An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers

The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc....

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Main Authors: Shenzhen Ding, Xumei Chen, Zexin Fu, Fei Peng
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2021/2739129
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author Shenzhen Ding
Xumei Chen
Zexin Fu
Fei Peng
author_facet Shenzhen Ding
Xumei Chen
Zexin Fu
Fei Peng
author_sort Shenzhen Ding
collection DOAJ
description The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.
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institution Kabale University
issn 0197-6729
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language English
publishDate 2021-01-01
publisher Wiley
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series Journal of Advanced Transportation
spelling doaj-art-db4b046b6d5343aabfcf71885129b97e2025-02-03T01:04:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/27391292739129An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between DriversShenzhen Ding0Xumei Chen1Zexin Fu2Fei Peng3Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, ChinaThe development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.http://dx.doi.org/10.1155/2021/2739129
spellingShingle Shenzhen Ding
Xumei Chen
Zexin Fu
Fei Peng
An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers
Journal of Advanced Transportation
title An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers
title_full An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers
title_fullStr An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers
title_full_unstemmed An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers
title_short An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers
title_sort extended car following model in connected and autonomous vehicle environment perspective from the cooperation between drivers
url http://dx.doi.org/10.1155/2021/2739129
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