Optimal estimates for short horizon travel time prediction in urban
  areas
  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.
