Analysis of an Intersection Based on Active Priority Strategies

Liu, Zihao; Guo, Xiucheng; Hu, Xiaojian; Wu, Zhongjun

doi:10.1007/978-981-10-3551-7_59

Cited by 1 publication

(2 citation statements)

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“…Different active TSP strategies have a different impact on the TSP implementation efficiency [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Therefore, there are many studies that try to improve the TSP implementation efficiency for active TSP strategy.…”

Section: Introductionmentioning

confidence: 99%

“…With the algorithm improvements, the active TSP can apply for arterial coordination control to meet the traffic demands of buses and general vehicles. Finally, the factors such as detector location, detection time prediction for buses, and mechanisms for providing green extension are considered to improve the active TSP control logic [14,18,28,29]. With the control logic improvements, the execution efficiency of active TSP can be significantly improved.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Transit Signal Priority Strategy for Real-World Signal Controllers that Considers the Number of Bus Arrivals

Lian

et al. 2019

Sustainability

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Active transit signal priority (TSP) is used more conveniently and widely than the other strategies for real-world signal controllers. However, the active TSP strategies of real-world signal controllers use the first-come-first-served rule to respond to any active TSP request and are not effective at responding to the number of bus arrivals. With or without the green extension strategy, the active TSP has little impact on the final green time of priority phase, even in the case where more buses arrive during the priority phase. The reduced green time of early green strategy is relatively large when a bus arrives, and it would be worse when more buses arrive, the active TSP has a big adverse impact on the final green time of the non-priority phase. Therefore, the active TSP strategies of real-world signal controllers cannot handle the downtown intersection where many bus lines converge or where many buses arrive in a signal cycle during the evening rush hour. Traffic engineers need to do much work to optimize the TSP parameters before field application. Consequently, it is necessary to improve the TSP strategy of the real-world signal controllers for the intersections with a lot of bus arrivals. In order to achieve that objective, the authors present the CNOB (cumulative number of buses) TSP strategy based on the Siemens 2070 signal controller. The TSP strategy extends the max call time according to the number of buses in the arrival section when priority phases are active. The TSP strategy truncates the green time according to the number of buses in the storage section when non-priority phases are active. The experiment’s result shows that the CNOB TSP strategy can not only significantly reduce the average delay per person without using TSP optimization but can also reduce the adverse impact on the general vehicles of non-bus-priority approaches for the intersections with a lot of bus arrivals. Additionally, because the system dynamically adjusts, traffic engineers do not need to do much optimization work before the TSP implementation.

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