High-Risk Road Accident Corridors in Dhaka, Bangladesh

2013 ◽  
Vol 65 (3) ◽  
Author(s):  
Ishtiaque Ahmed ◽  
Bayes Ahmed ◽  
Mohd. Rosli Hainin
Keyword(s):  
High Risk ◽  
Traffic Accident ◽  
Safety Management ◽  
Road Transport ◽  
Road Accident ◽  
Highway Safety ◽  
Road Accidents ◽  
Highway Safety Manual ◽  
Roadway Safety ◽  
Accident Data

Bangladesh has one of the highest fatality rates in road accidents and to address the safety problem is a serious concern. Dhaka is the most vulnerable city of the country. Bangladesh Road Transport Authority maintains a database of accidents using outdated software that lacks in geo-referencing facility.  This makes the analysis of accident locations a challenging task. The area for this study was the Dhaka Metropolitan Police area where the concerned forty one police stations are responsible for collecting traffic accident data. The Highway Safety Manual identifies the “Network Screening” as the first step of the Roadway Safety Management Process. This study focuses on locating the accidents on urban roadways in Dhaka and identifies thirty corridors and ranks them using geo-referenced data through developing and using a GIS database. Dhaka-Mymensing Road was found to be the most vulnerable road corridor followed by Airport Road and Mirpur Road respectively. The study recommended special attention and special “Diagnostic” studies as explained in the Highway Safety Manual for the high-risk corridors and to put emphasis on the accident data collection and reporting system. Adoption of modern technologies like GPS and GIS in collecting and reporting of the traffic accident data was emphasized.

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.

scholarly journals Analysing Road Accident Criticality using Data mining

2019 ◽  
pp. 408-415
Author(s):  
Shahsitha Siddique V ◽  
Nithin Ramakrishnan
Keyword(s):  
Data Mining ◽  
Road Transport ◽  
Road Accident ◽  
Machine Learning Algorithms ◽  
Road Accidents ◽  
Decision Tree Classifier ◽  
Efficient Manner ◽  
Tree Classifier ◽  
Accident Severity ◽  
Accident Data

Road transport is one of the most vital forms of transportation system, connecting both long and short distances in our country. There are several attributes, which affect the intensity of a road accident like speed of the vehicle, road conditions, time of the accident etc. Analysing these attributes gives an idea about the factors lead to the severity of the accident. Data mining is a method to analyse huge amount of traffic data in an efficient manner, which gives the factors, affect the road accidents. Several machine learning algorithms can be used to find the relation between traffic attributes the lead to the severity of the accidents. In this work, we use three methods for predicting accident criticality. First, Naive Bayesian Classifier is used to get the accident severity based on Bayes rule. Then, Decision Tree classifier is used for same purpose for accident severity calculation. Finally K-Nearest Neighbour(KNN) classifier is employed for severity calculation. The accuracy of the algorithms are compared and it is found that KNN performs better than the other two algorithms employed. The major aim of the work is to find the accident severity. Also the work aims to reduce road accidents by giving awareness to public using the above method.

scholarly journals Prioritization of Black Spots in Hilly Road Segment using Road Engineering Data

2020 ◽  
Vol 9 (3) ◽  
pp. 4418-4422
Keyword(s):  
Black Spot ◽  
Road Transport ◽  
Road Accident ◽  
Industrialized Countries ◽  
Road Accidents ◽  
Road Engineering ◽  
The Road ◽  
Black Spots ◽  
Accident Data ◽  
On The Road

Road accidents are one of the causes of disability, injury and death. As per the latest road accident data released by the Ministry of Road Transport & Highways (MoRTH), the total number of accidents increased by 2.5 percent from 4,89,400 in 2014 to 5,01,423 in 2015. The analysis reveals that about 1,374 accidents and 400 deaths take place every day. Every single year, it has been estimated that over three lakh persons die and 10-15 million persons are injured in road accidents throughout the world. According to the analyses, statistics of global accident indicate that in developing countries, the rate of fatality per licensed vehicle is very high as compared to that of industrialized countries. A road stretch of about 500 metres in length in which either ten fatalities or five road accidents (involving grievous injuries/fatalities) took place during last three calendar years, on National Highways is considered as a road accident black spot according to MoRTH, Government of India. In the present study the identified black spots of Haridwar and Dehradun city were included comprising of a total of 81 black spots out of which there were 49 black spots which were identified in Dehradun followed by 32 black spots in Haridwar. The present study was an attempt to carry out the prioritization of these identified blackspots with respect to the factors that were considered to evaluate accident prone locations on the road. The identified black spots were then prioritized using the classification scheme (ranking from low to high).The study reveals that the advantage of using this approach for prioritizing accident black spots on roads is that it requires very less additional data other than the road network maps.

scholarly journals Road Transport Accidents in Nigeria and the Role of Automotive Active Safety System

2016 ◽  
Vol 11 (1) ◽  
pp. 49-54
Author(s):  
Olasunkanmi Oriola Akinyemi ◽  
Hezekiah O Adeyemi ◽  
Olusegun Jinadu
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Transportation Network ◽  
Road Transport ◽  
Road Accident ◽  
Active Safety ◽  
Road Accidents ◽  
Road Transportation ◽  
Road Traffic Safety

Abstract Analysis of road traffic accidents revealed that most accidents are as a result of drivers’ errors. Over the years, active safety systems (ASS) were devised in vehicle to reduce the high level of road accidents, caused by human errors, leading to death and injuries. This study however evaluated the impacts of ASS inclusions into vehicles in Nigeria road transportation network. The objectives was to measure how ASS contributed to making driving safer and enhanced transport safety. Road accident data were collected, for a period of eleven years, from Lagos State Ministry of Economic Planning and Budget, Central Office of Statistics. Quantitative analysis of the retrospective accident was conducted by computing the proportion of yearly number of vehicles involved in road accident to the total number of vehicles for each year. Results of the analysis showed that the proportion of vehicles involved in road accidents decreased from 16 in 1996 to 0.89 in 2006, the injured persons reduced from 15.58 in 1998 to 0.3 in 2006 and the death rate diminished from 4.45 in 1998 to 0.1 in 2006. These represented 94.4 %, 95 % and 95 % improvement respectively on road traffic safety. It can therefore be concluded that the inclusions of ASS into design of modern vehicles had improved road safety in Nigeria automotive industry.

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

scholarly journals TRAFFIC-RELATED MORTALITY IN MOLDOVA

2020 ◽  
pp. 140-147
Keyword(s):  
Life Expectancy ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Road Accidents ◽  
Continuous Increase ◽  
Risk Of Death ◽  
Significant Difference ◽  
Related Mortality

This article analyses the mortality caused by road accidents in Moldova depending on the degree of involvement of pedestrians, cyclists, motorcyclists, drivers and passengers of transport units, depending on age and sex. Results suggest that traffic-related mortality in Moldova has shown an increased incidence among the young and working-age population, where a significant difference between males and females is observed. Among the youth, traffic-related deaths register between 10-27% of the overall mortality in both sexes. The risk exposure of dying in a traffic accident decreases with age and is less significant in the retired ages. During the years 1998-2015, avoidance of trafficrelated deaths would have assured an increase in life expectancy between 0.40-0.56 years in males, and 0.09-0.23 years in females. The continuous increase in the number of transport units on public roads, as well as in the number of hours spent in traffic, influences the degree of exposure to the risk of death or injury as a result of road traffic accidents. Trauma resulting from road accidents increases the incidence of premature mortality and disability among the population, which is reflected by the decrease of healthy life expectancy. It is ascertained that the road accident mortality requires a detailed and comprehensive analysis given the multitude of factors influencing deaths and injuries related to a traffic accident among the population. Thus, in order to improve road safety and reduce mortality incidence among traffic participants, a range of actions has to be implemented by the liable actors, including through the international experience.

scholarly journals Road Safety Assessment Considering the Expected Fatal Accident Density

2020 ◽  
Vol 15 (2) ◽  
pp. 31-48
Author(s):  
Vilma Jasiūnienė ◽  
Rasa Vaiškūnaitė
Keyword(s):  
Road Safety ◽  
Safety Assessment ◽  
Safety Management ◽  
Road Accident ◽  
Fatal Accident ◽  
Road Infrastructure ◽  
The Road ◽  
Management Procedures ◽  
Accident Data ◽  
Wide Road

Network-wide road safety assessment throughout the whole network is one of the four road infrastructure safety management procedures regulated by Directive 2019/1936/EC of the European Parliament and of the Council of 23 October 2019 Аmending Directive 2008/96/EC on Road Infrastructure Safety Management and one of the methods for determining the direction of investment in road safety. So far, the implementation of the procedure has been lightly regulated and adapted using various road safety indicators. The paper describes the evaluation of road accident data that is one of the criteria for conducting a network-wide road safety assessment. Taking into consideration that networkwide road safety assessment is a proactive road safety activity, the paper proposes to conduct road safety assessment considering the expected fatal accident density. Such assessment makes it possible to assess the severity of accidents, and the use of the predicted road accident data on calculating the introduced road accident rate contributing to the prevention of accidents. The paper describes both the empirical Bayes method for predicting road accidents and the application of one of the road safety indicators – the expected fatal accident density – to determine five road safety categories across the road network. The paper demonstrates the application of the proposals submitted to Lithuanian highways using road accident and traffic data for the period 2014–2018.

scholarly journals Model of registration of road accidents: problems of law enforcement

2021 ◽  
Vol 109 ◽  
pp. 01015
Author(s):  
Marina Fokina ◽  
Lilia Voitovich ◽  
Olga Egorova
Keyword(s):  
Law Enforcement ◽  
Police Officers ◽  
Mobile Application ◽  
Traffic Accident ◽  
Road Accident ◽  
Road Accidents ◽  
Legal Proceedings ◽  
The Russian Federation ◽  
Technical Factors ◽  
Russian Association

This publication focuses on the theoretical investigation of issues arising in the Russian Federation in law enforcement related to the recently introduced digital procedure for the registration of road accidents through the “DTP.Europrotokol” electronic application running on the Android or Apple iOS operating systems. The purpose of the study is to examine the above method of independent registration of a road accident without authorized police officers, developed by the Russian Association of Motor Insurers, with a substantiation of the possibility of subsequent attaching evidentiary value to the information contained in such an application in legal proceedings for the recovery of insurance compensation in connection with the occurrence of this road accident. The authors assessed the effectiveness and feasibility of this digital traffic accident model, including the lack of sufficient regulatory guarantees for the safety of the information uploaded by the user to the mobile application, the existence of virtually unlimited powers of the application developer to control its functioning. They drew attention to certain enforcement problems associated with the assessment of the information available from the “DTP.Europrotokol” application, which is not possible to use in all cases, data the accuracy of which depends on many technical factors.

scholarly journals Modeling the Complexity of Road Accidents Prevention

2020 ◽  
Vol 9 (2) ◽  
pp. 24-41
Author(s):  
Alex Kizito ◽  
Agnes Rwashana Semwanga
Keyword(s):  
Traffic Safety ◽  
Police Officers ◽  
Road Transport ◽  
Road Accident ◽  
Road Accidents ◽  
Dynamic Interactions ◽  
The Road ◽  
Dynamic Synthesis ◽  
Road Users ◽  
Synthesis Methodology

Simplistic representations of traffic safety disregard the dynamic interactions between the components of the road transport system (RTS). The resultant road accident (RA) preventive measures are consequently focused almost solely on individual/team failures at the sharp end of the RTS (mainly the road users). The RTS is complex and therefore cannot be easily understood by studying the system parts in isolation. The study modeled the occurrence of road accidents in Uganda using the dynamic synthesis methodology (DSM). This article presents the work done in the first three stages of the DSM. Data was collected from various stakeholders including road users, traffic police officers, road users, and road constructors. The study focused on RA prevention by considering the linear and non-linear interactions of the variables during the pre-crash phase. Qualitative models were developed and from these, key leverage points that could possibly lower the road accident incidences demonstrating the need for a shared system wide responsibility for road safety at all levels are suggested.

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