Self-adaptive variable lane control method and system for signal control intersection

Abstract

The invention discloses a self-adaptive variable lane control method and system for a signal control intersection. The method comprises the steps: collecting the historical flow data of the intersection, and determining the period of each conversion of a lane function according to the flow change rule of an entrance; taking the traffic load degree index of a current intersection as a basis for lane function conversion; according to the conversion period and the traffic load degree index of the current intersection, judging whether lane conversion is carried out or not; if so, switching a pre-signal, and optimizing the timing of a main signal; otherwise, keeping an original signal scheme; judging whether the optimized signal scheme is superior to the original scheme or not, and if so, changing the current signal scheme into the optimized scheme; otherwise, keeping the original signal scheme. The method can adapt to traffic flows, changing continuously along with time, of all vehicle flows at the intersection, and the phenomenon that a lane in one flow direction is crowded and a lane in the other flow direction is vacant is avoided.

Description

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AI Technical Summary

Benefits of technology

This technology helps optimize signals by adjusting how lanes are used based on changes made during certain times throughout an entire network's operation. It also optimizes utilization of available spaces within this area without causing delays or interruptions from another route.

Problems solved by technology

This patented technical solution described in the previous figure describes how city streets can have issues overcrowding cars or people that could cause accidents during peak hours when they meet again after being delayed from their usual route. Existing methods use fixed lanes (fixed speeds) without considering any specific factors like speed limits or other drivers' preferences. Additionally, these techniques only consider static variables such as vehicle size and distance between them rather than analyzing vehicular dynamics and optimizing signals accordingly.

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