A Dual Traffic Network Coupled with Improved Delay Models to Estimate Travel Time in Urban Traffic Systems

Travel time estimation plays a core role in intelligent traffic systems, as people or smart vehicles can choose a better travel route in advance, while local authorities can make urban traffic planning based on it. The paper proposes a dual traffic network topology coupled with improved delay models to estimate travel time in urban traffic system. The structure of the system is demonstrated by the dual traffic network (DTN) topology. It hires nodes to representjoint points of links and intersections; it also has two kinds of edges-links and turning cases of intersections. The BPR model is improved by concerning the influence of pedestrians and the lane width, and the HCM model is improved by adding travel time at intersections and considering three different turning cases to match with the DTN topology. The proposed methodology is evaluated by a comparison and an experiment in Changzhou, China. The error is greatly reduced, even though the degree of saturation exceeds 0.72. This proposed estimation methodology can improve the estimation accuracy significantly, especially in high degree of saturation cases.