Driving risk assessment using driving behavior data under continuous tunnel environment

Yan, Ying; Dai, Youhua; Li, Xiaodong; Tang, Jinjun; Guo, Zhongyin

doi:10.1080/15389588.2019.1675154

Cited by 29 publications

(11 citation statements)

References 13 publications

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“…Consequently, research on driving risk has been a topic of interest over recent decades. Simulation experiments to evaluate driving risk have been designed in the laboratory setting to identify driving risk factors [ 11 , 12 , 13 , 14 ] as well as experiments using actual vehicles on the road [ 15 , 16 , 17 , 18 , 19 ]. Questionnaire surveys for driving risk assessment have also been studied [ 20 , 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Sun

Guillén

et al. 2021

Entropy

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This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

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Section: Literature Reviewmentioning

confidence: 99%

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Sun

Guillén

et al. 2021

Entropy

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“…Generally speaking, AADT represents [61,62]. In this case, the transportation authorities may make a series of strategies to deal with the problems caused by these incidents [63,64]. Interestingly, the longer clearance time seems less sensitive to incident type than shorter clearance time.…”

Section: Importance Evaluation For Explanatory Factorsmentioning

confidence: 99%

Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model

Tang

Zheng

Han

et al. 2020

Journal of Advanced Transportation

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Accurate prediction and reliable significant factor analysis of incident clearance time are two main objects of traffic incident management (TIM) system, as it could help to relieve traffic congestion caused by traffic incidents. This study applies the extreme gradient boosting machine algorithm (XGBoost) to predict incident clearance time on freeway and analyze the significant factors of clearance time. The XGBoost integrates the superiority of statistical and machine learning methods, which can flexibly deal with the nonlinear data in high-dimensional space and quantify the relative importance of the explanatory variables. The data collected from the Washington Incident Tracking System in 2011 are used in this research. To investigate the potential philosophy hidden in data, K-means is chosen to cluster the data into two clusters. The XGBoost is built for each cluster. Bayesian optimization is used to optimize the parameters of XGBoost, and the MAPE is considered as the predictive indicator to evaluate the prediction performance. A comparative study confirms that the XGBoost outperforms other models. In addition, response time, AADT (annual average daily traffic), incident type, and lane closure type are identified as the significant explanatory variables for clearance time.

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“…For tunnels, numerous studies have been conducted on traffic safety. However, most studies have focused on tunnel geometric characteristics and traffic volumes, tunnel illumination and visibility (Miyake et al, 2019 ), accident zone locations (Amundsen and Ranes, 2000 ; Ma et al, 2009 ), and safety speed (Yan et al, 2019 ). The interesting study by Yan et al ( 2019 ) addressed driving risk levels in continuous tunnels, 250–1,000 m long and two lanes in each direction, using various risk indicators.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

et al. 2021

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In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel.

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Driving risk assessment using driving behavior data under continuous tunnel environment

Cited by 29 publications

References 13 publications

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

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