Optimal estimates for short horizon travel time prediction in urban areas
This provides a method for real-time travel time prediction in urban road networks, but it is incremental as it builds on existing segment-based aggregation approaches.
The paper tackles the problem of predicting travel times in urban areas by aggregating data from vehicles on different routes, finding that the optimal estimate minimizing mean absolute error is a combination of mean and median travel times per segment, with a case study in St. Petersburg showing effectiveness.
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.