Intelligent Transportation System Real Time Traffic Speed Prediction with Minimal Data

Purpose: An Intelligent Transportation System (ITS) must be able to predict traffic speed for short time intervals into the future along the branches between the many nodes in a traffic network in near real time using as few observed and stored speed values as possible. Such predictions support timely ITS reactions to changing traffic conditions such as accidents or volume-induced slowdowns and include re-routing advice and time-to-destination estimations. Design/methodology/approach: Traffic sensors are embedded in the interstate highway system in Detroit, Michigan, USA, and metropolitan area. The set of sensors used in this project is along interstate highway 75 (I-75) southbound from the intersection with interstate highway 696 (I-696). Data from the sensors including speed, volume, and percent of sensor occupancy, were supplied in one minute intervals by the Michigan Intelligent Transportation Systems Center (MITSC). Hierarchical linear regression was used to develop a speed prediction model that requires only the current and one previous speed value to predict speed up to 30 minutes in the future. The model was validated by comparison to collected data with the mean relative error and the median error as the primary metrics. Findings and Originality/value: The model was a better predicator of speed than the minute by minute averages alone. The relative error between the observed and predicted values was found to range from 5.9 % for 1 minute into the future predictions to 10.9 % for 30 minutes int