Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

Abstract

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

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

“…The integration of microsimulation and mesosimulation models is presented in Figure 1. This approach has been developed by the authors in an earlier study and has been discussed in detail in previous publications ( 21 , 22 ).…”

“…The other advantage of connectivity, which this study aims to demonstrate, is that a connected traffic stream can utilize the same facility at a higher capacity. In other words, connected vehicles would modify the fundamental diagram of the network by increasing the maximum flow and critical density, especially at lower speed limits ( 21 ). Hence, the same demand within the connected vehicle environment would be served with less severe or even no breakdown compared with the case without connectivity with the assistance of DSL.…”