Network-Wide Link Travel Time Inference Using Trip-Based Data From Automatic Vehicle Identification Detectors

Abstract

With the continuous expansion of transportation networks in the urbanization process, monitoring network-wide traffic dynamics with the link resolution has become a significant challenge. Moreover, applying the traditional link-based data into traffic states identification over large-scale networks requires expensive costs. Fortunately, nowadays, we can collect large amounts of trip-based data which makes it possible to tackle this problem. In this paper, we propose a probabilistic framework to infer network-wide link travel time with trip-based data from automatic vehicle identification (AVI) detectors. Different from the link-based data, the AVI data contains multiple attributes, such as trip origin, destination, time stamps, and vehicle license plate. Thus, it is more possible to infer individual traces at a network-wide scale with the AVI detectors. In the proposed framework, we first develop the probabilistic trip travel time allocation (PTTA) model to assign the trip travel time of each vehicle into its traversed links. Especially, the combination impacts of signal control and traffic flow on link travel time are introduced in this model for each allocation. Then, we extract available allocations covered by divided intervals for real-time estimation of network-wide average link travel time. A real-world data set was collected from Xuancheng, Anhui, China. The empirical results show that the performance of PTTA model has a superior level on allocation accuracy over similar methods, and the proposed framework is proved to be reliable at a network-wide scale when the AVI detection sparsity of network is within a certain range.