Identifying Similarities and Dissimilarities Among Road Accident Patterns

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

This study presents the distribution of road accidents in Bangkok, Thailand, during the COVID-19 outbreak (162 days) using spatial statistics. The findings show that the number of road accidents fell by 28.53% during the lockdown period (March 26 to June 14, 2020) compared to before the lockdown period (January 5 to March 25, 2020). There was also a considerable decrease in road accidents by approximately 4.96% compared to the same period in 2019 (January 4 to June 14, 2019). High accident incidence areas were located in central and southwestern Bangkok, and this pattern was also observed before and during the lockdown as well as in 2019. The findings revealed the numbers and patterns of road accidents that occurred during the COVID-19 outbreak. This information can be useful in minimizing and limiting the impact and damage caused by road accidents in Bangkok.

Identifying the (Hot Spots) for traffic accidents in Erbil City A study of the problems of the Urban Environment

Social patterns of road accidents to children; some characteristics of vulnerable families.

With the rapidly increasing rates of vehicle usage during recent decades, road accidents have become a significant concern, posing not only risks of injuries but also ranking among the leading causes of fatalities for young and middle-aged individuals. Several factors influence the occurrence of accidents, including careless driving, atmospheric conditions, speeding, and driving under the influence. Understanding the circumstances that lead to a greater risk of road accidents is very helpful for their prevention. The primary goal of this work is to explore patterns in road accidents that have occurred within the state of Albania based on web data scraped from news portals and reports from governmental institutions. The data mining pipeline first involves an intensive data preprocessing phase where scraping, filtering, and refining techniques are employed. Subsequently, several classification models are built on the preprocessed data. These models are developed using various methodologies, including naïve Bayes, random forests, XGBoost, and LightGBM. The constructed classification models are evaluated based on training-test splitting of the preprocessed data using various performance measures. Finally, these models can be used to predict the likelihood of accidents based on certain circumstances.

The main aim of this research work is to be aware of the road traffic accident scenario, injurious effects and pattern in Bangladesh. Moreover we are interested to forecast the magnitude of road traffic accidents for the future so that decision makers can make appropriate decision for precaution. This study also provides an assessment of road traffic accidents in Bangladesh and its impact based on data collected for the period of 1971 to 2017. In this study we have tried to pick up the main reasons of road accidents and to observe the tremendous situation. The study observed that the general trends of road traffic accident (RTA), deaths and injuries reveal that the number of RTA, deaths and injuries increased gradually with little fluctuations form 1971 to 2007 and after 2007 there is a slow decreasing trend. Although the number of RTA and deaths observed decreasing trend in recent years, the ratio of number of deaths to number of accident increased significantly. The rate of register vehicles per 10,000 people increased moderately throughout the period but a sharp increment is exhibited from 2009. Highest percentage of RTA (34%) and deaths is due to RTA (32%) in Dhaka division while the lowest percentage of RTA (4%) in Barisal and Sylhet divisions and deaths is due to RTA (3%) in Barisal division. It is noticed that the maximum number of injuries occurred between ages 21 and 30 while the maximum number of deaths occurred between ages 11 and 30. Most of the RTA and deaths due to RTA are caused by run over by vehicles and head to head collision. The severity of occurring road accident and number of deaths are higher during the festive periods because of involving higher frequency of traveling than usual. The time plot shows that the graph maintains a decreasing movement from 2012 to 2015 but increases from 2015 to 2017. In the research an additive time series model approach is applied. It included the estimation of trend, seasonal variation and random variation using triple exponential smoothing method. We performed forecasting of RTA eliminating seasonal impact for the next three consecutive years (2018-2020) with 95% confidence interval using Holt-Winters exponential technique.

To assess seasonal, weekday, and public holiday effects on alcohol-related road accidents and drinking diaries among young Swiss men. Federal road accident data (35,485 accidents) from Switzerland and drinking diary data from a large cohort of young Swiss men (11,930 subjects) were analysed for temporal effects by calendar week, weekday and public holiday (Christmas, New Years, National Day). Alcohol-related accidents were analysed using rate ratios for observed versus expected numbers of accidents and proportions of alcohol-related accidents relative to the total number. Drinking diaries were analysed for the proportion of drinkers, median number of drinks consumed, and the 90th percentile's number of drinks consumed. Several parallel peaks were identified in alcohol-related accidents and drinking diaries. These included increases on Fridays and Saturdays, with Saturday drinking extending until early Sunday morning, an increase during the summer on workdays but not weekends, an increase at the end of the year, and increases on public holidays and the evening before. Our results suggest specific time-windows that are associated with increases in drinking and alcohol-related harm. Established prevention measures should be enforced during these time-windows to reduce associated peaks.

A study based on the Danish Road Directorates accident statistics shows that there are different gender patterns in how we behave in traffic and our involvement in traffic accidents. Overall men drive more by car and take longer trips. Women take more trips, but they are shorter, and travel more by bicycle or walking. Although men and women spend the same amount of time in traffic, twice as many men as women are involved in traffic accidents. Men constitute 62% of all persons killed and injured in road accidents, and are involved in 66% of the accidents. Not only gender, but education, income and age are also important factors in the accident patterns of men and women in Denmark. People with primary/lower secondary school as the highest education level are over represented in accident statistics in proportion to other educational groups. Young people aged 18–24 constitute about 20% (the largest group) of persons killed and injured in traffic accidents, although they only constitute approximately 8% of the population. In this group, there are about twice as many men killed or injured as women. The overall pattern is that, the older you become, the higher educated you are and the higher income you have—the smaller is your risk of involvement in accidents in Denmark. This knowledge is of importance when planning communication strategies with different segments of the population and when reaching out to young people in schools, who are at the highest risk of getting killed or injured in accidents.

Understanding spatial concentrations of road accidents using frequent item sets

Evaluation of a stimulation plan for municipalities in the Netherlands

Accidents — In-Depth Analysis; towards a method AIDA?