Stochastic Model for Traffic Flow Prediction and Its Validation

This research presents and explores a stochastic model to predict traffic flow along a highway and provides a framework to investigate some potential applications, including dynamic congestion pricing. Traffic congestion in urban areas is posing many challenges, and a traffic flow model that accurately predicts traffic conditions can be useful in re-sponding to them. Traditionally, deterministic partial differential equations, like the LWR model and its extensions, have been used to capture traffic flow conditions. However, a traffic system is subject to many stochastic factors, like erratic driver behavior, weather conditions etc. A deterministic model may fail to capture the many effects of randomness, and thus may not adequately predict the traffic conditions. The model proposed in this research uses a stochastic partial differential equation to describe the evolution of traffic flow on the highway. The model as calibrated and validated by real data shows that it has better predictive power than the deterministic model. Such a model has many uses, including dynamic toll pricing based on congestion, estimating the loss of time due to congestion, etc.