Robust Route Guidance Model Based on Advanced Traveler Information Systems

Abstract

In this paper a new robust optimization (RO) model is proposed for route guidance based on the advanced traveler information system. The arc travel time is treated as a random variable, and its distribution is estimated from historical data. Traditional stochastic routing models just minimize the expected travel time between the origin and the destination. Such approaches do not account for the fact that travelers often incorporate travel time variability in their decision making. Recently some RO models were proposed to incorporate more statistical information into the models, but these models still ignore much information available from historical travel time data. The probability measurement, time at risk (TaR), is introduced in this paper, and a multiobjective model is built up that allows a trade-off between the expected travel time and the TaR. Thus, the skewness and kurtosis of the arc travel time distribution are taken into consideration; that is important because the travel time distributions of typical arcs show high asymmetry and long tails on the right side as a result of the impact of random incidents and events. This approach is applied in two examples, one of which is a real traffic network.