Routing Policy Choice Set Generation in Stochastic Time-Dependent Networks: Case Studies for Stockholm and Singapore

1 Transportation systems are inherently uncertain due to the occurrence of random disruptions; meanwhile, real-time 2 traveler information offers the potential to help travelers make better route choices under such disruptions. This paper 3 presents the first revealed preference (RP) study of routing policy choice where travelers opt for routing policies 4 instead of fixed paths. A routing policy is defined as a decision rule applied at each link that maps possible realized 5 traffic conditions to decisions on the link to take next. It represents a traveler’s ability to look ahead in order to 6 incorporate real-time information not yet available at the time of decision. An efficient algorithm to find the optimal 7 routing policy (ORP) in large-scale networks is presented, as the algorithm is a building block of any routing policy 8 choice set generation method. Two case studies are conducted in Stockholm, Sweden and Singapore, respectively. 9 Data for the underlying stochastic time-dependent network are generated from taxi Global Positioning System (GPS) 10 traces through the methods of map-matching and non-parametric link travel time estimation. The routing policy choice 11 sets are then generated by link elimination and simulation, in which the ORP algorithm is repetitively executed. The 12 generated choice sets are first evaluated based on whether or not they include the observed GPS traces on a specific 13 day, which is defined as coverage. They are then evaluated on the basis of adaptiveness, defined as the capability 14 of a routing policy to be realized as different paths over different days. It is shown that using a combination of link 15 elimination and simulation methods yield satisfactory coverage. The comparison to a path choice set benchmark 16 suggests that a routing policy choice set could potentially provide better coverage and capture the adaptive nature of 17 route choice. The routing policy choice set generation enables the development of a discrete choice model of routing 18 policy choice, which will be explored in the second stage of the study. 19 J. Ding, S. Gao, E. Jenelius, M. Rahmani, H. Huang, L. Ma, F. Pereira, M. E. Ben-Akiva 2