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Dynamic Trajectory Control and Signal Coordination for a Signalized Arterial with Significant Freight Traffic

Proposal Summary and Objectives

Given the existing infrastructure, the improvement of freight traffic operation can be conducted at the tactical and operational level. In the research of FMRI first-year project, the connected vehicle techniques are assumed so that signal information and estimated queuing information are treated as known inputs for the optimization of individual truck speed profile. Besides, in FMRI first-year project, the optimization is conducted for individual trucks given signal information and estimated queuing information as inputs. This second-year FMRI research focuses on the vehicle dynamics of trucks. In this proposed research, multiple trucks dynamic trajectories and their interactions with the conventional cars will be investigated, and an analytical tool of traffic flow performance will be developed. Based on the analytical models, control strategies are developed to schedule the trajectories of trucks/cars dynamically to improve the mobility of a corridor, assisted by the new coordination strategies of signals.

Two levels of the strategies are defined in the scope of this research: At the first level, the strategy takes into account the vehicle dynamics and optimizes the trajectories of trucks/cars given signal timing plans, signal states and traffic conditions. Real-time decisions and behaviors of vehicle motions and their interactions with other vehicles and with the infrastructure are analyzed. At the second level, the strategies will model the interactions among trucks and conventional vehicles while a new coordination strategy of signals is established, in which the timing variables are used as decision variables. While developing these strategies, the factors of robustness, optimality, predictability will be considered if necessary, and realistic factors such as truck market penetration rate, truck characteristics, speed variability, and signal types will be considered.

Funding Amount: $70,000
Status: Complete
Duration: Dec. 13, 2018 - Dec. 13, 2019