Estimation of urban route travel time distribution using Markov chains and pair-copula construction

Route travel time information is essential for better travel planning, traffic guidance and congestion avoidance. An available way of representing such information is the estimation of travel time distribution (TTD). However, most methods both in literature and practice prefer to estimate route mean travel times rather than TTDs, especially with less consideration about the spatiotemporal correlations between adjacent links. This study develops a framework of estimating urban route TTD based on Markov chain approach and pair-copula construction with emphasis on capturing the dependence in time and space. The proposed method is validated with Radio Frequency Identification Data collected from an urban arterial in Nanjing, China. The results indicate that the proposed method can dynamically capture the positively correlated, negatively correlated, and uncorrelated relationships between adjacent link travel times. Moreover, the performance of the proposed method produces the least deviation from the route empirical distributions, compared to the considered competing methods.