Measuring the Quality of Arterial Traffic Signal Timing – A Trajectory-based Methodology

Evaluating the benefits from traffic signal timing is of increasing interest to transportation policymakers, operators, and the public as integrating performance measurements with agencies’ daily signal timing management has become a top priority. This dissertation presents a trajectory-based methodology for evaluating the quality of arterial signal timing, a critical part of signal operations that promises reduced travel time and fewer vehicle stops along arterials as well as improved travelers’ perception of transportation services. The proposed methodology could significantly contribute to performance-oriented signal timing practices by addressing challenges regarding which performance measures should be selected, how performance measurements can be performed cost-effectively, and how to make performance measures accessible to people with limited knowledge of traffic engineering. A review of the current state of practice and research was conducted first, indicating an urgent research need for developing an arterial-level methodology for signal timing performance assessments as the established techniques are mostly based on by-link or by-movement metrics. The literature review also revealed deficiencies of existing performance measures pertaining to traffic signal timing. Accordingly, travel-run speed and stop characteristics, which can be extracted from vehicle GPS trajectories, were selected to measure the quality of arterial signal timing in this research.Two performance measures were then defined based on speed and stop characteristics: the attainability of ideal progression (AIP) and the attainability of user satisfaction (AUS). In order to determine AIP and AUS, a series of investigations and surveys were conducted to characterize the effects of non-signal-timing-related factors (e.g., arterial congestion level) on average travel speed as well as how stops may affect travelers’ perceived quality of signal timing. AIP was calculated considering the effects of non-signal-timing-related factors, and AUS accounted for the changes in the perceived quality of signal timing due to various stop circumstances.Based upon AIP and AUS, a grade-based performance measurement methodology was developed. The methodology included AIP scoring, AUS scoring, and two scoring adjustments. The two types of scoring adjustments further improved the performance measurement results considering factors such as cross-street delay, pedestrian delays, and arterial geometry. Furthermore, the research outlined the process for implementing the proposed methodology, including the necessary data collection and the preliminary examination of the applicable conditions. Case studies based on real-world signal re-timing projects were presented to demonstrate the effectiveness of the proposed methodology in enhancing agencies’ capabilities of cost-effectively monitoring the quality of arterial signal timing, actively addressing signal timing issues, and reporting the progress and outcomes in a concise and intuitive manner

Integrating Traffic Signal Performance Measures into Agency Business Processes

This report discusses uses of and requirements for performance measures in traffic signal systems facilitated by high-resolution controller event data. Uses of external travel time measurements are also discussed. The discussion is led by a high-level synthesis of the systems engineering concepts for traffic signal control, considering technical and non-technical aspects of the problem. This is followed by a presentation of the requirements for implementing data collection and processing of the data into signal performance measures. The remaining portion of the report uses an example-oriented approach to showing a variety of uses of performance measures for communication and detector system health, quality of local control (including capacity allocation, safety, pedestrian performance, preemption, and advanced control analysis), and quality of progression (including evaluation and optimization)