Analysis of impact of elderly drivers on traffic safety using ANN based car-following model

Jian, Meiying; Shi, Jing

doi:10.1016/j.ssci.2019.104536

Cited by 21 publications

(10 citation statements)

References 30 publications

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“…The results showed that the models which were developed for each group separated had slightly better modeling performance. These models were primarily used to quantify the impact of elderly drivers on traffic safety (Jian and Shi 2020). On the other hand, Huang et al (Huang et al 2018) investigated the variability in the human driving behavior by investigating the asymmetric driving behavior for hysteresis, discrete driving (i.e., acceleration and deceleration actions are not completely continuous), and intensity difference (i.e., the magnitudes of acceleration and deceleration are different at the same following distance, speed of the following vehicle, and same relative speed magnitude).…”

Section: R a F Tmentioning

confidence: 99%

“…Moreover, several carfollowing models were developed to mimic the safe human driving behavior in autonomous vehicles environment (Zhu et al 2018;Fu et al 2019;Yang et al 2019). Also, car-following models were developed for different drivers (i.e., elderly and young drivers) (Jian and Shi 2020) and to model the variability in driver behavior (Huang et al 2018). Despite these huge efforts, the developed car-following models did not consider the change in the behavior due to the proximity of intersections, especially in the AV environment.…”

Section: R a F Tmentioning

confidence: 99%

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Human-like speed modeling for autonomous vehicles during car-following at intersections

Tawfeek

2022

Can. J. Civ. Eng.

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This study aims at modelling drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for Autonomous Vehicles (AV). Several car-following behavioral measures at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) were extracted to train the model using three machine learning techniques. The results showed that the XGBoost model is superior when compared to other techniques with R-squared values of 0.99 and 0.97 for training and testing datasets. The results also indicated that the control type impact driver speed at the minimum following distance. The modelled speed will provide more comfortable speed to the AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and its control type.

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Section: R a F Tmentioning

confidence: 99%

Section: R a F Tmentioning

confidence: 99%

Human-like speed modeling for autonomous vehicles during car-following at intersections

Tawfeek

2022

Can. J. Civ. Eng.

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“…Unlike those external conditions, the human factors are believed to be the most influential yet complex factors in the occurrence of crashes. Usually, the sociodemographic characteristics of drivers, such as age, gender, driving experience, and driving style, are investigated to reveal their association with the occurrence and/or likelihood of crashes in a macroscopic manner [40,41]. Besides, there is also wide agreement that the microscopic behavioral aspects of drivers including cognitive psychological behavior [14,21,24,30,[42][43][44] and driving behaviors [26] are critical influential human factors in crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Ding

Jiao

2021

Discrete Dynamics in Nature and Society

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Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

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“…Many researchers have investigated the influencing factors of crashes in the elderly (Al AlKheder, AlRukaibi et al, 2020;Amiri, Sadri et al, 2020;Casado-Sanz, Guirao et al, 2019;Haule, Sando et al, 2019;Noh, Kim et al, 2018;Pan, Wu et al, 2022;Park, Choi et al, 2021;Wen, Qu et al, 2021;Yuan, Q., Zhang et al, 2022;, the spatial and temporal distribution characteristics of the elderlyinvolved accidents (Cardona, Arango et al, 2017;Chang, Li et al, 2020;Etehad, Yousefzadeh-Chabok et al, 2015;Freitas, Bonolo et al, 2015;Kang, Cho et al, 2018;Nishino, 2013;Prochowski, Gidlewski et al, 2018;Sadeghi-Bazargani, Samadirad et al, 2018;Sagar, Stamatiadis et al, 2020;Setiawan, 2021), safety awareness and risk cognition of the elderly (Doi, Ishii et al, 2020;Guo, Shi et al, 2018;Hamido, Hamamoto et al, 2021;Handa and Mitobe, 2020;Jian and Shi, 2020;Laosee, Rattanapan et al, 2018;Makizako, Shimada et al, 2018;Nakagawa, 2019;Nishiuchi, Park et al, 2021;Pulvirenti, Distefano et al, 2020;Söllner and Florack, 2019;Uchibori, Handa et al, 2021). Using traditional traffic safety analysis methods, scholars have analysed the relationship between the number or severity of crashes and various factors involving human error, vehicles and the road environment to find the cause of accidents.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Studies on Traffic Crashes Involving the Elderly:

Jin

et al. 2023

IRSPSD International

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Ageing is an inevitable trend of population development. It is vital to study the traffic safety of elderly populations. Although much literature has been produced about this issue, problems such as an inconsistent definition of the elderly and scattered research objectives still exist. This study uses crash data analysis to find the conclusions, limitations and development directions of this research by retrieving accident analysis studies of the elderly from 2015 to the present. This study reviews crash analysis methods and crash characteristics and crash-influencing factors among the elderly. Most of the current studies analysing traffic accidents in the elderly come from the United States, China and Korea. The literature typically defines the elderly as those above 60 or 65 years old. Common research methods include statistical and machine learning methods. Key factors influencing traffic safety among the elderly are factors related to crash victims, vehicles and the road environment. Future research directions are proposed to investigate the heterogeneity of elderly populations in greater depth.

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Analysis of impact of elderly drivers on traffic safety using ANN based car-following model

Cited by 21 publications

References 30 publications

Human-like speed modeling for autonomous vehicles during car-following at intersections

Human-like speed modeling for autonomous vehicles during car-following at intersections

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Analysis of Studies on Traffic Crashes Involving the Elderly:

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