Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1092-1099.doi: 10.13229/j.cnki.jdxbgxb20180211

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Car⁃following model with improving safety distance based on optimal velocity model

Zhao⁃wei QU1(),Zhao⁃tian PAN1,Yong⁃heng CHEN1(),Peng⁃fei TAO1,Di SUN2   

  1. 1. College of Transportation, Jilin University, Changchun 130022, China
    2. School of Urban Rail Transit and Logisitic, Beijing Union University, Beijing 100101, China
  • Received:2018-03-09 Online:2019-07-01 Published:2019-07-16
  • Contact: Yong?heng CHEN E-mail:quzw@jlu.edu.cn;cyh@jlu.edu.cn

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Abstract:

In order to overcome the defect of fixed safety distance in Optimal Velocity (OV) model and its related derivative generalized force (GF) model and full velocity difference (FVD) model, a car-following model with improving safety distance based on the OV model is proposed in this paper. The improvements of the proposed model are analyzed theoretically. Then, according to the proposed model, the simulation frameworks are developed to analyze the impact of three model parameters on traffic flow stability. These parameters are the density of traffic flow on the road, the maximum velocity for driving vehicle and the time of driver's reaction delay. The simulation can truly reproduce the propagation state of traffic flow. Finally, using the data from U.S. NGSIM Database as samples, the accuracy and effectiveness of the model presented in this paper are verified after testing with cluster analysis by comparing with OV model, GF model and FVD model.

Key words: engineering of communications and transportation system, car?following model, numerical simulation, traffic flow theory

CLC Number: 

  • U491.25

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Table 1

K?means clustering center of test samples and size of various samples"

类编号 MEAN VAR 类内样本数量
1 19.057 3.4068 1561
2 8.6798 78.062 167
3 8.0471 23.305 381
4 7.353 49.766 284
5 5.0778 2.9554 591

Table 2

Mean value and variance of root mean square error (RMSE) for various samples"

类编号 RMSE OV GF FVD MCF
1 MEAN 31.14 31.60 31.17 18.90
VAR 870.56 895.75 871.19 629.49
2 MEAN 39.64 39.97 39.66 33.45
VAR 2444.55 2479.77 2445.07 2255.03
3 MEAN 28.74 29.22 28.77 20.86
VAR 1418.24 1458.84 1418.55 1309.34
4 MEAN 30.36 30.80 30.39 24.37
VAR 1973.46 2047.67 1973.67 1565.88
5 MEAN 36.64 37.94 36.66 21.47
VAR 2174.57 2258.46 2175.35 1747.29
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