Zihan Hong

Online implementation and evaluation of weather-responsive coordinated signal timing operations

This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the systems estimations and future predictions through detectors and roadside sensor coverage.

Data Mining and Pattern Matching for Dynamic Origin–Destination Demand Estimation: Improving Online Network Traffic Prediction

Historical traffic data are widely used in the estimation of origin– destination (O-D) demand patterns in simulation-based dynamic traffic assignment models, in the prediction of traffic states, and as a basis for defining traffic management scenarios. This study investigated the determination of historical traffic patterns by applying a classical clustering algorithm to a very large data set of sensor observations over an extended period and identified appropriate patterns for use in the application of traffic estimation and prediction systems. Systematic identification of similarity and dissimilarity of traffic flow data can lead to a systematic process for defining critical demand scenarios for traffic state prediction. The objective was to explore the impact of various demand scenarios on real-time traffic estimation and prediction. A detailed procedure for clustering traffic flow data that is based on the K-means clustering algorithm is presented. The procedure was applied to a subnetwork in Salt Lake City, Utah. An O-D estimation procedure was applied to each cluster separately; this procedure repeatedly solved a bilevel optimization problem in which the goal was to minimize the deviation from observations and historical demand. For the evaluation of the effect of selecting a best-matching initial matrix on traffic pattern estimation and prediction quality, a comparative application of initial matrix choices was conducted with an online traffic estimation and prediction system. The results indicate that the clustering process results in better starting matrices matched to the days unfolding traffic and weather conditions.

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

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

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.

Recognizing Network Trip Patterns Using a Spatio-Temporal Vehicle Trajectory Clustering Algorithm

This paper presents a spatio-temporal trajectory clustering method for vehicle trajectories in transportation networks to identify heterogeneous trip patterns and explore underlying network assignment mechanisms. The proposed algorithm ST-TOPOSCAN is designed to consider both temporal and spatial information in trajectories. We adopt the time-dependent shortest-path distance measurement and take advantage of topological relations of a predefined network to discover the shared sub-paths among trajectories and construct the clusters. The proposed algorithm is implemented with a trajectory dataset obtained in the Chicago area. The results confirm the method’s ability to extract and generate spatio-temporal (sub-)trajectory clusters and identify trip patterns. Extensive numerical experiments verify the method’s performance and computational efficiency. Through spatio-temporal data mining, this paper contributes to exploring traffic system dynamics and advancing state-of-the-art spatio-temporal clustering for vehicle trajectories.