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

TL;DR

This paper introduces a novel method to estimate and reduce network disequilibrium levels in real-world transportation networks using ride-sourcing vehicle data, enhancing traffic management and user routing strategies.

Contribution

It presents a new statistical approach to measure network disequilibrium and a zone-to-zone travel time estimation method that preserves privacy, improving dynamic network modeling.

Findings

NDL correlates with high travel demand and congestion.

The proposed methods are validated on large-scale real-world networks.

NDL-based routing improves traffic flow efficiency.

Abstract

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).