Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: association knowledge using data mining approach

• URL: http://arxiv.org/abs/2302.14754v1
• Date: Tue, 28 Feb 2023 16:53:54 GMT
• Title: Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: association knowledge using data mining approach
• Authors: Ahmed Hossain, Xiaoduan Sun, Shahrin Islam, Shah Alam, Md Mahmud Hossain
• Abstract summary: More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. Based on the generated rules, the findings reveal several interesting crash patterns in the daylight, dark-with-streetlight, and dark-no-streetlight.
• Score: 0.4899818550820575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed. Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and dark (with/without streetlight). This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. To accomplish this, an unsupervised data mining algorithm association rules mining (ARM) was utilized. Based on the generated rules, the findings reveal several interesting crash patterns in the daylight, dark-with-streetlight, and dark-no-streetlight, emphasizing the importance of investigating RwD crash patterns depending on the lighting conditions. In daylight, fatal RwD crashes are associated with cloudy weather conditions, distracted drivers, standing water on the roadway, no seat belt use, and construction zones. In dark lighting conditions (with/without streetlight), the majority of the RwD crashes are associated with alcohol/drug involvement, young drivers (15-24 years), driver condition (e.g., inattentive, distracted, illness/fatigued/asleep) and colliding with animal (s). The findings reveal how certain driver behavior patterns are connected to RwD crashes, such as a strong association between alcohol/drug intoxication and no seat belt usage in the dark-no-streetlight condition. Based on the identified crash patterns and behavioral characteristics under different lighting conditions, the findings could aid researchers and safety specialists in developing the most effective RwD crash mitigation strategies.

Related papers

• SmartRSD: An Intelligent Multimodal Approach to Real-Time Road Surface Detection for Safe Driving [4.22695582100076]
  We introduce a multimodal approach for the automated detection of road surface conditions by integrating audio and images. Our findings highlight the potential of integrating auditory and visual cues for enhancing road safety and minimizing accident risks.
  arXiv  Detail & Related papers  (2024-06-14T15:38:21Z)
• Abductive Ego-View Accident Video Understanding for Safe Driving Perception [75.60000661664556]
  We present MM-AU, a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11,727 in-the-wild ego-view accident videos, each with temporally aligned text descriptions. We present an Abductive accident Video understanding framework for Safe Driving perception (AdVersa-SD)
  arXiv  Detail & Related papers  (2024-03-01T10:42:52Z)
• NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes [49.92839157944134]
  In nighttime driving scenes, insufficient and uneven lighting shrouds the scenes in darkness, resulting degradation of image quality and visibility. We develop an image de-raining framework tailored for rainy nighttime driving scenes. It aims to remove rain artifacts, enrich scene representation, and restore useful information.
  arXiv  Detail & Related papers  (2024-02-28T09:02:33Z)
• Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach [15.717402981513812]
  This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed. Experimental results from 4815 crashes on Highway Interstate 5 in Washington State reveal the heterogeneous treatment effects of crashes at varying distances and durations.
  arXiv  Detail & Related papers  (2024-01-01T15:03:14Z)
• Deep learning based black spot identification on Greek road networks [52.77024349608834]
  Black spot identification, atemporal phenomenon, involves analyzing geographical location and time-based occurrence of road accidents. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports.
  arXiv  Detail & Related papers  (2023-06-19T07:08:30Z)
• Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes [0.4129225533930965]
  This study tries to eliminate shortcomings by considering important pre-crash events related factors as selected variables and the number of vehicles with or without injury as a predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. Analysis results revealed that the extent of the damage, critical pre-crash event, pre-impact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or
  arXiv  Detail & Related papers  (2023-01-04T13:07:28Z)
• Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark [77.54411007883962]
  We propose a Cognitive Accident Prediction (CAP) method that explicitly leverages human-inspired cognition of text description on the visual observation and the driver attention to facilitate model training. CAP is formulated by an attentive text-to-vision shift fusion module, an attentive scene context transfer module, and the driver attention guided accident prediction module. We construct a new large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames.
  arXiv  Detail & Related papers  (2022-12-19T11:43:02Z)
• Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions [0.0]
  This study applied Association Rules Mining to identify the hidden pattern of crash risk factors according to three different lighting conditions. Daylight pedestrian crashes are associated with children (less than 15 years), senior pedestrians (greater than 64 years), older drivers (>64 years), and other driving behaviors. Fatal pedestrian crashes are found to be associated with roadways with high-speed limits (>50 mph) during the dark without streetlight condition.
  arXiv  Detail & Related papers  (2022-11-06T17:44:25Z)
• Modelling and Detection of Driver's Fatigue using Ontology [60.090278944561184]
  Road accidents are the eight leading cause of death all over the world. Various factors cause driver's fatigue. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system.
  arXiv  Detail & Related papers  (2022-08-31T08:42:28Z)
• Human error in motorcycle crashes: a methodology based on in-depth data to identify the skills needed and support training interventions for safe riding [0.0]
  This paper defines a methodology to identify the skills needed by riders in the highest risk crash configurations to reduce casualty rates. We present a case study using in-depth data of 803 powered-two-wheeler crashes.
  arXiv  Detail & Related papers  (2021-02-19T21:30:37Z)
• Emergent Road Rules In Multi-Agent Driving Environments [84.82583370858391]
  We analyze what ingredients in driving environments cause the emergence of road rules. We find that two crucial factors are noisy perception and agents' spatial density. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
  arXiv  Detail & Related papers  (2020-11-21T09:43:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.