  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).
