Chapter 6. Conclusion and Discussion
The project was designed to evaluate marginal benefit of combining information from connected vehicles (CV) to a legacy system for weather-responsive traffic management (WRTM). The study demonstrated road weather connected vehicle (RWCV) applications in snowplow operations. In the framework, the Snow Command system utilized incoming traffic data from CVs and weather forecasts to prioritize road sections to maximize the benefit of snowplow route operations. A mathematical model and algorithm to find the solution were described and test results presented. Performance of weather-related strategies was quantitatively evaluated with measurements of traffic speed and flow on the network. The performance measures were compared to the results under the two scenarios of (1) doing nothing, and (2) executing a predetermined plan extracted from global positioning system (GPS) data.
When a traffic management system decides to do nothing, the road network under a severe snowstorm becomes impassible; however, with active snowplow operations, the network can serve its demand effectively. For a meaningful comparison to predetermined routes, the optimization problem was constrained with the same conditions as the original routes in terms of vehicle route, vehicle working hours, and depot locations. The comparison results show that snowplow operations can significantly improve by using real-time or predictive road-specific information, possibly collected from CVs, while still using the same amount of resources required by the current weather-response operation scheme.
The simulation results support the potential benefit of two different types of CV technology in WRMS practice. First, the traffic estimation results verify that data from passenger vehicles with connectivity can be a great source of timely disaggregated information on traffic conditions, even with low market penetration rates (MPR). Many American cities have installed roadside detectors and have extended their traffic surveillance systems with emerging technologies. However, the coverage of current implementation is concentrated on highway facilities, rather than arterials that require winter maintenance services as much as highways. By integrating various data sources including private CVs, local transportation agencies may be able to overcome the current issue of limited coverage and to monitor the network traffic states with a high resolution.
Second, local agencies can monitor the current WRMS performance by tracking CVs acting as agents or probes, estimate road surface condition with the executed winter maintenance plan, and generate real-time plans for remaining road sections by using incoming information to maximize the WRMS performance. Current WRMS practices are primarily predetermined on the basis of past traffic patterns and weather observations. The maintenance plans are developed for a few levels of weather severity and, in an inclement weather event, one of them is executed based on a qualitative decision supported by weather forecasts and near real-time road weather observations. By using CV technologies, the ongoing operations can be tightly monitored, assessed, and modified, if better service plans exist for the unfolding road network environment.
The findings from the present study have certain limitations due to assumptions made regarding the diagnosis of road surface condition:
More disaggregated weather information and more sophisticated assessment model of road surface under adverse weather would be necessary for advanced WRMS in practice. This may be a direction for future study. These limitations do not alter the general conclusions about the value of CV data for traffic state prediction and WRMS application. The framework and methodology developed as part of the study could readily accommodate improved relations about snow-pavement interaction as those become calibrated for particular locations.
United States Department of Transportation - Federal Highway Administration