State space reduction in modeling traffic network dynamics for dynamic routing under ITS

Effective en route guidance for vehicles can play an important role in alleviating the negative impacts of evergrowing congestion. As network traffic conditions change due to recurrent and non-recurrent congestion, the optimal route can change, and updated directions should be given to the driver in real-time. However, the task of exploiting real-time traffic information for optimal routing is computationally challenging. On the other hand, simplistic schemes (e.g., assuming constant speeds for different network arcs across all hours of the day) lead only to poor travel time performance and driver dissatisfaction. Hence, there is need for compact yet effective representations of traffic network dynamics for supporting routing algorithms. In this paper, we propose two state space reduction approaches employing knowledge discovery and data mining (KDD) methods and mathematical programming (MP) to strike an effective balance between accuracy and state space reduction (i.e., compactness). In doing so, they exploit historical data from ITS systems. We demonstrate the performance of the proposed approaches using actual road network data from Southeast Michigan.