Active traffic management systems aim to relieve recurring and non-recurring congestion by using estimated and predicted traffic conditions to guide trip makers before and after entering the network as well as to design control strategies to improve system performance. With external disruptions and interventions (e.g., weather, special events, and incidents), from either nature or human beings, the dynamic traffic system has higher stochastic characteristics, a more complicated transition process, and unexpected hysteresis patterns. This study illustrates data-driven approaches to defining and characterizing traffic performance and conditions, addresses algorithmic problems associated with modeling and designing active management strategies, and evaluates and regulates the systematic parameters and sensitivity for predictive traffic management systems. Operational conditions are derived from historical traffic system data to cover a wide range of traffic situations. This characterization and modeling procedure enables the design of strategies to achieve transformative mobility, safety, and environmental benefits in surface transportation systems management, as it accounts for any specific disruptions or interventions that may impact the traffic system. With more accurately defined and clustered operational conditions, traffic management strategies designed to address these conditions are generally expected to be more reliable and robust than those based on aggregated operational conditions. To characterize the traffic conditions and system performance under external interventions, this study examines (1) traffic flow relationships, (2) probability of breakdown and recovery events, and (3) traffic speed profiles and the hysteresis phenomenon. To define representative traffic conditions given historical and probabilistic weather and incident events, this study illustrates the traffic network modeling at the mesoscopic level. It covers the weather affected traffic demand at the origin-destination (zone to zone) level, the traffic incident density in the entire area, and the strategies, which are designed for weather-related traffic conditions and demand management. The active traffic and demand strategies are designed correspondingly to the characterized and extracted conditions and features. The methods are implemented and evaluated in a traffic estimation and prediction system (TrEPS) on real-world networks. To provide more active management strategies, a novel demand management strategy based on the concept of shared mobility is developed by involving user engagement and an available learning database. The ride-sharing solution is proposed specifically for commuters, rooted in the analysis of spatio-temporal patterns manifested through individual trajectories. A case study within the Chicago network is conducted to demonstrate the proposed framework’s ability to support better decision-making for carpool commuters. The results indicate that with ride-matching recommendations using shared vehicle trajectory data, carpool programs for commuters contribute to a less congested traffic state and environment-friendly travel patterns. The study provides an analytics framework and comprehensive procedures to explore the active traffic system and design traffic management strategies. The contributions of this study include (1) introducing the concept, framework, and methodology of performance-driven traffic strategy design and evaluation, (2) building a valuable library of traffic operational conditions to apply to locations where no local data may be available, (3) proposing data-mining algorithms for the vehicle trajectory data to characterize performance and design strategies, and (4) providing a real-world case study for external intervention affected traffic conditions.

Last modified

• 11/19/2019

Creator

• Zihan Hong

DOI

• https://doi.org/10.21985/n2-x18k-mk93

Subject

• Civil and Environmental Engineering

Keyword

• Data Mining
• Performance Evaluation
• Weather-Responsive Traffic Management
• Active Transportation and Demand Management
• Smart Mobility
• Network Modeling

Date created

• 2018-01-01

Resource type

• Dissertation

Rights statement

• In Copyright