Abstract

Computational resources in the traffic operation field as well as the bandwidth of field communication links, are often quite limited. Accordingly, for real-time implementation of Advanced Transportation Management and Information Systems (ATMIS) strategies, such as vehicle reidentification, there is strong interest in development of field-based techniques and models that can perform satisfactorily while minimizing computational and communication requirements in the field. The ILD (Inductive Loop Detector)-based Vehicle ReIDentification system (ILD-VReID) is an example of a currently applied approach. Although ILDs are not without limitations as a traffic sensor, they are widely used for historical reasons and the sunken investment in the large installed base makes their use in this research highly cost-effective. Therefore, this dissertation develops a new vehicle reidentification algorithm, RTREID-2, for real-time implementation by adopting a PSR (Piecewise Slope Rate) approach that extracts features from raw vehicle signature data. The results of cases studies indicate that RTREID-2 is capable of accurately providing individual vehicle tracking information and performance measurements such as travel time and speed. The potential contributions of RTREID-2 are: application to square and round single loop configurations, and reduced computational requirements because re-estimation or transferability of the speed models used in the previously developed approach is not necessary. As a consequence, RTREID-2 is free of site-specific calibration and transferability issues. A freeway corridor study also demonstrates that RTREID-2 has the potential to be implemented successfully in a congested freeway corridor, utilizing data obtained from both homogenous and heterogeneous loop detection systems. A real-time vehicle classification model, which is based on the PSR approach, was also developed on the part of RTREID-2. The classification model can successfully classify vehicles into 15 classes using single loop detector data without any explicit axle information. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging. To investigate real-time freeway performance measurement in a realworld setting, the design of a RTPMS (Real-time Traffic Performance Measurement System) that is based on RTREID-2 is also presented in this dissertation. A simulation of RTPMS is conducted to evaluate its feasibility. The simulation results demonstrate the potential of implementing RTPMS in real world applications.

Abstract

A big segment of the traffic signal control systems in California and United States are closed-loop systems. Because wide-scale deployment of advanced adaptive control systems may be many years away due to the associated high costs, there is a significant need to improve the effectiveness of the state-of-the-practice closed-loop systems. To address the need, this project focuses on: 1) developing an integrated micro-simulation/signal optimization tool to enhance the capability of generating efficient signal timing plans, and 2) developing a systematic approach to make closed-loop systems be more robust and traffic responsive.

An integrated simulation/signal optimization tool can generate, evaluate and fine tune signal timing plans in a cohesive manner. This report presents an approach for integrating Paramics with Synchro and TRANSYT-7F. Two sets of Paramics plug-ins are developed to facilitate the two-way data conversion between Paramics and Synchro or TRANSYT-7F. The first set of plug-ins read Paramics network data and traffic volume data and generate Synchro or TRANSYT-7F data files while the second transfer optimized signal timing data back to Paramics. These tools may assist traffic engineers in developing efficient signal timing plans for arterial traffic operations. Step-by-step tutorials are also included in the report to teach how to use the new plug-ins.

The advancement and deployment of telecommunication and ITS technologies make traffic and signal status data more readily available. These high-resolution data provide opportunities to allow closed-loop control systems to operate more adaptively and robustly to the changes in traffic demands and patterns. This report presents a systematic approach to make use of traffic and signal data to further improve the control performance of closed-loop systems. The systematic approach includes three components: timing, monitoring, and fine-tuning. For timing, two innovative models are developed to generate robust optimal signal timings that are less sensitive to fluctuations of traffic flows at the same time minimizing the mean of the delays per vehicle across all possible realizations of uncertain traffic flows. Another procedure is proposed to optimally determine time-of-day intervals for time-of-day controls based on a large set of archived traffic data. For monitoring, a prototype signal performance monitoring system is developed to report performance measures of signal control operations and help traffic operation staffs make the decision whether a retiming or fine-tuning effort is needed or not. Finally, for fine-tuning, an offset refiner is introduced to fine tune signal offsets to provide smoother progression in either one-way or two-way coordination. The offset refiner is easy to implement, and could be run periodically or together with the performance monitoring system. If the signal performance degrades, the refiner can be called to fine-tune the offsets for better progression.