Decision-adjusted driver risk predictive models using kinematics information

Mao, Huiying; Guo, Feng; Deng, Xinwei; Doerzaph, Zachary R.

doi:10.1016/j.aap.2021.106088

Cited by 13 publications

(3 citation statements)

References 27 publications

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“…High G-force events have also been used in teenage risk behavior intervention [32]. Mao et al [33] investigated the optimal threshold to define a high G-force event and to predict high risk drivers. The literature in general supports the validity of high G-force events as a surrogate, with hard-braking events (large longitudinal deceleration) being one of the most important metrics.…”

Section: Improved Safety Through Awareness and Avoidancementioning

confidence: 99%

“…A wheel speed-based Hbe is identified when the minimum acceleration value on the longitudinal direction, min(Accel v long ), is lower than or equal to a predefined threshold. We empirically set this threshold value to −5m/s 2 , which is similar to values suggested by literature [28,29,33]. The wheel speed-based Hbe, as described in the following formula, is treated as the ground truth label for the prediction model.…”

Section: Name Unit Shape Descriptionmentioning

confidence: 99%

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Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services

Liu¹,

Rácz²,

Vaillancourt³

et al. 2022

Preprint

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Road crashes are the sixth leading cause of lost disability-adjusted life-years (Dalys) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash causations and predict future crash risk in a timely manner. Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors. As an alternative to using sensors fixed in vehicles, this paper presents a scalable approach for detecting hard-braking events using the kinematics data collected from smartphone sensors. We train a Transformer-based machine learning model for hard-braking event detection using concurrent sensor readings from smartphones and vehicle sensors from drivers who connect their phone to the vehicle while navigating in Google Maps. The detection model shows superior performance with a 0.83 Area under the Precision-Recall Curve (Pr-auc), which is 3.8×better than a Gps speed-based heuristic model, and 166.6×better than an accelerometer-based heuristic model. The detected hard-braking events are strongly correlated with crashes from publicly available datasets, supporting their use as a safety surrogate. In addition, we conduct model fairness and selection bias evaluation to ensure that the safety benefits are equally shared. The developed methodology can benefit many safety applications such as identifying safety hot spots at road network level, evaluating the safety of new user interfaces, as well as using routing to improve traffic safety.

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Section: Improved Safety Through Awareness and Avoidancementioning

confidence: 99%

Section: Name Unit Shape Descriptionmentioning

confidence: 99%

Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services

Liu¹,

Rácz²,

Vaillancourt³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…One study used the NEO Five Factor Inventory representing drivers’ neuroticism (N), openness to experience (O), extroversion (E), agreeableness (A), and conscientiousness (C) to show the relationship between these factors and safety critical events (SCEs) ( 24 ). A subsequent study used demographic features, driving knowledge, clock drawing score, Barkley’s ADHD score, sensation seeking score (SSS), and driver behavioral factors to develop an optimal driver risk prediction model ( 25 ). Other potentially important driver factors from different psychological domains include perception, cognition, and attentiveness when driving, and interactions with the environment ( 26 , 27 ).…”

Section: Introductionmentioning

confidence: 99%

Driver Psychology Latent Classes as Predictors of Traffic Incident Occurrence in Naturalistic Driving Study Data

Sharmin

Ivan

Marsh

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Crash risk depends on several factors, driver factors being the most significant of all. Previous studies have tended to use roadway and driver demographic information to explain crash risk, overlooking driver psychological characteristics, which are also important for crash risk estimation. Crash data from police reports are available only for reportable crashes and do not detail driver characteristics or comparable information on driving activities. Naturalistic driving studies (NDSs) offer unique opportunities to obtain information about driver attributes, behavior, or other precrash factors for predicting crash occurrence. This study estimated NDS event-oriented models to evaluate the interaction between driver attributes and roadway environmental factors for predicting safety critical events. A latent class clustering approach was used to uncover categories of drivers by psychological, perceptual, and cognitive characteristics, and by driving experience. The results revealed four driver types: risk-taker, careful-impaired, careful-unimpaired, and distractible. These types were incorporated in mixed-effects binary logistic models, with roadway, traffic, and environmental variables to estimate and predict crash risk. The models that included driver factors more successfully predicted crash risk than those without. Risk-takers showed the highest probability of being in crashes. However, careful-impaired drivers—those whose impairments made it difficult to identify the location of another vehicle, visualize missing information, who had difficulties with visual–spatial perception and executive functioning—posed a higher crash risk in roadway conditions such as snow, lack of lane markings, and certain traffic operating conditions. The results point to novel avenues for educational and behavioral interventions to improve road safety.

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Driving Behavior and Customer Handling of Urban Public Transportation Drivers and Operators Before and After the COVID-19 Outbreak in Ethiopia, 2022

Tareke

2023

Transp. in Dev. Econ.

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Decision-adjusted driver risk predictive models using kinematics information

Cited by 13 publications

References 27 publications

Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services

Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services

Driver Psychology Latent Classes as Predictors of Traffic Incident Occurrence in Naturalistic Driving Study Data

Driving Behavior and Customer Handling of Urban Public Transportation Drivers and Operators Before and After the COVID-19 Outbreak in Ethiopia, 2022

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