Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (6): 1973-1983.doi: 10.13229/j.cnki.jdxbgxb.20230981

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Highway infrastructure performance and traffic state prediction on road network

Zhen YANG(),Rui-ping ZHENG,Zhe GONG   

  1. The Key Laboratory of Road and Traffic Engineering,Ministry of Education,Tongji University,Shanghai 201804,China
  • Received:2023-09-13 Online:2025-06-01 Published:2025-07-23

Abstract:

By establishing the prediction model for the highway infrastructure performance that considered the temporal variation of traffic status and the feedback model for traffic status that considered the decay of highway infrastructure performance, the mutual influence between the two was explored. By integrating the expression and storage of road network topology, the coupled simulation model was constructed. Using the variable step-size discrete event simulation method and Matlab Simulink for decoupling on PC platform, the coupled simulation of the highway infrastructure performance and traffic status was implemented. The application example demonstrates that the simulation model can predict highway infrastructure performance well, and the accuracy is higher than that of the traditional prediction without considering the coupling.

Key words: road engineering coupling simulation, highway infrastructure performance, road network traffic state, pavement roughness index

CLC Number: 

  • U491

Fig.1

Dynamic response module of pavement-vehicle suspension systems"

Fig.2

Roughness curve of grade B pavement"

Table 1

Quarter car parameters"

参数数值
簧上质量Ms/kg310
簧下质量Mu/kg35
悬架阻尼系数C/[N·(s·m-1)]1 000
悬架刚度Ks/(N·m-119 500
轮胎刚度Ku/(N·m-1197 000

Fig.3

"SGM7156DAAB" -IRI-aw relation"

Fig.4

Weighted acceleration contour"

Fig.5

Bounded rational traffic assignment results(β=0.7)"

Fig.6

Bounded rational traffic assignment results(β=0.4)"

Fig.7

Bounded rational traffic assignment results considering heterogeneous users (β=[0.6,0.2,0])"

Fig.8

Bounded rational traffic assignment results considering heterogeneous users (β=[0.6,0.6,0.4])"

Fig. 9

Simulation model framework"

Fig.10

Time-stepping logic for discrete simulation systems"

Fig.11

Variable step size solution diagram"

Fig.12

Simplified map of road network"

Fig.13

Comparison of PPCUrelative changes"

Fig.14

Comparison of RQI relative changes between the two predicted results"

Fig.15

RQI relative change comparison between the predicted results and the measured results in 2020"

Fig.16

RQI relative change comparison between the predicted results and the measured results in 2021"

Table 2

Prediction accuracy of road network operational status"

2020年2月RQI2021年2月RQI2020年4月13日PPCU

不考虑耦合

预测精度/%

76.074.077.4

耦合仿真模型

预测精度/%

91.395.392.3
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