Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

摘要

Transportation systems are being reshaped by ride sourcing and shared mobility services in recent years. The transportation network companies (TNCs) have been collecting high-granular ride-sourcing vehicle (RV) trajectory data over the past decade, while it is still unclear how the RV data can improve current dynamic network modeling for network traffic management. This paper proposes to statistically estimate network disequilibrium level (NDL), namely to what extent the dynamic user equilibrium (DUE) conditions are deviated in real-world networks. Using the data based on RV trajectories, we present a novel method to estimate the real-world NDL measure. More importantly, we present a method to compute zone-to-zone travel time data from trajectory-level RV data. This would become a data sharing scheme for TNCs such that, while being used to effectively estimate and reduce NDL, the zone-to-zone data reveals neither personally identifiable information nor trip-level business information if shared with the public. In addition, we present an NDL based traffic management method to perform user optimal routing on a small fraction of vehicles in the network. The NDL measures and NDL-based routing are examined on two real-world large-scale networks: the City of Chengdu with trajectory-level RV data and the City of Pittsburgh with zone-to-zone travel time data. We found that, on weekdays in each city, NDLs are likely high when travel demand is high (thus when congestion is mild or heavy). Generally, a weekend midnight exhibits higher NDLs than a weekday midnight. Many NDL patterns are different between Chengdu and Pittsburgh, which are attributed to unique characteristics of both demand and supply in each city. For instance, NDL in Pittsburgh is much more stable from day to day and from hour to hour, comparing to Chengdu. In addition, we observe that origin-destination pairs with high NDLs are spatially and temporally sparse for both cities. For the Pittsburgh network, we evaluate the effectiveness of NDL-based traffic routing, which shows great potential to reduce total travel time with routing a small fraction of vehicles (1% in the experiments), even using dated NDL that is estimated in the prior hour.