Does connected environment contribute to the driving safety and traffic efficiency improvement in emergency events?

被引:1
作者
Lyu, Nengchao [1 ]
Du, Zijun [1 ]
Hao, Wei [2 ]
机构
[1] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China
[2] Changsha Univ Sci & Technol, Hunan Key Lab Smart Roadway & Cooperat Vehicle Inf, Changsha 410205, Peoples R China
关键词
Connected vehicle environment; Multi-vehicle interaction; Driving safety; Traffic efficiency; Simulated driving; AUTOMATED VEHICLES; TIME; INFORMATION; SITUATIONS; BEHAVIOR; DRIVERS;
D O I
10.1016/j.aap.2024.107810
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
A connected environment is crucial for improving road traffic safety and efficiency. However, it remains unclear how different connected environments affect the interaction between vehicles and their impact on driving safety and traffic efficiency in scenarios with potential risks, such as forced lane changes during emergency events. To investigate the effects of different connected environments on drivers' interaction characteristics and their impact on driving safety and traffic efficiency, a group of simulated driving test was implemented in a multi-agent interactive intelligent connected vehicle driving simulation platform. Four types of connected environments were designed, Non-Connected Vehicles (NCV), Front Vehicle Single-Connected Vehicles (FCV), Rear Vehicle Single-Connected Vehicles (RCV), and Double-Connected Vehicles (DCV). Additionally, four different initial headways were tested (10 m, 20 m, 30 m, and 40 m). 40 drivers were recruited to participate in driving simulation experiments, and simulated driving data were collected. The research results indicate that for the front vehicle (FV), connectivity significantly reduces the collision risk with the accident vehicle (TTCFCV = 4.238 s, TTCDCV = 4.385 s), decreases the maximum longitudinal deceleration of FV (FCV = -1.212 m/s(2), DCV = -1.022 m/s(2)), and reduces the speed fluctuation of FV (FCV = 4.748 km/h, DCV = 3.784 km/h). For the rear vehicle (RV), benefits are observed only in the FCV environment, where connectivity helps reduce the maximum deceleration of RV (FCV = -1.545 m/s(2)), decrease its speed fluctuation (FCV = 3.852 km/h), and enhance overall traffic efficiency (FCV = 12.133 s). Additionally, the minimum time difference to collision (TDTC) in the RCV environment (2.679 s) is significantly higher compared to other connected environments, and the number of cases with TDTC < 1.5 s (49) is notably lower than in other connected environments (NCV = 101, FCV = 107, DCV = 80). This suggests that the RCV environment effectively reduces the lateral collision risk during lane changes. Overall, while single-vehicle connectivity may help reduce driving risks and improve traffic efficiency, DCV may not significantly enhance vehicle safety and traffic efficiency. When the vehicle headway between FV and RV is 20 m (1.651 s), lateral conflicts between the vehicles are most severe. The maximum longitudinal deceleration of FV and RV also significantly decreases with increasing vehicle headway, and when the vehicle headway exceeds 30 m, the maximum longitudinal deceleration of RV nearly ceases to decrease (-1.993 m/s(2) at 30 m, -1.948 m/s(2) at 40 m). As the distance between the front and rear vehicles (DHWFV-RV) increases, the speed of FV becomes more stable, particularly when DHWFV-RV is 40 m (M = 4.204 km/h), where the speed fluctuations of FV are significantly lower compared to other vehicle headways. A 30-meter vehicle headway (M = 5.684 km/h) is more effective in maintaining speed stability for RV. Although travel time increases with the increase in DHWFV-RV, this change does not show a significant difference. Overall, to ensure traffic efficiency, a vehicle headway of 30 m generally satisfies lane-change safety requirements and provides more stable vehicle speed and acceleration.
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页数:15
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共 61 条
  • [21] Körber Moritz, 2015, Dyna rev.fac.nac.minas, V82, P195
  • [22] Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation
    Laureshyn, Aliaksei
    Svensson, Ase
    Hyden, Christer
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2010, 42 (06) : 1637 - 1646
  • [23] Modeling and simulation of vehicle group collaboration behaviors in an on-ramp area with a connected vehicle environment
    Li, Haijian
    Zhang, Junjie
    Li, Yuxuan
    Huang, Zhufei
    Cao, Huiyu
    [J]. SIMULATION MODELLING PRACTICE AND THEORY, 2021, 110
  • [24] Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model
    Liu, Pengfei
    Fan, Wei
    [J]. TRANSPORTATION PLANNING AND TECHNOLOGY, 2020, 43 (03) : 279 - 292
  • [25] Lane Selection Model Based on Phase-Field Coupling and Set Pair Logic
    Liu, Shijie
    Wang, Xiaoyuan
    Liu, Yaqi
    Chen, Longfei
    Li, Hao
    Wang, Gang
    Li, Qingyin
    [J]. JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS, 2022, 148 (11)
  • [26] Multiobjective optimization on cooperative control of autonomous emergency steering and occupant restraint system for enhancing occupant safety
    Liufu, Kangmin
    Liu, Qiang
    Lu, Yu
    Chen, Zeping
    Zhang, Zengbo
    Li, Qing
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2021, 159
  • [27] Multi-Vehicle Interactive Lane-Changing Velocity Change Model Based on Potential Energy Field
    Ma, Yanli
    Yin, Biqing
    Chen, Ke
    Zhang, Peng
    Chan, Ching-yao
    [J]. TRANSPORTATION RESEARCH RECORD, 2022, 2676 (11) : 306 - 323
  • [28] Collision-avoidance lane change control method for enhancing safety for connected vehicle platoon in mixed traffic environment
    Ma, Yitao
    Liu, Qiang
    Fu, Jie
    Liufu, Kangmin
    Li, Qing
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2023, 184
  • [29] UCLF: An Uncertainty-Aware Cooperative Lane-Changing Framework for Connected Autonomous Vehicles in Mixed Traffic
    Mao, Yijun
    Ding, Yan
    Jiao, Chongshan
    Ren, Pengju
    [J]. 2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV, 2023,
  • [30] Impacts of Connected and Automated Vehicles on Road Safety and Efficiency: A Systematic Literature Review
    Matin, Ali
    Dia, Hussein
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (03) : 2705 - 2736