Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

Chen, Chen; Zhao, Xiaohua; Liu, Hao; Ren, Guichao; Liu, Xiaoming

doi:10.1007/s40534-019-00197-4

Cited by 36 publications

(13 citation statements)

References 14 publications

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“…Driving in heavy rain is also quite dangerous due to reduced visibility of the drivers. The worsening weather conditions, such as heavy rain, etc., have significant effect on increasing drivers' perceived risk due to limited visibility (Chen et al, 2019). This is in line with research related to situation awareness in different road environments (Salmon et al, 2014).…”

Section: Introductionsupporting

confidence: 84%

“…Several situation scenarios that may affect driver's intention to violate traffic signs can also be added as shown in Table 1. Human-related factors can be specified into four aspects, that is, "No Police Officers" (Purnamasari, 2019;Purnamasari & Ansusanto, 2017;Winarso, 2016), "In A Hurry" (Purnamasari, 2019;Purnamasari & Ansusanto, 2017;Winarso, 2016), "Imitate Mistakes" (Purnamasari, 2019;Purnamasari & Ansusanto, 2017;Winarso, 2016) and "Sleep Deprivation & Fatigue" (Ho et al, 2015;Ho & Widaningrum, 2016;Sanjaya et al, 2018;Wijayanto et al, 2018) while environmental-related factors can be specified into 3 aspects, that is, "Quiet Roads" (Salmon et al, 2014), "Night Driving" (Setyaningsih et al, 2018) and "Driving in Heavy Rain" (Chen et al, 2019;Salmon et al, 2014). This study analyzed the influence factors of traffic signs violation based on several situation scenarios by well-educated young drivers in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

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Prohibitory traffic signs violation by well-educated young drivers based on situation scenarios

2021

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Traffic violations in Indonesia were mostly committed by young drivers. Surprisingly, university students ranked third as traffic violators despite they are expected to have better understanding of traffic signs instead of others. This study was conducted to investigate the influence factors that might encourage university students as well-educated young drivers to commit traffic violations through several situation scenarios. Five prohibitory traffic signs (no u-turn, no parking, stop sign, maximum speed limit, no stopping) were subjectively evaluated by 212 university students (18-23 years old, having valid car and or motorcycle driving license) whether they will violate these signs in 7 situation scenarios as indicators based on human-related factors (no police officers, in a hurry, imitate mistakes, sleep deprivation and fatigue) and environmental-related factors (quiet roads, night driving, driving in heavy rain). From the results, the influence varied for each sign. Both factors influenced no u-turn, no parking and no stopping signs. Human-related factors influenced maximum speed limit sign while environmental-related factors influenced stop sign. This study has provided a useful initial insight to understand young driver's behavior to violate these prohibitory traffic signs. For further studies, it can be applied to other signs with different respondent profiles. Apart from that, the relationship between indicators can also be investigated.

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Section: Introductionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Prohibitory traffic signs violation by well-educated young drivers based on situation scenarios

2021

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“…Putting formulas (4) and (5) into formula (3), the state space model of CAVs within the fleet can be written as…”

Section: Model Formulation 221 E State Space Model Of Cavsmentioning

confidence: 99%

“…erefore, the driver tends to continue driving until the following distance is equal to or less than the safety distance. After realizing the crash-prone traffic condition, the driver will operate the vehicle with sequential rushed acceleration or nasty deceleration to keep out of traffic accidents [5]. Furthermore, the researchers found that most drivers prefer to drive at high speeds when they cannot see the vehicle in front.…”

Section: Introductionmentioning

confidence: 99%

Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

Gong

Wei

et al. 2020

Journal of Advanced Transportation

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Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.

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“…Quddus et al proposed a data-driven approach with the help of VISSIM platform to estimate the traffic collision probability [17]. Pan et al extracted vehicle trajectory information by scanning street scenes with the help of Lidar and camera data [18]. Huang et al proposed a force-based heterogeneous traffic simulation model to identify dangerous behaviors of various roadway user, which may trigger traffic accident in real world [19].…”

Section: Introductionmentioning

confidence: 99%

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Sheng¹,

Feng

Biancardo

2022

Journal of Advanced Transportation

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Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.

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Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

Cited by 36 publications

References 14 publications

Prohibitory traffic signs violation by well-educated young drivers based on situation scenarios

Prohibitory traffic signs violation by well-educated young drivers based on situation scenarios

Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

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