Real-Time Traffic Prediction and Probing Strategy for Lagrangian Traffic Data

Abstract

The objective of this paper is to present a new analytical tool that predicts highway congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data collection strategy that is adaptable to the quality of traffic information. A stochastic Lagrangian traffic flow model is proposed to capture the transition into traffic jam and randomness in the traffic flow. To calibrate the model, vehicles in a traffic flow are divided into cells, and only the first and last vehicles in each cell are probed. Model parameters and traffic information are updated in real time by the unscented Kalman filter, and an advance warning is provided for stop-and-go traffic jam. Adaptive data collection is done by adjusting the probing cell size based on the variance of the prediction from the stochastic model. By validating the model with empirical highway traffic data, the proposed stochastic model shows a 20% improvement in predicting the one-step-ahead traffic state, comparing it to the result from the deterministic model. The 3-sec prediction of traffic status, which may be applied to compensate for the latency of data processing in real-time applications, can be obtained with a 15% error. The model parameter can be used to warn the drivers 6.76 sec before entering a traffic jam. The scenario with low penetration rate and longer sample time interval is also demonstrated with traffic data collected by smartphones. The results from adaptive probing suggest that it can efficiently use less data to provide higher prediction accuracy than using non-adaptive probing.