Predictive Dynamic Speed Limit in a Connected Environment for a Weather Affected Traffic Network: A Case Study of Chicago

Abstract

Dynamic speed limits (DSLs) are used to improve safety and mobility on freeways in unfavorable traffic conditions due to recurring congestion, roadworks, incidents, or adverse weather. The evaluation of in-field deployment reveals that the effectiveness of DSLs can be hampered by low compliance rates or lack of inherent capacity. With the emergence of vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication, it is believed that the operation of DSLs will be able to take advantage of vehicle connectivity. In this paper, the effectiveness of the predictive DSL operation in a connected environment is investigated on the weather affected traffic network of Chicago city under different operational conditions. For the sensitivity test, different market penetration rates of connected vehicles are tested in microsimulation. Microscopic models are used to simulate information exchange by V2V or V2I communication. However, such an application over a large network with mixed traffic can be computationally expensive. A mesoscopic or macroscopic tool is needed that can scale and be computationally economical at the network level. This study integrates the microscopic aspect of V2V communication and the macroscopic for dynamic traffic assignment at a network level. The evaluation of effectiveness at network level is conducted by the Traffic Estimation and Prediction System (TREPS), which is a mesoscopic simulator. The results show, depending on the strategy applied, meaningful increases in both throughput and prevailing speed.