2015 CSCE Annual Conference Regina - Building on our Growth Opportunities Conference
Ms. Xu Wang, University of Alberta
Dr. Zhijun Qiu, University of Alberta
Travel time is a representative index of travel systems. Several models and tools have been used to predict and estimate travel time, but few of them relate current speed and flow data with future travel time prediction. Differently, this paper combines the time template prediction and corridor travel time estimation together, based on the current point-level speed and volume data, to address the gap in the literature. Firstly, to predict the time stamp and speed of vehicles arriving at each loop detector, the METANET macroscopic traffic model is used for traffic state simulation. METANET takes real-time traffic measurements and simulates traffic state variables in a future time period. Then, segment-level travel time equals the travel time that is derived from the predicted speeds at the time of entering the section. From the piece-wise linear speed-based method (PLSB), the method assumes that speeds are changing linearly, and instantaneous travel time can be calculated. Based on the concept of “first in, first out (FIFO),” this departure time would be the starting time stamp of the next section. The speeds of vehicles are not fixed but instead dynamic, as they change from one moment to the next, so the time of sections would vary. When each section’s time boundaries are defined, the corridor-level travel time can be finally formed from adjacent sections by constructing imaginary vehicle trajectories. This paper tests, validates and links two practical models by using real-time loop detector data on Whitemud Drive in Edmonton, Canada.