Intelligent Trip Modeling for Prediction of Origin-Destination Traveling Speed Profile

Accurate prediction of the traffic information in real time such as flow, density, speed, and travel time has important applications in many areas, including intelligent traffic control systems, optimizing vehicle operations, and the routing selection for individual drivers on the road. This is also a challenging problem due to dynamic changes of traffic states by many uncertain factors along a traveling route. In this paper, we present an Intelligent Trip Modeling System (ITMS) that was developed using machine learning to predict the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The ITMS contains neural networks to predict short-term traffic speed based on the traveling day of the week, the traffic congestion levels at the sensor locations along the route, and the traveling time and distances to reach individual sensor locations. The ITMS was trained and evaluated by using ten months of traffic data provided by the California Freeway Performance Measurement System along a California Interstate I-405 route that is 26 mi long and contains 52 traffic sensors. The ITMS was also evaluated by the traffic data acquired from a 32-mi-long freeway section in the state of Michigan. Experimental results show that the proposed system, i.e., ITMS, has the capability of providing accurate predictions of dynamic traffic changes and traveling speed at the beginning of a trip and can generalize well to prediction of speed profiles on the freeway routes other than the routes the system was trained on.