Simulation Study Estimates That a Fleet of Connected Vehicles Would Improve Safety Benefits from Weather-Responsive Traffic Management by 20 Percent in Severe Weather, As Measured by Inverse Time-to-Collision.
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.
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Implementation of Analysis, Modeling, and Simulation Tools for Road Weather Connected Vehicle Applications Project Report

Summary Information

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.


  • The Wyoming simulations demonstrated that all three CV-based WRMS applications could improve traffic safety performance, although the level of safety benefits significantly depends on the weather scenarios. The effectiveness was most dramatic under severe weather conditions and lowest in clear weather cases. In addition, the results showed that the introduction of CVs to WRMS would maintain at least the same level of mobility efficiency of the system.
  • For the traveler information message application, the simulation results indicated that the time-weighted iTTC of all vehicles decreased by 19.96 percent when the CV penetration rate increased from zero percent to 100 percent on I-80.
  • For I-80 VSL simulation scenarios, at CV market penetration rates below 40 percent, without any smoothing of non-CV flow from the TSR parameter, safety risks as measured by iTTC increased, due to interactions between fast non-CVs and slower CVs. With the introduction of the TSR parameters at 50 and 100 percent, decreases in the total iTTC were found to be 25.37 and 27.24 percent, respectively, as CV penetration rate went from 0 percent to 100 percent. The system mobility efficiency was not compromised when CV VSL was applied as the changes in travel time were limited.
  • The simulation of both the two-point (snowplows start from opposite ends of I-80) and multipoint (snowplows start from four locations) snowplow prepositioning strategies indicated reduced travel time and iTTC for snowy weather scenarios. When the CV penetration rate was set to 20 percent, two-point and multipoint prepositioning strategies decreased the time weighted iTTC of all vehicles by 8.6 and 9.85 percent, respectively. The multipoint prepositioning strategy also led to a decrease in maximum travel time (up to 9.65 percent). The two-point prepositioning strategy had a shorter snowplow operation time (i.e., reduced the operation time by 33.07 percent) as compared to the base case and multipoint prepositioning strategy.
  • In the Chicago simulation, the optimized snowplow route strategy consistently improved both speed and vehicular flow over the two other cases, doing nothing and running a predefined plan. For the freeway network, the optimized snowplow route strategy was able to restore travel speed from approximately 20 miles per hour (mph) to 60 mph and from 50 mph to 75 mph under severe and moderate capacity reduction scenarios, respectively.

Implementation of Analysis, Modeling, and Simulation Tools for Road Weather Connected Vehicle Applications Project Report

Implementation of Analysis, Modeling, and Simulation Tools for Road Weather Connected Vehicle Applications Project Report
Source Publication Date
Ma, Jiaqi; Hani Mahmassani; J. Kyle Garrett; Eunhye Kim; Michelle Neuner
Prepared by Leidos for Federal Highway Administration
Other Reference Number
Report No. FHWA-HOP-20-060
Results Type
Deployment Locations