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

Cattin, Johana; Leclercq, Ludovic; Pereyron, Florian; Faouzi, Nour‐Eddin El

doi:10.1049/iet-its.2018.5303

Cited by 16 publications

(5 citation statements)

References 25 publications

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“…However, the performance of the previous estimation method is unsatisfactory when describing a vehicle of abnormal type. To address this, Cattin et al [84] adopted energy consumption estimation as one of the optimization objectives and proposed a car-following model calibration method based on a multi-objective particle swarm optimization algorithm. The results suggest that the Gipps model calibrated by this method can accurately estimate the fuel consumption of trucks in the car-following process.…”

Section: Direct Consideration Of Vehicle Type Impactsmentioning

confidence: 99%

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Han

Wang

2022

Sustainability

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Car-following behavior is the result of the interaction of various elements in the specific driver-vehicle-environment aggregation. Under the intelligent and connected condition, the information perception ability of vehicles has been significantly enhanced, and abundant information about the driver-vehicle-environment factors can be obtained and utilized to study car-following behavior. Therefore, it is necessary to comprehensively take into account the driver-vehicle-environment factors when modeling car-following behavior under intelligent and connected conditions. While there are a considerable number of achievements in research on car-following behavior, a car-following model with comprehensive consideration of driver-vehicle-environment factors is still absent. To address this gap, the literature with a focus on car-following behavior research with consideration of the driver, vehicle, or environment were reviewed, the contributions and limitations of the previous studies were analyzed, and the future exploration needs and prospects were discussed in this paper. The results can help understand car-following behavior and the traffic flow characteristics affected by various factors and provide a reference for the development of traffic flow theory towards smart transportation systems and intelligent and connected driving.

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Section: Direct Consideration Of Vehicle Type Impactsmentioning

confidence: 99%

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Han

Wang

2022

Sustainability

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“…Additionally, drivers’ reactions as per existing car‐following models depend on stimuli from the leading vehicle [16, 34, 35]. However, drivers' reactions can be influenced by all the surrounding obstacles.…”

Section: Literature Reviewmentioning

confidence: 99%

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Sharath

Velaga

Quddus

2020

IET intell. transp. syst

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Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego‐vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human‐like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data‐driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two‐dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.

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“…This kind of model is simple in form and can guarantee the safety of the vehicle when it is driving along, so it is adopted by many traffic simulation software. The safety-distance model uses speed to describe the response of the following vehicle to changes in the motion of the vehicle in front, but the simulated traffic volume is much smaller than the actual traffic volume, so it cannot truly describe the phenomenon of the vehicle following on the road [5,6]. The physiological-psychological following model believes that the driver can perceive the change of the relative speed of the two cars in front and behind by the change of the size of the car in front in the field of vision, and judge whether it is close to the car in front according to the preset perception threshold, so as to take appropriate measures to continue driving [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing traffic flow and safety: modeling and simulation analysis of vehicle following in V2V environment

Wang,

Zhang,

Shang

2024

Fourth International Conference on Computer Vision and Information Technology (CVIT 2023)

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With the development of high and new technologies such as 5G communication and unmanned driving, the road traffic environment is also changing quietly. The following model based on traditional traffic parameters can no longer accurately describe the following behavior of vehicles in the intelligent traffic environment. Based on the study of micro-traffic popularity in V2V environment and the reference to the traditional following up model and intelligent driving model, a micro-traffic flow model based on V2V environment is constructed. Based on the control theory, the Laplace transform of the model is carried out to obtain the transfer function under the following model, and the stability of the traffic flow system is studied through the analysis of the transfer function. Analysis results show that the thinking of the leading vehicle and following the speed of the car is poor and car impact on the stability of traffic flow, traffic flow stability critical sensitivity coefficient decreased, stable area, set up by the new model is of high stability, can be more comprehensive description of the V2V environment with chi behavior characteristics of microscopic traffic flow.

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

Cited by 16 publications

References 25 publications

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Enhancing traffic flow and safety: modeling and simulation analysis of vehicle following in V2V environment

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