Recommended Passenger Car Size Definitions and the Need for Vehicle Identification in Traffic Accident Data

1985 ◽  
Author(s):  
J. C. Marsh
Keyword(s):  
Traffic Accident ◽  
Passenger Car ◽  
Vehicle Identification ◽  
Accident Data

scholarly journals ROAD SAFETY CAMPAIGNS TO REDUCE TRAFFIC ACCIDENTS FOR YOUNG ROAD USERS

ICCD ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 601-606
Author(s):  
Widodo Budi Dermawan ◽  
Dewi Nusraningrum
Keyword(s):  
School Environment ◽  
Road Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
School Administrators ◽  
Road User ◽  
The Road ◽  
Road Users ◽  
Accident Data

Every year we lose many young road users in road traffic accidents. Based on traffic accident data issued by the Indonesian National Police in 2017, the number of casualties was highest in the age group 15-19, with 3,496 minor injuries, 400 seriously injured and 535 deaths. This condition is very alarming considering that student as the nation's next generation lose their future due to the accidents. This figure does not include other traffic violations, not having a driver license, not wearing a helmet, driving opposite the direction, those given ticket and verbal reprimand. To reduce traffic accident for young road user, road safety campaigns were organized in many schools in Jakarta. This activity aims to socialize the road safety program to increase road safety awareness among young road users/students including the dissemination of Law No. 22 of 2009 concerning Road Traffic and Transportation. Another purpose of this program is to accompany school administrators to set up a School Safe Zone (ZoSS), a location on particular roads in the school environment that are time-based speed zone to set the speed of the vehicle. The purpose of this paper is to promote the road safety campaigns strategies by considering various campaign tools.

scholarly journals Road Traffic Accident Data Analysis and Its Visualization

2021 ◽  
Vol 9 (5) ◽  
pp. 1603-1614
Author(s):  
Muhammad Babar Ali Rabbani ◽  
Muhammad Ali Musarat ◽  
Wesam Salah Alaloul ◽  
Ahsen Maqsoom ◽  
Hamna Bukhari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Road Traffic ◽  
Accident Data

scholarly journals Trend analysis and fatality causes in Kenyan roads: A review of road traffic accident data between 2015 and 2020

2020 ◽  
Vol 7 (1) ◽  
pp. 1797981
Author(s):  
Joseph Kamau Muguro ◽  
Minoru Sasaki ◽  
Kojiro Matsushita ◽  
Waweru Njeri
Keyword(s):  
Trend Analysis ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Road Traffic ◽  
Accident Data

scholarly journals Mining of inland water traffic accident data using a biclustering algorithm: A case study of the Yangtze River

2018 ◽  
Vol 233 (1) ◽  
pp. 48-57
Author(s):  
Chen Chen ◽  
Qing Wu ◽  
Song Gao
Keyword(s):  
High Risk ◽  
Traffic Accident ◽  
Safety Management ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Inland Water ◽  
Raising Awareness ◽  
Accident Data ◽  
Local Patterns ◽  
Conserved Gene

Analysis of maritime accident data is important for improving safety management. Clustering is the favoured method of mining marine accident data. However, traditional one-way clustering methods are limited by their focus on global patterns, which does not account for the contingent characteristics of accidents. In this study, biclustering algorithms (BAs) typically used for gene expressions are introduced for analysis of inland water traffic accident data. BAs are good for discovering local patterns (LPs), which represent the similarities between partial accidents and partial attributes. LPs are the more likely modes in accident data, which are difficult to discern using who is traditional one-way clustering. During biclustering of original accident data, six LPs involving replicative accidents are uncovered, thereby suggesting a high risk in similar scenarios. With biclustering of accident attribute factors, the interrelationships among factors are discovered. According to the LPs explored using BAs, high-risk scenarios should gain the attention of shipping companies and safety management departments. Two recommendations are presented: raising awareness of the need for immediate accident reporting and disseminating rescue knowledge. After comparing their applications, the order-preserving submatrix (OPSM) and conserved gene expression motif (xMotifs) algorithms are regarded as the most suitable BAs for analysing maritime accident data.

Augmenting Classifiers Performance through Clustering

2018 ◽  
Vol 8 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Sachin Kumar ◽  
Prayag Tiwari ◽  
Kalitin Vladimirovich Denis
Keyword(s):  
Traffic Accident ◽  
Latent Class ◽  
Safety Concern ◽  
Health And Safety ◽  
Road Accident ◽  
Support Vector ◽  
Road Accidents ◽  
Real World Data ◽  
The Public ◽  
Accident Data

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

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