A road network mixed flow forecasting method considering unmanned vehicles
A technology of unmanned vehicles and mixed traffic, applied in the field of transportation, can solve the problems of fluctuations in traffic supply and traffic demand, affecting the stability and reliability of the traffic network, etc.
Active Publication Date: 2021-09-21
SOUTH CHINA UNIV OF TECH
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Problems solved by technology
[0003] Existing studies mainly focus on the attractiveness of unmanned vehicles to travelers and the analysis of the impact on urban traffic congestion, but have not analyzed the dynamic impact of unmanned vehicles on the flow of traditional road networks after the launch of unmanned vehicles. Changes in traffic flow will cause fluctuations in traffic supply and traffic demand, thus affecting the stability and reliability of the entire transportation network
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[0077] Suppose the road network G=(N,A), where N represents the node set, A represents the road segment set, for any road segment a∈A. r and s represent the origin and destination points respectively, and R and S represent the origin and destination point sets respectively, and there is rs∈RS. k represents the path, K represents the path set, there is k∈K. K rs Indicates the set of all paths connecting OD pair rs. This chapter considers the multi-OD case. q rs Indicates the traffic demand between origin and destination within the study period. u and z denote travelers and unmanned vehicles, respectively.
[0078] Such as figure 1 As shown, a road network mixed flow forecasting method considering unmanned vehicles includes the following steps:
[0079] S1: Determination of link-path flow relationship in mixed flow daily variation model
[0080] The traffic in the road network is composed of two parts: unmanned driving traffic and traveler traffic. The evolution of the t...
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The present invention discloses a road network mixed traffic daily change prediction method considering unmanned vehicles, comprising the following steps: S1 determination of road section-path flow relationship of mixed traffic daily change model; S2 path-road section impedance function / travel time determination; S3 Determination of the relationship between the daily change prospect of traveler flow; S4 Daily update method and conditions of traveler flow; S5 Determination of the optimal flow distribution of the daily change model of unmanned driving flow. Establish a mixed traffic daily change model with travelers and unmanned vehicles, and divide the model into two sub-models according to traffic categories, namely, the daily change model of traveler traffic and the daily change model of driverless traffic; the path with the largest foreground value The daily evolution of traveler flow is carried out as the goal; the daily evolution of unmanned driving flow is carried out with the goal of minimizing the path marginal impedance; when the two types of flow evolve to their respective equilibrium states day by day, the system flow is balanced accordingly.
Description
technical field [0001] The invention belongs to the field of transportation, and in particular relates to a method for predicting the daily change of road network mixed flow in consideration of unmanned vehicles. Background technique [0002] With the maturity of unmanned driving technology, unmanned vehicles will become an important way of travel, and combined with the characteristics of unmanned vehicles, it will inevitably have a major impact on the existing urban traffic system, changing the existing travel patterns and traffic flow patterns. Similar to traditional travelers (referring to non-autonomous driving vehicles), unmanned vehicles will gradually adjust their travel choices during day-to-day travel, but unlike traditional travelers, unmanned vehicles do not have the power of human beings. Preference attributes and risk attitudes do not produce subjective perception of path selection, but make path planning based on the results calculated by the computer. Theref...
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IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0129G08G1/065
Inventor 田晟朱泽坤肖佳将冯宇鹏许凯
Owner SOUTH CHINA UNIV OF TECH