DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

Xie, Yizhen; Ni, Qichao; Alfarraj, Osama; Gao, Huafeng; Shen, Guojiang; Kong, Xiangjie; Tolba, Amr

doi:10.1155/2020/8816681

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…The discriminator part compares the real state‐action pairs with the generated one and updates the reward of the CF environment in an iterative way until the maximum reward is achieved. A similar method is used by [105] to develop a CF model which also considers the influence of driving time on driving behavior.…”

Section: Data‐driven Modelsmentioning

confidence: 99%

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

Sadid

Antoniou

2023

IET Intelligent Trans Sys

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Microscopic traffic models (MTMs) are widely used for assessing the impacts of (connected) autonomous vehicles ((C)AVs). These models utilize car-following (CF) and lane-changing models to replicate the (C)AVs driving behaviors. Numerous studies are being lately published regarding the approximation of the driving behaviors of (C)AVs (especially CF behavior) with many state-of-the-art modelling methods. Still, there is no established CF model to mimic the accurate behavior of (C)AVs. Researchers often utilize existing mathematical CF models as well as limited data-driven models for (C)AVs modelling. Meanwhile, several studies conduct simulation-based impact assessments with various key performance indicators (KPIs). Identification of these KPIs is a crucial step for future studies. Hence, this paper presents a comprehensive outlook on different CF models with their adopted parameters for (C)AVs modelling and investigates how and in which aspects might the CF behaviors of (C)AVs are different from human-driven vehicles. In addition, the recent publications in data-driven CF models including their methodologies are explicitly discussed. This work also reviews simulation-based studies with the reported impacts and used KPIs. Finally, in light of the findings of this paper, several future research needs are highlighted.

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Section: Data‐driven Modelsmentioning

confidence: 99%

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

Sadid

Antoniou

2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

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“…The results suggest that the model with consideration of drivers' internal heterogeneity can reproduce the density wave in traffic flow. With consideration of the differences in reaction time of the same driver at different times or under various conditions, Xie et al [58] established a data-driven car-following model based on the Generative Adversarial Network (GAN). The results reveal that this GAN-based model can accurately describe the car-following behavior of the driver under the fatigue state.…”

Section: Internal Heterogeneitymentioning

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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A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system

Yang

Feng

2021

Int J Intell Syst

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This paper investigates the problem of the existing information system security situation assessment models that are less concerned about fuzzy factors and lack of reasoning. First, Pythagorean fuzzy sets are adopted to deal with the ambiguity and uncertainty of the civil aviation airport security inspection information system security situation indicators, and a Pythagorean fuzzy Petri nets (PFPNs) model is established according to the indicator system. Then, based on the characteristics of PFPN, the propositions credibility reasoning algorithm and the security situation fuzzy reasoning algorithm are designed. Finally, the feasibility of the model is verified through the assessment experiment on the security inspection information system of a civil aviation airport. The experimental results show that the PFPN model has better stability and lower algorithm time complexity compared with other models.

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DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

Cited by 5 publications

References 32 publications

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

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

A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system

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