Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1085-1093.doi: 10.13229/j.cnki.jdxbgxb.20210843

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Day⁃to⁃day equilibrium of hybrid traffic considering obedience degree under internet of vehicles environment

Yu-lin CHANG1,2(),Wen-qian XU1,Chao SUN1(),Peng ZHANG1   

  1. 1.School of Automobile and Traffic Engineering，Jiangsu University，Zhenjiang 212013，China
    2.Jiangsu Key Laboratory of Intelligent Traffic System，Southeast University，Nanjing 211189，China
  • Received:2021-08-29 Online:2023-04-01 Published:2023-04-20
  • Contact: Chao SUN E-mail:ylchang@ujs.edu.cn;chaosun@ujs.edu.cn

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Abstract:

In order to study the route choice behavior of travelers in the mixed traffic of network connected vehicles and non-connected vehicles， according to whether the travelers are connected vehicle users and the obedience degree to the Internet of Vehicles （IOV） information， the travelers are divided into non-connected vehicle travelers， connected vehicle travelers who fully obey the information and connected vehicle travelers who do not fully obey the information. The three types of travelers update their travel routes according to historical experience information， real-time information of the Internet of Vehicles， and the combination of the first two kinds of information. Futher， a hybrid day-to-day evolution model considering the obedience degree of the IOV information is established by using the traffic network theory. From the example， it is found that the network stability under mixed travel flow is better than that under stochastic user equilibrium （SUE）， and the number of days for travelers to reach a stable perceived travel time under mixed travel flow is shorter than that under stochastic system optimal （SSO） equilibrium. The proportion of information travelers and the obedience degree of the IOV information are the main influencing factors of the evolution of network traffic. The model finally evolves to the mixed equilibrium state of SUE and SSO.

Key words: engineering of communication and transportation, system, day-to-day network traffic, route choice behavior, internet of vehicles, obedience degree

CLC Number: 

  • U491

Fig.1

Day to day model framework"

Fig.2

Test network"

Table 1

Association between routes and links"

路径l路段a集合路径l路段a集合
11，3，5，7，14，23122，8，10，13，21，23
21，3，6，12，14，23132，8，10，13，22，25
31，3，6，13，21，23142，8，11，17，19，21，23
41，3，6，13，22，25152，8，11，17，19，22，25
51，4，10，12，14，23162，8，11，17，20，24，25
61，4，10，13，21，23172，9，15，17，19，21，23
71，4，10，13，22，25182，9，15，17，19，22，25
81，4，11，17，19，21，23192，9，15，17，20，24，25
91，4，11，17，19，22，25202，9，16，18，19，21，23
101，4，11，17，20，24，25212，9，16，18，19，22，25
112，8，10，12，14，23222，9，16，18，20，24，25

Table 2

Impedance function data of links"

路段acafree/minuˉa/（pcu·h-1）路段acafree/minuˉa/（pcu·h-1）
12900144900
21900154300
33500164600
42600172600
52900183900
62900193900
72900205600
84600212900
94800225900
103900235600
112600241600
122600252600
134900

Fig.3

Flow evolution of route 13 under different θ values"

Fig.4

Travel time variation of different types of travelers on route 13 in 100 days under different network equilibrium conditions"

Fig.5

Flow distribution under different networkequilibrium conditions"

Fig.6

Relationship between travel time of three types of travelers on path 13 and α in 100 d"

Fig.7

Relationship between travel time of three types of travelers on path 13 and β in 100 d"

Fig.8

Relationship between mean and mean squaredeviation of route 13 flow and μ,λ in 100 days"

Fig.9

Relationship between the mean and mean squaredeviation of route 13 flow and α,β in 100 days"

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