Dongyuan Yang

How to Find the Optimal Paths in Stochastic Time-dependent Transportation Networks?

This paper conducted a theoretical study on finding optimal paths in stochastic time-dependent (STD) transportation networks. A stochastic consistent network with STD link travel times was built. The methodology of robust optimization was applied to evaluate the paths for a priori optimization without requiring the probability distribution of travel times. The paths with greatest robustness, namely minimum upper bounds of travel times were defined as the optimal path. Under the stochastic consistent condition, the STD robust optimal path model can be converted into solving a time-dependent shortest path problem in a FIFO network. Then extended Dijkstras algorithm can be applied to solve the simplified STD problem with computation complexity O(n2). In the field experiment, several tests were conducted on finding robust optimal paths in a sampled STD transportation network of Shanghai, China. The numerical results confirmed the validity of the proposed approach and verified that the natural extension of conventional Dijkstras algorithm can solve the STD robust optimal path problem as efficiently as a static network problem.

Urban Freight Management with Stochastic Time-Dependent Travel Times and Application to Large-Scale Transportation Networks

This paper addressed the vehicle routing problem (VRP) in large-scale urban transportation networks with stochastic time-dependent (STD) travel times. The subproblem which is how to find the optimal path connecting any pair of customer nodes in a STD network was solved through a robust approach without requiring the probability distributions of link travel times. Based on that, the proposed STD-VRP model can be converted into solving a normal time-dependent VRP (TD-VRP), and algorithms for such TD-VRPs can also be introduced to obtain the solution. Numerical experiments were conducted to address STD-VRPTW of practical sizes on a real world urban network, demonstrated here on the road network of Shenzhen, China. The stochastic time-dependent link travel times of the network were calibrated by historical floating car data. A route construction algorithm was applied to solve the STD problem in 4 delivery scenarios efficiently. The computational results showed that the proposed STD-VRPTW model can improve the level of customer service by satisfying the time-window constraint under any circumstances. The improvement can be very significant especially for large-scale network delivery tasks with no more increase in cost and environmental impacts.

Using Longitudinal Mobile Phone Data to Understand the Stability of Individual Travel Patterns

Most travel demand models assume that individuals’ daily travel patterns are stable or follow a fixed routine. This hypothesis is being questioned by more and more researchers. In this study, longitudinal mobile phone data were used to study the stability of individual daily travel patterns from three aspects, including activity space, activity points, and daily trip-chain patterns. The activity space was represented by the number of nonhome activity points, the radius of nonhome activity points, and the distance from home. The visitation pattern of activity points was analyzed by entropy and predictability measures. The stability of trip-chain patterns was described by the number of distinct trip chains, the typical trip chain, and the typical trip-chain ratio. Analysis of 21 days of mobile phone data from three communities in Shanghai, China, revealed that individuals’ daily travel patterns showed considerable variation. Although individuals’ visitation patterns to activity points were very regular, the day-to-day variations of individual trip-chain patterns were quite significant. On average, an individual exhibited about eight types of daily trip chains during the 21-day period. The daily travel patterns of residents in the outskirts were more stable than those of residents in the city center. Individuals’ travel patterns on weekdays were more complex than those on weekends. As individuals’ activity spaces increased, the stability of their travel patterns decreased.