Optimal estimates for short horizon travel time prediction in urban areas

TL;DR

This paper develops an optimal method for short-term travel time prediction in urban areas by combining mean and median estimates, validated with real-world GPS data from St. Petersburg.

Contribution

It introduces a novel approach to aggregate segment travel times optimally, improving prediction accuracy for urban route planning.

Findings

Optimal estimate minimizes mean absolute error

Method effectively predicts travel times in real-time

Validated with a year-long GPS dataset from St. Petersburg

Abstract

Increasing popularity of mobile route planning applications based on GPS technology provides opportunities for collecting traffic data in urban environments. One of the main challenges for travel time estimation and prediction in such a setting is how to aggregate data from vehicles that have followed different routes, and predict travel time for other routes of interest. One approach is to predict travel times for route segments, and sum those estimates to obtain a prediction for the whole route. We study how to obtain optimal predictions in this scenario. It appears that the optimal estimate, minimizing the expected mean absolute error, is a combination of the mean and the median travel times on each segment, where the combination function depends on the number of segments in the route of interest. We present a methodology for obtaining such predictions, and demonstrate its effectiveness with a case study using travel time data from a district of St. Petersburg collected over one year. The proposed methodology can be applied for real-time prediction of expected travel times in an urban road network.