Routing Policy Choice Set Generation in Stochastic Time-Dependent Networks

Transportation systems are inherently uncertain because of random disruptions; nevertheless, real-time information can help travelers make better route choices under such disruptions. The first revealed-preference study of routing policy choice is presented. A “routing policy” is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions to be made on the link next. The policy represents a traveler's ability to incorporate real-time information not yet available at the time of decision. Two case studies are conducted in Stockholm, Sweden, and in Singapore. Data for the underlying stochastic time-dependent network are generated from taxi GPS traces through map-matching and nonparametric link travel time estimation. An efficient algorithm to find the optimal routing policy in large-scale networks is first presented, which is a building block of any routing policy choice set generation method. The routing policy choice sets are then generated by link elimination and simulation. The generated choice sets are first evaluated on the basis of whether they include the observed traces on a specific day, or coverage. The sets are then evaluated on the basis of “adaptiveness,” defined as the capability of a routing policy to be realized as different paths over different days. A combination of link elimination and simulation methods yields satisfactory coverage. The comparison with a path choice set benchmark also suggests that a routing policy choice set could potentially provide better coverage and capture the adaptive nature of route choice.