Modeling travel time in urban arterial networks with time-variant turning movements using state-space neural networks.

摘要

Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) have become integral components in congestion mitigation strategies, and are dependent on the ability to reliably estimate and predict travel time. Urban arterial networks are highly complex and dynamic systems for which travel time has been difficult to accurately model. This is in part due to the impact of turning movements at signalized intersections on traffic flow, and thus travel time. State-Space Neural Network (SSNN) models are developed in this thesis to estimate and predict travel time on arterial links and routes within an arterial network. Separate models are developed respective to through, right-turn, and left-turn vehicle movements. The data used to model travel time include variables that are easily collected in the field using existing surveillance infrastructure such as queue length, flow rate, and average speed. The inclusion of variable turning movements in the modeling procedure is observed to have a significant impact on the accuracy of SSNN models developed to estimate and predict travel time, especially for the right-turn movement.;Keywords: Travel Time Prediction, State-Space Neural Networks, Turning Movements, Urban Arterial Networks.