Optimal Timing Model for Continuous-Flow Intersection

Continuous flow intersection (CFI) is one of the most important unconventional intersection design (UID) modes. This kind of intersection transfers left-turn movements to secondary intersections to obtain a 2-phase signal control scheme at the main intersection, so the traffic efficiency at the main intersection is greatly improved. To further exploit the traffic efficiency of continuous flow intersection, the coordination of main and secondary intersection signals is theoretically studied, and an optimal timing model for general use is proposed. The optimal timing model can effectively coordinate primary and secondary intersection signal timings and produce the most effective signal timing schemes for main and secondary intersections to great extent reduce vehicle delay and improve intersection capacity, and can ensure reliable operation of primary and secondary intersections in practical application. Based on the delay compositions and their characteristics of CFI, the delay and queue process evolution with the signal offset of main and secondary intersections and the corresponding scene are analysed. Then, the method for solving optimal offset between main and secondary intersections is given by optimal analysis to simplify the solving procedure of the model. In the end, an arterial intersection in Nanchang is studied as a case study of how to apply the CFI channelization design to a conventional intersection and how to apply the optimal timing model to generate the coordinate timing scheme, and the micro traffic simulation is used to evaluate the traffic benefit. The result shows that (1) the optimal timing scheme can effectively reduce delays and queues and improve the intersection capacity; (2) the model calculation agrees well with the simulation result, which proves that the signal timing model is effective and accurate.

Language

  • English

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Filing Info

  • Accession Number: 01695790
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Jan 23 2019 3:01PM