Modeling and simulation of the car-truck heterogeneous traffic flow based on a nonlinear car-following model

Liu, Lan; Zhu, Lei; Yang, Da

doi:10.1016/j.amc.2015.10.032

Cited by 46 publications

(24 citation statements)

References 17 publications

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“…Many scholars have used the simulation technology to study the disturbance effects of large vehicles on traffic flow from the microlevel [14][15][16][17][18][19][20][21][22]. Liu et al [14] proposed a new model, Intelligent Driver Model (IDM), which was based on a nonlinear ordinary differential car-following model. e research shows that the mixture of congested and free-flow regime occurs when the trucks reach their maximum speeds.…”

Section: Introductionmentioning

confidence: 99%

Influence of Large Vehicles on the Speed of Expressway Traffic Flow

Gao

Jia

et al. 2020

Advances in Civil Engineering

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Large vehicles impact the quality of traffic flow. To predict the impact of large-scale vehicles on the average speed of traffic flow, vehicle speeds under different vehicle mixing rates were collected through field observations. A laser roadside traffic survey instrument with automatic vehicle type identification functionality was used to collect cross section traffic flow data. The v/C ratio, large vehicle mixing rate, and average speed of traffic were calculated for each data set. A total of 158 traffic flow data sets were captured and divided into three groups according to the v/C ratio of the expressway. The v/C ratio ranges of the three groups are v/C≤0.35, 0.35<v/C≤0.55, and 0.55<v/C≤0.90. SPSS software was used to analyze the correlation between the vehicle mixing rate and the average speed under different traffic flow conditions, and a model was determined between the average speed of the vehicle flow and the large vehicle mixing rate. Analysis of the results with SPSS revealed a negative logarithmic linear relationship between the average traffic speed and the mixing rate of large vehicles. The results could also be applied to passenger cars. The models are considered as corrections of the average speed of the traffic flow after the mixing of large vehicles. When the mixing rate of large vehicles is close to zero, the forecast value of the model is the average speed of passenger cars. Furthermore, as the traffic volume of the road section increased, the influence of the mixing rate on traffic flow speed became more obvious. The adaptability of the proposed prediction model of the expressway mixing rate was verified by evaluating model predictions against actual measurements.

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Section: Introductionmentioning

confidence: 99%

Influence of Large Vehicles on the Speed of Expressway Traffic Flow

Gao

Jia

et al. 2020

Advances in Civil Engineering

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“…The main studies taking into account truck following behaviour focus on the differences in following reactions [2,3,5,6] depending of the follower and the leader type (passenger car or HDV). Other existing studies which consider truck following behaviour describe the influence of heterogeneous traffic flow on traffic instability [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…To model the heterogeneity of real road traffic, several modified car-following models were previously developed. Previous studies about truck-following rule were based either on specific mode formulation [3,8], on quite complex existing car-following rules [3] or on new developed models based on the local linear mode tree approach, LOLIMOT [10,11]. All of these studies used data from the Next Generation Simulation project.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Gipps’ car‐following model for trucks and the impacts on fuel consumption estimation

Cattin

Leclercq

Pereyron

et al. 2018

IET Intelligent Transport Systems

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“…Sun et al [3] verified that the average speed of a certain number of ahead cars could enhance the stability of traffic flow. Liu et al [4] established a mixed car-truck car-following model based on the road of hybrid vehicles. Saifuzzaman et al [5] investigated the behavior of drivers who use cell phones, which affects the stability of traffic flow.…”

Section: Introductionmentioning

confidence: 99%

The Feedback Control Strategy of the Takagi-Sugeno Fuzzy Car-Following Model with Two Delays

Zhang

Liu

Huang

2016

Journal of Control Science and Engineering

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Considering the driver’s sensing the headway and velocity the different time-varying delays exist, respectively, and the sensitivity of drivers changes with headway and speed. Introducing the fuzzy control theory, a new fuzzy car-following model with two delays is presented, and the feedback control strategy of the new fuzzy car-following model is studied. Based on the Lyapunov function theory and linear matrix inequality (LMI) approach, the sufficient condition that the existence of the fuzzy controller is given making the closed-loop system is asymptotic, stable; namely, traffic congestion phenomenon can effectively be suppressed, and the controller gain matrix can be obtained via solving linear matrix inequality. Finally, the simulation examples verify that the method which suppresses traffic congestion and reduces fuel consumption and exhaust emissions is effective.

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Modeling and simulation of the car-truck heterogeneous traffic flow based on a nonlinear car-following model

Cited by 46 publications

References 17 publications

Influence of Large Vehicles on the Speed of Expressway Traffic Flow

Influence of Large Vehicles on the Speed of Expressway Traffic Flow

Calibration of Gipps’ car‐following model for trucks and the impacts on fuel consumption estimation

The Feedback Control Strategy of the Takagi-Sugeno Fuzzy Car-Following Model with Two Delays

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