Data-driven Methods for Characterizing Transportation System Performances Under Congested Conditions: A Phoenix Study

Travel time is the main transportation system performance measure used by the planning community to evaluate the impacts of traffic congestion on infrastructure investment projects and policy development plans. Planners rely on the travel demand model tool estimates for the selection and prioritization of critical and sensitive projects to meet the fiscally constraint requirements imposed by the Federal Highway Administration (FHWA) on their transportation improvement programs (TIP). While travel demand model estimates have been successfully implemented in the evaluation of project scenarios or alternatives, the application of the methods used in the travel demand model to generate these estimates continues to present a critical challenge, particularly to modelers who have to produce a validated model upon which traffic predictions can be made. The various volume-delay functions (VDFs) including the Bureau of Public Roads (BPR) function, used in the travel demand model to relate traffic volume to travel time, are developed based on system-wide attributes. BPR function in its polynomial form is computationally efficient and simple for implementation in a transport planning software. The planning community has long recognized that the BPR function cannot capture traffic flow dynamics and queue evolution processes. Besides, it has difficulties in using the average travel time measure to describe an oversaturated bottleneck with high density but low throughput. This dissertation aims to propose a simplified and yet effective point-queue based modeling approach built on the cumulative vehicle arrival concept, and the polynomial equation formula, based on Newell’s method, to estimate travel time at a corridor level using real-world speed and count measurements. A traffic state estimation (TSE) method is also proposed to characterize data into various states, such as congested state and uncongested state, using Markov Chain to capture current traffic pattern and Bayesian Classifier to infer congestion effects. As the testbed for the case study, the research selects the Phoenix freeway corridor with year-round traffic data collected from embedded traffic loop detectors. The results and effectiveness of the proposed methods are discussed to shed light on the calibration of link performance function, which is an analytical building block for system-wide performance evaluation.