Intelligent Algorithms for Reducing Short-Term Traffic State Prediction Error in Active Traffic Management

Abstract

Macroscopic traffic flow models have been widely used to provide reliable prediction inputs for active transportation and demand management (ATDM). The representative models include METANET, FREFLOW, and KRONOS. In recent development of ATDM, METANET-based prediction models have started to more attention with their convenience of formulating several important macroscopic control variables, such as speed limit and ramp flow. However, applying METANET models on real-world observations (e.g., loop detector data) may experience significant performance issues, especially when applied to a corridor with heterogeneous links. Heterogeneous links refer to adjacent links that have different macroscopic traffic flow characteristics such as free flow speed and capacity. The heterogeneity is usually caused by changes in road geometry and environment, which commonly exist in urban freeway networks. This article provides an in-depth study to investigate the error mechanism of this problem and proposes several effective methods to improve the performance of METANET model over heterogeneous links. It is found in this article that applying some dynamic formulations to the convention term of the speed equation in METANET can significantly reduce the impact of link heterogeneity issue. The proposed method was evaluated using field loop detector data collected at I-894 corridor in Milwaukee, WI.