Showing papers in "Accident Analysis & Prevention in 2018"

TL;DR: In this article, the authors investigated the main contributing factors to road accidents by drawing on multiple sources: expert views of police officers, lay views of the driving public, and official road accident records.

TL;DR: The results show that the knowledge bases (classical documents) of road safety studies in the last two decades have focused on five major areas of "Crash Frequency Data Analysis", "Driver Behavior Questionnaire", "Safety in Numbers for Walkers and Bicyclists", "Road Traffic Injury and Prevention", and "Driving Speed and Road Crashes".

TL;DR: This study provides a comprehensive overview of the fragmented data obtained from AV manufacturers testing on California public roads from 2014 to 2017, providing an important starting point for improvements on the current drafts of the testing and deployment regulations for autonomous vehicles on public roads.

TL;DR: An hierarchical regression analysis revealed that TPB constructs; attitude toward the behavior, subjective norms, and perceived behavioral control, were significant predictors of intentions to use AV and there was partial support for the test of TAM, with ease of use predicting intended use of AV (SAE Level 3).

TL;DR: Until these driverless vehicles are able to provide universally comprehensible externally presented information or messages during interaction with other road users, they are likely to contribute to confusing and conflicting interactions between these actors, especially in a shared space setting, which may, therefore, reduce efficient traffic flow.

TL;DR: The results suggest that wearing a helmet while cycling is highly recommendable, especially in situations with an increased risk of single bicycle crashes, such as on slippery or icy roads.

TL;DR: This research found that the CV&DA technologies could lead to the reduction of light vehicles' crashes and heavy trucks' crashes by at least 32.99% and 40.88%, respectively, based on the 2005-2008 GES Crash Records.

TL;DR: This study employed the full Bayesian random parameters logistic regression approach to account for the unobserved heterogeneous effects of red-light running behaviors in Nanjing, China, and found that more red- light runners are predicted at signalized intersection crosswalks than at road segmentCrosswalks.

TL;DR: In this article, a dynamic binary random parameters (mixed) logit framework is employed to simultaneously identify pre-crash stationary and dynamic factors of accident occurrence, while accounting for unobserved heterogeneity.

TL;DR: Results from the case study suggest that the proposed framework works well in describing pedestrian-vehicle interactions which helps in evaluating pedestrian safety at non-signalized crosswalk locations.

TL;DR: Four macro-level crash models incorporating variables related to actual traffic exposure, socio-economics, land use, built environment, and bike network showed that the cyclist crashes were positively associated with bike and vehicle exposure measures, households, commercial area density, and signal density.

TL;DR: Investigating the relationship between self-regulatory secondary task performance and driving suggests that road traffic demands play a vital role in both secondary task management and driving performance.

TL;DR: This paper presented a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data, and recommended the thresholds for risk assessment of pedestrian safety.

TL;DR: In this paper, Binary Logit Models (BLM) with Bayesian inferences were utilized to classify heavy truck involvement in severe and non-severe crashes using ten years (2002-2011) of historical crash data in the State of Wyoming.

TL;DR: It is shown that the classification performance is the highest when bagging is used with decision trees, with over-sampling treatment for imbalanced data, and the effect of treatments for the im balanced data is maximized when under-sampled is combined with bagging.

TL;DR: With various accurately identified non-linear relationships between the built environment and pedestrian crashes, this study suggests local agencies to adopt geo-spatial differentiated policies to establish a safe walking environment.

TL;DR: This study compares the characteristics of compulsory and discretionary lane changes observed in a work zone section and a general section of a freeway using the lane change risk index (LCRI), a new method for estimating crash risk while a subject vehicle changes lanes.

TL;DR: Bayesian conditional logistic models were developed for within intersection crashes and intersection entrance crashes and showed that the increased adaptability for the left turn signal timing of "B" approach and more priority for "A" approach could significantly decrease the odds of crash occurrence.

TL;DR: The research showed that the three mobile phone distractions cause different levels of impairment to pedestrians' crossing performance, with the greatest effect from text distraction, followed by phone conversation distraction and music distraction.

TL;DR: It has been concluded that the scenario-based approach shared similar findings with those of the disaggregated crash risk analysis approach in which a U-shaped relationship between operating speed and crash occurrence was identified, however, the commonly adopted segment-based aggregation approach revealed a monotonous negative relationship between speed andCrash frequency.

TL;DR: A relationship between the driver's drowsiness and NDRT engagement in partial automation but not in highly automated driving is suggested and visual and mental demand associated with NDRTs did decrease reaction time, suggesting that the NDRt helped the drivers to maintain alertness during the partially automated drive.

TL;DR: It was found that intention and social norms have the biggest influence on whether a worker was observed to work safely or not, and machine learning algorithms provide an alternative approach for analyzing the relationship between the cognitive factors and behavioral data.

TL;DR: This paper aims to comprehensively establish a relationship between mean speed, speed variation and traffic crashes for the purpose of formulating effective speed management measures, specifically using an urban dataset.

TL;DR: The effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated and results show that the training system affects the take-over performances.

TL;DR: A flexible mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle type at the macro-level and the Excess Predicted Proportion (EPP) -a screening performance measure analogous to Highway Safety Manual, Excesspredicted Average Crash Frequency is proposed for hot zone identification.

TL;DR: Compact cars, young drivers, female drivers, heavy rain, deep water, and roads with a long drainage length are more likely to be associated with an increase in the level of accident severity, as are features like a tangent, down slope, right-hand curve, and shorter curve length.

TL;DR: This study used five years of WWD crashes in Louisiana to determine the key associations between the contribution factors by using multiple correspondence analysis (MCA), and showed that MCA helps in presenting a proximity map of the variable categories in a low dimensional plane.

TL;DR: This algorithm has a significantly lower false positive rate than PERCLOS-the current gold standard-and baseline, non-contextual, algorithms under design parameters that prioritize drowsiness detection and suggests contextual factors should be considered in subsequent algorithm development processes.

TL;DR: It would be recommendable to include other nonstandard vision tests, which have shown associations with driving performance, in the examination for driver licensing, to help raise the awareness of older drivers concerning their visual limitations, and adopt compensatory measures to improve their driving safety.

TL;DR: The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better and compared the performance of two CAR models in crash prediction.