Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions

• URL: http://arxiv.org/abs/2211.03187v1
• Date: Sun, 6 Nov 2022 17:44:25 GMT
• Title: Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions
• Authors: Ahmed Hossain, Xiaoduan Sun, Raju Thapa, Julius Codjoe
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that 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 such as failure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally, young drivers (15-24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian alcohol/drug involvement as the most frequent item in the dark-with-streetlight condition. This crash type is particularly associated with pedestrian action (crossing intersection/midblock), driver age (55-64 years), speed limit (30-35 mph), and specific area type (business with mixed residential area). Fatal pedestrian crashes are found to be associated with roadways with high-speed limits (>50 mph) during the dark without streetlight condition. Some other risk factors linked with high-speed limit related crashes are pedestrians walking with/against the traffic, presence of pedestrian dark clothing, pedestrian alcohol/drug involvement. The research findings are expected to provide an improved understanding of the underlying relationships between pedestrian crash risk factors and specific lighting conditions. Highway safety experts can utilize these findings to conduct a decision-making process for selecting effective countermeasures to reduce pedestrian crashes strategically.

Related papers

• How Do Drivers Behave at Roundabouts in a Mixed Traffic? A Case Study Using Machine Learning [1.1137087573421256]
  This study investigates driving behavior at roundabouts in a mixed traffic environment using a data-driven unsupervised machine learning. Results showed that most of the drivers proceeding through a roundabout can be mostly classified into two driving styles: conservative and normal. It seems that drivers tend to behave abnormally as they interact with VRUs at roundabouts, which increases the risk of crashes when an intersection is multimodal.
  arXiv  Detail & Related papers  (2023-09-23T18:02:57Z)
• 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)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• Infrastructure-based End-to-End Learning and Prevention of Driver Failure [68.0478623315416]
  FailureNet is a recurrent neural network trained end-to-end on trajectories of both nominal and reckless drivers in a scaled miniature city. It can accurately identify control failures, upstream perception errors, and speeding drivers, distinguishing them from nominal driving. Compared to speed or frequency-based predictors, FailureNet's recurrent neural network structure provides improved predictive power, yielding upwards of 84% accuracy when deployed on hardware.
  arXiv  Detail & Related papers  (2023-03-21T22:55:51Z)
• Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: association knowledge using data mining approach [0.4899818550820575]
  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.
  arXiv  Detail & Related papers  (2023-02-28T16:53:54Z)
• 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)
• Drivers' attention detection: a systematic literature review [62.997667081978825]
  Many factors can contribute to distractions while driving, since objects or events to physiological conditions, as drowsiness and fatigue, do not allow the driver to stay attentive. The technological progress allowed the development and application of many solutions to detect the attention in real situations. Our work presents a Systematic Literature Review of the methods and criteria used to detect attention of drivers at the wheel.
  arXiv  Detail & Related papers  (2022-04-06T11:36:40Z)
• Datacentric analysis to reduce pedestrians accidents: A case study in Colombia [0.0]
  Since 2012, in a case-study in Bucaramanga-Colombia, 179 pedestrians died in car accidents, and another 2873 pedestrians were injured. This work implements simulations to save lives by reducing the city's accidental rate and suggesting new safety policies to implement. The first and most efficient safety policy to implement-validated by our simulations-would be to build speed bumps in specific places before the crossings reducing the accident rate by 80%.
  arXiv  Detail & Related papers  (2021-04-02T06:59:50Z)
• 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)
• Where are the Dangerous Intersections for Pedestrians and Cyclists: A Colocation-Based Approach [3.8355893560092893]
  Pedestrians and cyclists are at greater risk for being killed in a crash than other road users. Finding ways to minimize problematic street environments is critical.
  arXiv  Detail & Related papers  (2020-05-30T13:54:43Z)

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.