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Advanced signal control algorithms are anticipated with the increasingavailability of vehicle speed and position data from vehicle-to-infrastructurecommunication and from sensors. This study examines the impact of the sensingrange, meaning the distance from the intersection that such data can beobtained, on the quality of the signal control. Two advanced signal controlmethods, a Self-Organizing Algorithm (SOA) and Phase Allocation Algorithm(PAA), were implemented in simulation and tested to understand the impact ofsensing ranges. SOA is based on fully-actuated control with an added secondaryextension for vehicle platoons along the arterial. PAA uses dynamic programmingto optimize phase sequences and phase duration within a planning horizon. Threedifferent traffic scenarios were developed: symmetric, asymmetric, andbalanced. In general, both algorithms exhibited improvements in performance asthe sensing range was increased. Under the symmetric volume scenario, SOAconverged at 1000 ft and PAA converged at 1500 ft (with vehicles traveling at45mph). Under the asymmetrical and balanced volume scenarios, both algorithmsoutperformed conventional methods. Both algorithms performed better thancoordinated-actuated control if the sensing range is 660 ft or higher. For lowsensing ranges, SOA experiences similar delay compared to fully-actuatedcontrol with advance detector and PAA experienced more delay thancoordinated-actuated control in symmetric and asymmetric scenario but performedbetter for asymmetric scenario. The results suggest that for the SOA algorithm,the sensing range may constrain the maximum allowable secondary extension,hence as the sensing range increases, the vehicular delay would decrease forarterial movements and increase for non-arterial movements. For PAA, arterialand non-arterial delay decreases with increase in sensing range until itconverges.

Impact of Sensing Range on Real-Time Adaptive Control of Signalized Intersections Using Vehicle Trajectory Information