Weather-Responsive Management Strategies for Traffic Management and Winter Road Maintenance Were Assessed on Simulated Wyoming and Chicago Road Networks with Varying Levels of Connected Vehicles.
Implementation of Analysis, Modeling, and Simulation Tools for Road Weather Connected Vehicle Applications Project Report
Weather-responsive traffic management (WRTM) focuses on actionable strategies for system management and operations when weather affects road conditions. Researchers used analysis, modeling and simulation (AMS) tools with connected vehicle (CV) data to assess WRTM strategies in challenging road weather conditions and evaluate the marginal benefit of combining information from connected vehicles to a legacy WRTM system. A simulation analysis of Interstate 80 (I-80) in Wyoming assessed a set of CV applications, including traveler information messages (TIM), variable speed limits (VSL), and snowplow pre-positioning strategies for a 23-mile corridor. A case study using a network of freeways and arterials in the Chicago area evaluated the potential of using CV data for optimizing snowplow routes to reduce impacts on traffic flow.
Three weather-responsive management strategies (WRMS) for I-80 in Wyoming were evaluated using a framework consisting of a simulation platform network module, a simulation manager module, and an application programming interfaces (API) module that determines driver behavior under the CV application scenario. Time-to-collision (TTC) is defined as the expected time for two vehicles to collide if they remain at their present speed and on the same path. In this study, the effectiveness of the CV application was measured by inverse time-to-collision (iTTC), an index of longitudinal collision risk. Large iTTC values indicate large safety risks. In order to assess the sensitivity to how vehicles interact with each other in the simulation, researchers also defined a traffic smoothing rate (TSR) parameter that represents the percentage of non-CVs that can be influenced by CVs have slowed down.
For Chicago case study, the network traffic states were estimated and predicted by processing data from CVs running in the network, drawn from freeways and arterials in the Chicago area. A mesoscopic simulation model used the information to generate snowplow routes to minimize the weather impact on traffic. Three scenarios of snowplow operation under identical weather conditions were evaluated to evaluate the marginal benefit of additional layers of CV data input and route optimization: do nothing, execute predefined snowplow route plan, and execute a snowplow route plan optimized with CV data. Three simulation runs were executed under the same snowstorm scenario emulating November 26, 2018.