Effects of advanced warning information systems on secondary crash risk under connected vehicle environment

Son, Seung-oh; Jeong, Jae–Tack; Park, Seongmin; Park, Juneyoung

doi:10.1016/j.aap.2020.105786

Cited by 16 publications

(5 citation statements)

References 39 publications

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“…Zheng et al [17] further supported this by demonstrating that the downstream average speed was the best crash precursor variable across different segment types. The effectiveness of warning information systems in preventing secondary crash risks was shown by Son et al [43], with Jang et al [45] reporting a significant reduction in the crash potential through the provision of warning information. Wu et al [29] emphasized the importance of speed oscillation patterns and driver response time, as prolonged response time due to distractions can increase the crash risk.…”

Section: Research Outcomesmentioning

confidence: 99%

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Safety in Traffic Management Systems: A Comprehensive Survey

Dash

et al. 2023

Designs

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Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

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Section: Research Outcomesmentioning

confidence: 99%

“…It assesses whether the difference observed in sample means is likely to be a true difference in the population or simply due to random variation. Son et al [43] utilized a t-test in a connected vehicle setting to assess the efficacy of an in-vehicle advanced warning information service for mitigating secondary crashes.…”

Section: T-testmentioning

confidence: 99%

Safety in Traffic Management Systems: A Comprehensive Survey

Dash

et al. 2023

Designs

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“…The traffic conflict severity was quantified via TTC and the deceleration rate to avoid the crash (DRAC). Son et al [ 30 ] assessed in-vehicle ASLG in CV environment to prevent SCs. Since the driving behaviors are different in a CV environment, the trajectories were collected from driving simulation experiments and traffic conflict severity was quantified via TTC.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Intelligent transportation systems are constantly being upgraded to make the countermeasures technically feasible. The emergence of connected vehicles (CVs) with advanced speed and lane-changing guidance (ASLG) is considered to be a promising solution for SC mitigation [ 27 , 28 , 29 , 30 ], even if its market penetration is at a low level up to date. Adaptive tunnel lighting control (ATLC) smooths the transition between daylight and tunnel lighting [ 31 , 32 ], such that the driver’s visual oscillation with the vehicle’s speed turbulence can be alleviated.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency

Chen

Song

et al. 2023

IJERPH

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This paper models and mitigates the secondary crash (SC) risk for serial tunnels on the freeway which is incurred by traffic turbulence after primary crash (PC) occurrence and location-heterogeneous lighting conditions along serial tunnels. A traffic conflict approach is developed where SC risk is quantified using a surrogate safety measure based on the simulated vehicle trajectories after PC occurs from a lighting-related microscopic traffic model with inter-lane dependency. Numerical examples are presented to validate the model, illustrate SC risk pattern over time, and evaluate the countermeasures for SC, including adaptive tunnel lighting control (ATLC) and advanced speed and lane-changing guidance (ASLG) for connected vehicles (CVs). The results demonstrate that the tail of the stretching queue on the PC occurrence lane, the adjacent lane of the PC-incurred queue, and areas near tunnel portals are high-risk locations. In serial tunnels, creating a good lighting condition for drivers is more effective than advanced warnings in CVs to mitigate SC risk. Combined ATLC and ASLG is promising since ASLG informs CVs of an immediate response to traffic turbulence on the lane where PC occurs and ATLC alleviates SC risks on adjacent lanes via smoothing the lighting condition variations and reducing inter-lane dependency.

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“…Communication information is provided to the subjects through a human-machine interaction interface display and via voice broadcast to provide driving assistance [34,35]. The relayed information includes the speed of the leading and trailing vehicles in the longitudinal lane, the distance between the vehicles, the vehicle speeds, and the distance from intersections [36,37]. The communication principle involves broadcasting the driving information for the vehicle that is closest to the subject vehicle because the closest vehicle is the most likely vehicle to initiate a dangerous accident.…”

Section: Experimental Scenarios and Assumptionsmentioning

confidence: 99%

Stacking‐based ensemble learning method for cognitive distraction state recognition for drivers in traditional and connected environments

Jin

Hua

Zhang

et al. 2021

IET Intelligent Trans Sys

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Compared with the traditional environment, a connected environment in which multiple types of intelligent vehicles share roads will be more complicated. However, do changes in the traffic environment affect the driver's distraction level and distraction recognition? Relatively few studies have addressed these questions. This study constructs traditional and connected environments and conducts driving simulation experiments. Distracted driving data for 60 drivers were collected in each environment, and the one‐way repeated analysis of variance (ANOVA) method was used to select the distraction indices with significant differences as the input indices. An independent t‐test was applied to analyse changes in the distraction indices in the two environments. The results show that the distraction indices, including the standard deviation (SD) of the horizontal fixation angles, blink frequency, mean pupil diameter, mean saccade speed, SD of the steering wheel angle, mean speed and SD of the lane acceleration, differ in the two environments; therefore, the distraction recognition model in the traditional environment is no longer suitable for connected environments. A distraction state recognition model developed with a stacking‐based ensemble learning algorithm composed of a support vector machine (SVM), extreme gradient boosting (XGBoost), long short‐term memory (LSTM), and bi‐directional LSTM with an attention mechanism (AT‐Bi‐LSTM) was proposed for each environment. The recognition accuracy reaches 95.71% in the traditional environment, and the accuracy rate reaches 97.92% in the connected environment. Compared with traditional algorithms, the proposed approach yields greater recognition accuracy and can be applied in intelligent vehicle distraction early warning assistance systems to improve road safety.

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Effects of advanced warning information systems on secondary crash risk under connected vehicle environment

Cited by 16 publications

References 39 publications

Safety in Traffic Management Systems: A Comprehensive Survey

Safety in Traffic Management Systems: A Comprehensive Survey

Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency

Stacking‐based ensemble learning method for cognitive distraction state recognition for drivers in traditional and connected environments

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