Determination method of correction factor and conversion factor for network-connected automatic vehicle capacity

Abstract

The invention relates to a method for determining a capacity correction factor and a conversion coefficient of a networked automatic vehicle, comprising the following steps: S1 sets the traffic flow of a road section to include three categories of human-driven vehicles, single-vehicle intelligent automatic driving vehicles and intelligent networked automatic driving vehicles Motor vehicles; S2 counts the research object road sections to obtain the occupancy rates of HDV, AV and CAV; S3 calculates the expression of the capacity correction factor of AV and CAV; S4 defines the capacity conversion coefficient of AV and CAV; S5 measures to obtain HDV, AV and CAV deceleration, perception reaction time, vehicle speed and vehicle length; S6 calculates HDV, AV and CAV as the expressions of the saturated headway when the rear vehicle follows the preceding vehicle; S7 calculates HDV, AV and CAV as the rear vehicle. The expected value of the saturated headway when the car follows the preceding car; S8 calculates the capacity conversion coefficient of AV and CAV. On the one hand, the present invention considers a variety of automatic driving modes, and proposes the concept of a joint correction factor; on the other hand, the calculation method is simplified, the calculation efficiency is improved, and the engineering application is convenient.

Description

technical field [0001] The invention relates to the technical field of traffic system analysis of road vehicles, in particular to a method for determining a correction factor and a conversion coefficient of a network-connected automatic vehicle traffic capacity in a free-flow scenario. Background technique [0002] Traffic capacity is one of the core concepts of traffic engineering and the basic theory for analyzing traffic systems. With the rapid development of transportation, automobile and other technologies, autonomous vehicles will gradually be mixed into the human driving vehicle (HDV) traffic flow. In particular, autonomous vehicles can be divided into single-vehicle intelligent autonomous vehicles (AV) and intelligent vehicles. There are two categories of Connected Autonomous Vehicles (CAVs). Since highways are free-flowing and the traffic environment is relatively simple compared to urban roads, the promotion of autonomous vehicles at present often starts from high...

AI Technical Summary

Problems solved by technology

However, there is no correction factor that can easily convert the traffic capacity of AV and CAV to HDV, especially there is no conversion factor that can simultaneously consider the mixing of two types of autonomous driving vehicles

Images