Traffic State Estimation and Prediction under Heterogeneous Traffic Conditions

The recent economic growth in developing countries like India has resulted in an intense increase of vehicle ownership and use, as witnessed by severe traffic congestion and bottlenecks during peak hours in most of the metropolitan cities. Intelligent Transportation Systems (ITS) aim to reduce traffic congestion by adopting various strategies such as providing pre-trip and en-route traffic information thereby reducing demand, adaptive signal control for area wide optimization of traffic flow, etc. The successful deployment and the reliability of these systems largely depend on the accurate estimation of the current traffic state and quick and reliable prediction to future time steps. At a macroscopic level, this involves the prediction of fundamental traffic stream parameters which include speed, density and flow in spacetime domain. The complexity of prediction is enhanced by heterogeneous traffic conditions as prevailing in India due to less lane discipline and complex interactions among different vehicle types. Also, there is no exclusive traffic flow model for heterogeneous traffic conditions which can characterize the traffic stream at a macroscopic level. Hence, the present study tries to explore the applicability of an existing macroscopic model, namely the Lighthill-Whitham-Richards (LWR) model, for short term prediction of traffic flow in a busy arterial in the city of Chennai, India, under heterogeneous traffic conditions. Both linear and exponential speed-density relations were considered and incorporated into the macroscopic model. The resulting partial differential equations are solved numerically and the results are found to be encouraging. This model can ultimately be helpful for the implementation of ATIS/ATMS applications under heterogeneous traffic environment.