formulation to lessen the travel time and mitigate traffic congestion problems. This research investigates the possibility of using the GPS trajectory dataset for travel time prediction in Indian traffic conditions having heterogeneous disordered traffic and improvement in prediction accuracy by shifting from the traditional historical average method to modern machine learning algorithms such as linear regressions, decision tree, random forest, and gradient boosting regression. The present study uses massive location data consisting of historical trajectories that were collected by installing GPS devices on the probe vehicles. A 3.6 km long stretch of the Delhi–Noida Direct (DND) flyway is selected as a case study to predict the travel time and compare the performance as well as the efficiency of various travel time prediction algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10070-:d:888111
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    Citations

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    Cited by:

    1. Shukun Lai & Hongke Xu & Yongyu Luo & Fumin Zou & Zerong Hu & Huan Zhong, 2024. "Expressway Vehicle Arrival Time Estimation Algorithm Based on Electronic Toll Collection Data," Sustainability, MDPI, vol. 16(13), pages 1-30, June.
    2. Promporn Sornsoongnern & Suthatip Pueboobpaphan & Rattaphol Pueboobpaphan, 2023. "Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    3. 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.
    4. 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.

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