Significance of Sensor Location in Real-time Traffic State Estimation

Abstract

Short-term congestion caused due to traffic incidents or other road environment factors significantly reduces traffic flow capacity of a link which forms a major part of travel time delays. Accurate and reliable estimate of real-time traffic state is essential for optimizing network performance during unpredictable events. Inaccurate estimate of current traffic state produces unreliable travel-time estimations which lead to ineffective traffic management strategies during traffic incident.This study highlights the accuracy and reliability of traffic state estimate when a traffic flow prediction model is not provided with information about duration and impact of the incident on traffic flow capacity of the link. Cell Transmission Model (CTM) is used for prediction of traffic state and measurements from the sensor are combined in Extended Kalman Filter (EKF) to minimize square of error between predicted and measured traffic state. A simple link is used to highlight the difference between actual traffic state and estimated traffic state using a naive prediction model for real-time traffic state estimation. Analysis of simulation results shows that estimate of traffic state is reliable and accurate for cells upstream of the measurement sensor when incident occurred downstream of measurement sensor. Whereas when incident location is upstream of measurement sensor, the estimated traffic state for downstream cells of measurement sensor is more close to actual traffic condition.