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

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.