Understanding impacts of aggressive driving on freeway safety and mobility: A multi-agent driving simulation approach

Park, Subin; Oh, Cheol; Kim, Yunjong; Choi, Saerona; Park, Sehhoon

doi:10.1016/j.trf.2019.05.017

Cited by 23 publications

(14 citation statements)

References 15 publications

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“…e result is also consistent with some previous studies [57,58] which argued that aggressive driving had negative impacts on driving safety. It is worth mentioning that the maximum value of IDRAC occurs when the percentage of experienced drivers reaches 60% (see Table 2).…”

Section: Evaluating the Impacts Of Driver-experiencedsupporting

confidence: 92%

Simulation Study of Rear-End Crash Evaluation considering Driver Experience Heterogeneity in the Framework of Three-Phase Traffic Theory

Xiao

et al. 2021

Journal of Advanced Transportation

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Driving safety is considered to have a strong relationship with traffic flow characteristics. However, very few studies have addressed the safety impacts in the three-phase traffic theory that has been demonstrated to be an advancement in explaining the empirical features of traffic flow. Another important issue affecting safety is driver experience heterogeneity, especially in developing countries experiencing a dramatic growth in the number of novice drivers. Thus, the primary objective of the current study is to develop a microsimulation environment for evaluating safety performance considering the presence of novice drivers in the framework of three-phase theory. First, a car-following model is developed by incorporating human physiological factors into the classical Intelligent Driver Model (IDM). Moreover, a surrogate safety measure based on the integration concept is modified to evaluate rear-end crashes in terms of probability and severity simultaneously. Based on a vehicle-mounted experiment, the field data of car-following behavior are collected by dividing the subjects into a novice group and an experienced group. These data are used to calibrate the proposed car-following model to explain driver experience heterogeneity. The results indicate that our simulation environment is capable of reproducing the three-phase theory, and the changes in the modified surrogate safety measure are highly correlated with traffic phases. We also discover that the presence of novice drivers leads to different safety performance outcomes across various traffic phases. The effect of driver experience heterogeneity is found to increase the probability of the rear-end crashes as well as the corresponding severity. The results of this study are expected to provide a scientific understanding of the mechanisms of crash occurrences and to provide application suggestions for improving traffic safety performance.

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Section: Evaluating the Impacts Of Driver-experiencedsupporting

confidence: 92%

Simulation Study of Rear-End Crash Evaluation considering Driver Experience Heterogeneity in the Framework of Three-Phase Traffic Theory

Xiao

et al. 2021

Journal of Advanced Transportation

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“…In real traffic, aggressive driving is usually expressed as tailgating, performing abrupt and risky lane changes, speeding in heavy traffic, not respecting traffic regulations, etc. (Park, Oh, Kim, Choi, & Park, 2019).…”

Section: Discussionmentioning

confidence: 99%

Safety implications of higher levels of automated vehicles: a scoping review

et al. 2021

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Automated vehicles (AVs) promise to improve road safety, reduce traffic congestion and emissions, and enhance mobility. However, evidence regarding their safety benefits has not been systematically investigated and documented. In this study, we utilise a scoping review approach to investigate and synthesise the existing literature on higher levels of AVs' safety implications. This aids future relevant studies by identifying the research gaps and reporting the methodological approaches used. The review focused not only on peer-reviewed articles but also on grey literature to provide a comprehensive overview of the current research state. In total, 5724 articles were identified, and 4167 records were screened after duplicates and dual publications removal, from which 27 were found eligible for review. Ultimately, 24 studies met all the inclusion criteria and were considered for the review. The reported evidence was focused on changes in road safety levels after the deployment of AVs in transport networks. The data was extracted and charted by one reviewer using tables to create a descriptive summary of the results and address the scoping review's questions and objectives. In general, the findings suggest that AVs hold the potential to improve the overall safety on roads, although the existing evidence is not mainly based on real data but assumptions regarding vehicles' capabilities and behaviour. The limited number of studies and the fact that all of them were published or conducted after 2014 indicate that the research on AVs' safety impacts is just emerging.

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“…Lane-changing performance: to avoid aggressive lane-changing maneuvers, which would disturb traffic operation seriously (Park et al , 2019), it is essential to add a penalty discount when lane-changing maneuvers trigger: …”

Section: Problem Formulationmentioning

confidence: 99%

“…where a ( t ) denotes the acceleration in time t , a ( t − 1) is the acceleration in time t – 1, and Δ t indicates the sample interval. Lane-changing performance: to avoid aggressive lane-changing maneuvers, which would disturb traffic operation seriously (Park et al , 2019), it is essential to add a penalty discount when lane-changing maneuvers trigger: …”

Section: Problem Formulationmentioning

confidence: 99%

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Ding

et al. 2022

JICV

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Purpose This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

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Understanding impacts of aggressive driving on freeway safety and mobility: A multi-agent driving simulation approach

Cited by 23 publications

References 15 publications

Simulation Study of Rear-End Crash Evaluation considering Driver Experience Heterogeneity in the Framework of Three-Phase Traffic Theory

Simulation Study of Rear-End Crash Evaluation considering Driver Experience Heterogeneity in the Framework of Three-Phase Traffic Theory

Safety implications of higher levels of automated vehicles: a scoping review

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

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