Determination method of traffic capacity of signal-controlled intersection considering human-machine mixed traffic flow

Abstract

The invention relates to a method for determining the traffic capacity of a signal-controlled intersection considering human-machine mixed-driving traffic flow, comprising the following steps: S1. Set the traffic flow in the intersection to include two types of motor vehicles: human-driven vehicles and intelligent network-connected automatic driving vehicles; S2, obtain the occupancy rate of HDV and CAV; S3, obtain the operating indicators such as the saturation flow rate and green light loss time of HDV and CAV; S4, obtain the operating indicators such as the saturated flow rate in the lane; S5, obtain the human-machine hybrid driving Saturation flow rate and green light loss time of traffic flow at signal-controlled intersection; S6. Obtain the probability distribution function of the traffic flow of signal-controlled intersection considering human-machine mixed traffic flow; S7. Use deterministic expression and probabilistic expression as an expression of this capacity. The present invention takes into account the heterogeneous traffic flow of human-machine mixed driving, can reflect the essence of the traffic capacity of the intersection, and innovatively provides a probabilistic expression of the traffic capacity.

Description

technical field [0001] The present invention relates to the technical field of traffic system analysis of road vehicles, in particular to a method for determining the traffic capacity of a signal-controlled intersection considering human-machine mixed-driving traffic flow. Background technique [0002] Traffic capacity is one of the core concepts of traffic engineering and the theoretical cornerstone of traffic planning, traffic design, and traffic management and control. With the transformation of motor vehicle traffic flow from a homogeneous traffic flow containing only human driving vehicles (HDV) to a heterogeneous traffic flow containing both HDV and Connected Autonomous Vehicle (CAV), the existing 's capacity algorithm has shown its incompatibility. [0003] In the prior art, the lane capacity is based on "homogeneous traffic flow". The "Road Capacity Manual (2016 Edition)" defines capacity as "under certain road geometric conditions and traffic The maximum traffic f...

AI Technical Summary

Problems solved by technology

As the motor vehicle traffic flow changes from a homogeneous traffic flow that only includes human driving vehicles (Human Driving Vehicle, HDV) to a heterogeneous traffic flow that includes both HDV and connected autonomous vehicles (Connected Autonomous Vehicle, CAV), the existing The capacity algorithm has shown its inadaptability

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