A statistical analysis of the correlates of compliance and defiance of seatbelt use

Farooq, Muhammad; Ahmed, Asif; Saeed, Tariq Usman

doi:10.1016/j.trf.2020.12.008

Cited by 12 publications

(3 citation statements)

References 66 publications

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“…When ramps become congested, traffic on arterial roads and freeways begins to crawl. Some ramps are extremely close to one another, resulting in longer travel times, frequent queues, and crash hazards 5 . Also, due to the lack of a conflict point near the ramps, strict management and control measures are not generally implemented 7 .…”

Section: Literature Reviewmentioning

confidence: 99%

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A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions

Alshehri,

Farooq,

Farhan

et al. 2023

Sci Rep

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Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety.

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Section: Literature Reviewmentioning

confidence: 99%

“…Several times, congestion issues such as bottlenecks or lane-changing dilemmas during peak hours can affect driving abilities and cause severe crashes 4 . These crashes not only pose a difficult challenge to transportation agencies, but they also have a significant economic impact 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions

Alshehri,

Farooq,

Farhan

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…LR is a generalized linear model and has been widely used in accident prediction(Venkata et al, 2020;Dong et al,2018) and conflict risk prediction(Costela et al, 2020) in traffic safety research. At the same time, it is used in traffic system performance tests(Cafiso et al, 2020; Liu et al,2018) and behavior prediction(Farooq et al, 2021; Ghasemzadeh et al, 2018).…”

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confidence: 99%

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Lyu

Wang

et al. 2021

JICV

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Purpose An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS). Design/methodology/approach Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data. Findings The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine. Originality/value The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

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Young Adult Alcohol and Cannabis Impaired Driving After the Opening of Cannabis Retail Stores in Washington State

Hultgren,

Calhoun,

Fleming

et al. 2024

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A statistical analysis of the correlates of compliance and defiance of seatbelt use

Cited by 12 publications

References 66 publications

A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions

A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Young Adult Alcohol and Cannabis Impaired Driving After the Opening of Cannabis Retail Stores in Washington State

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