An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them

Jiao, Shuaiyang; Zhang, Shengrui; Li, Zongzhi; Zhou, Baojian; Zhao, Dan

doi:10.1155/2020/2797420

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Morlock et al [ 14 ] further used the collected traffic data to analyze the probability of stop modes of individual vehicles, and built the acceleration and deceleration models. However, for the individual vehicle, compared with macro traffic flow, the motion states of neighbor vehicles are more likely to affect its driving speed in the actual traffic situation, and the VSP model based on car-following was designed in [ 15 , 16 ]. The speed values of front vehicles were regarded as the future speed trajectory of the self-vehicle.…”

Section: Related Workmentioning

confidence: 99%

A Double-Layer Vehicle Speed Prediction Based on BPNN-LSTM for Off-Road Vehicles

Liu

Liang

Chen

et al. 2023

Sensors

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The accurate prediction of vehicle speed is crucial for the energy management of vehicles. The existing vehicle speed prediction (VSP) methods mainly focus on road vehicles and rarely on off-road vehicles. In this paper, a double-layer VSP method based on backpropagation neural network (BPNN) and long short-term memory (LSTM) for off-road vehicles is proposed. First of all, considering the motion characteristics of off-road vehicles, the VSP problem is established and the relationship between the variables in the problem is carefully analyzed. Then, the double-layer VSP framework is presented, which consists of speed prediction and information update layers. The speed prediction layer established by using LSTM is to predict vehicle speed in the horizon, and the information update layer built by BPNN is to update the prediction information. Finally, with the help of mining truck and loader operation scenarios, the proposed VSP method is compared with the analytical method, BPNN prediction method, and recurrent neural network (RNN) prediction method in terms of speed prediction accuracy. The results show that, under the premise of ensuring the real-time prediction performance, the average prediction error of the proposed BPNN-LSTM prediction method under two operation scenarios reduces by 48.14%, 35.82% and 30.09% compared with the other three methods, respectively. The proposed speed prediction method provides a new solution for predicting the speed of off-road vehicles, effectively improving the speed prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

A Double-Layer Vehicle Speed Prediction Based on BPNN-LSTM for Off-Road Vehicles

Liu

Liang

Chen

et al. 2023

Sensors

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“…According to Tang et al [12], the optimal velocity of the following vehicle is connected to both the precise spacing of the vehicle and the perceived spacing of the vehicle by the driver of the following vehicle. The optimal velocity function (OVF), on which the optimal velocity car-following model is based, has been extensively studied in research in order to determine how the optimal velocity affects car-following behavior [8,10]. Jiao et al [9] introduced an extended car-following model that takes into account the drivers' attributes as part of vehicle-to-vehicle communication.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of the Impact of Rainfall on Driving Vehicles

Pradhan¹,

Shrestha²,

Gurung³

2023

Int. J. of Appl. Math.

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Driving vehicles are impacted significantly by several environmental factors like rainfall, snowfall, storm, etc. Drivers attempt to adapt to the new environment by altering their driving behavior. In this paper, a new model equation of the dynamic equation of traffic flow under the consideration of different optimal velocities by incorporating the impact of rainfall on driving vehicles is proposed. The impact of rainfall is taken as the rainfall-resisting force on the vehicle proportional to the velocity of the driving vehicle. The parabolic impact of rainfall is studied on the driving vehicles that start at the green traffic signal at the intersection of the road. The dynamical behavior such as acceleration, velocity, and spacing between the consecutive vehicles of different car-following models are studied through the numerical simulation using the proposed model.

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“…Jiang et al [14] put forward a full velocity difference model (FVDM) based on the GFM by considering the negative and positive differences. Subsequently, many extended car-following models have been presented by incorporating different factors, such as the preceding vehicle's velocity [15,16], multiple preceding vehicles' velocities [17], the velocity difference [18], the acceleration difference [19], and the historical velocity difference [20].…”

Section: Introductionmentioning

confidence: 99%

An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity

Zhang

Zhou

et al. 2021

Journal of Advanced Transportation

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We investigate the dynamic performance of traffic flow using a modified optimal velocity car-following model. In the car-following scenarios, the following vehicle must continuously adjust the following distance to the preceding vehicle in real time. A new optimal velocity function incorporating the desired safety distance instead of a preset constant is presented first to describe the abovementioned car-following behavior dynamically. The boundary conditions of the new optimal velocity function are theoretically analyzed. Subsequently, we propose an improved car-following model by combining the heterogeneity of driver’s sensitivity based on the new optimal velocity function and previous car-following model. The stability criterion of the improved model is obtained through the linear analysis method. Finally, numerical simulation is performed to explore the effect of the desired safety distance and the heterogeneity of driver’s sensitivity on the traffic flow. Results show that the proposed model has considerable effects on improving traffic stability and suppressing traffic congestion. Furthermore, the proposed model is compatible with the heterogeneity of driver’s sensitivity and can enhance the average velocity of traffic flow compared with the conventional model. In conclusion, the dynamic performance of traffic flow can be improved by considering the desired safety distance and the heterogeneity of driver’s sensitivity in the car-following model.

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An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them

Cited by 7 publications

References 35 publications

A Double-Layer Vehicle Speed Prediction Based on BPNN-LSTM for Off-Road Vehicles

A Double-Layer Vehicle Speed Prediction Based on BPNN-LSTM for Off-Road Vehicles

Modeling and Simulation of the Impact of Rainfall on Driving Vehicles

An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity

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