Trajectory Analytics for Traffic Signal System Management in Connected Vehicle Environments

Ostojic, Marija

doi:https://doi.org/10.21985/n2-cpxs-8984

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Trajectory Analytics for Traffic Signal System Management in Connected Vehicle Environments

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With connected vehicle generated (CVG) information, traffic stream parameters become straightforwardly quantifiable, enabling traffic state characterization and examination over a variety of operational conditions. Since the observation is independent of any spatial restrictions and unaffected by queue buildup and discharge, CVG data offer more comprehensive, more reliable inputs to the traffic signal control system. The presented study investigates whether this new information is meaningful and actionable enough to enable advancing traffic operations management and control. This study formulates a conceptual framework for a high definition analysis platform intended to ascertain the responsiveness of trajectory-based measures in reflecting the experienced operational conditions. To this end, this study establishes a quality of service evaluation method at an intersection approach level by introducing a composite, Time-Space Signal Measure of Effectiveness – TSS-MOE - and cross-referencing it with high-resolution (Hi-Res) performance indicators. At the same time, graphical representations of time-space-signal (TSS) signatures aim to characterize the state of the system to identify the underlying cause of any detected disruptions or poor performance level. One of the ways to designing more effective signal control strategies is leveraging and synthesizing connected vehicle generated (CVG) information to identify traffic states for the controller to operate in a predictive, yet vehicle-actuated manner. The contribution of this dissertation is twofold: 1) it presents a framework for an advanced, online, signal control logic in a connected environment that utilizes information from CVs to augment high-resolution controller and/or sensor data, and 2) it applies the trajectory analytics to compare the performance of the new controller schemes with CVG data and functionalities relative to conventional, vehicle-actuated, control. The framework puts forward a predictive control logic that schedules phases in an acyclic manner over a variable planning horizon. Phase duration is continually evaluated in response to updated requests for service distributed among equipped vehicles and associated performance indicators. Within the same connected control setup, two measures of effectiveness of a decision were compared to determine the upper bound on the potential effectiveness of a more-responsive control strategy. Finally, the trajectory analytics was used to evaluate the effectiveness of the CV technology-based control scheme against the conventional one. The manner in which the real-time traffic information collected from external data sources (i.e. CVs) is utilized within the same controller logic, would determine which mode of operation is superior i.e. which of the two objectives should be responsible for signal control parameter optimization. This is why the two controller modes were isolated and their performance compared. The findings indicate that both control system performance assessment and optimization objectives should change with access to CVG data. Unlike the current state of the practice controllers, the developed method is able to handle high and low demand states equally well. The designed connected controller is shown to be robust in handling varying traffic conditions and demand levels.

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