scholarly journals Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Ling Shen ◽  
Jian Lu ◽  
Man Long ◽  
Tingjun Chen
Keyword(s):  
Rural Areas ◽  
Principal Component ◽  
Road Accident ◽  
Road Accidents ◽  
Segmentation Method ◽  
Rural Roads ◽  
Spatial Unit ◽  
Police Report ◽  
Accident Data ◽  
Grid Based

Identifying road accident blackspots is an effective strategy for reducing accidents. The application of this method in rural areas is different from highway and urban roads as the latter two have complete geographic information. This paper presents (1) a novel segmentation method using grid clustering and K-MEDOIDS to study the spatial patterns of road accidents in rural roads, (2) a clustering methodology using principal component analysis (PCA) and improved K-means to create recognition of road accident blackspots based on segmented results, and (3) using accidents causes in police report to analyze recognition results. The proposed methodology will be illustrated by accident data in Chinese rural area in 2017. A grid-based partition was carried on by using intersection as a basic spatial unit. Appended hazard scores were then added to the segments and using K-means clustering, a result of similar hotspots was completed. The accuracy of the results is verified by the analysis of the cause extracted by Fuzzy C-means algorithm (FCM).

scholarly journals Uncovering the behaviour of road accidents in urban areas

2020 ◽  
Vol 7 (4) ◽  
pp. 191739
Author(s):  
C. Cabrera-Arnau ◽  
R. Prieto Curiel ◽  
S. R. Bishop
Keyword(s):  
Time Series ◽  
Population Size ◽  
Urban Area ◽  
Rural Areas ◽  
Urban Areas ◽  
Road Accident ◽  
Road Accidents ◽  
Accident Data ◽  
The Relationship ◽  
Degrees Of Severity

Different patterns in the incidence of road accidents are revealed when considering areas with increased levels of urbanization. To understand these patterns, road accident data from England and Wales is explored. In particular, the data are used to (i) generate time series for comparison of the incidence of road accidents in urban as opposed to rural areas, (ii) analyse the relationship between the number of road accidents and the population size of a set of urban areas, and (iii) model the likelihood of suffering an accident in an urban area and its dependence with population size. It is observed that minor and serious accidents are more frequent in urban areas, whereas fatal accidents are more likely in rural areas. It is also shown that, generally, the number of accidents in an urban area depends on population size superlinearly, with this superlinear behaviour becoming stronger for lower degrees of severity. Finally, given an accident in an urban area, the probability that the accident is fatal or serious decreases with population size and the probability that it is minor, increases sublinearly. These findings promote the question as to why such behaviours exist, the answer to which will lead to more sustainable urban policies.

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.

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 Diagnosis on Transport Risk Based on a Combined Assessment of Road Accidents and Watershed Vulnerability to Spills of Hazardous Substances

2018 ◽  
Vol 15 (9) ◽  
pp. 2011 ◽  
Author(s):  
Emerson Machado ◽  
Renato Valle Junior ◽  
Teresa Pissarra ◽  
Hygor Siqueira ◽  
Luís Sanches Fernandes ◽  
...  
Keyword(s):  
Risk Management ◽  
Road Accident ◽  
Hazardous Substances ◽  
Road Accidents ◽  
Prevention Measures ◽  
Management Tools ◽  
Accident Data ◽  
Development And Validation ◽  
Soil And Water Contamination

Roads play an important role in the economic development of cities and regions, but the transport of cargo along highways may represent a serious environmental problem because a large portion of transported goods is composed of dangerous products. In this context, the development and validation of risk management tools becomes extremely important to support the decision-making of people and agencies responsible for the management of road enterprises. In the present study, a method for determination of environmental vulnerability to road spills of hazardous substances is coupled with accident occurrence data in a highway, with the purpose to achieve a diagnosis on soil and water contamination risk and propose prevention measures and emergency alerts. The data on accident occurrences involving hazardous and potentially harmful products refer to the highway BR 050, namely the segment between the Brazilian municipalities of Uberaba and Uberlândia. The results show that many accidents occurred where vulnerability is high, especially in the southern sector of the segment, justifying the implementation of prevention and alert systems. The coupling of vulnerability and road accident data in a geographic information system proved efficient in the preparation of quick risk management maps, which are essential for alert systems and immediate environmental protection. Overall, the present study contributes with an example on how the management of risk can be conducted in practice when the transport of dangerous substances along roads is the focus problem.

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 Contactless Automated Express Evaluation of Damages to a Car Body by Visual Parameters

2019 ◽  
Vol 18 (6) ◽  
pp. 471-475
Author(s):  
M. Tarasovа ◽  
N. Filkin ◽  
R. Yurtikov
Keyword(s):  
Road Safety ◽  
Human Factor ◽  
Road Accident ◽  
The Body ◽  
Road Accidents ◽  
Car Body ◽  
The Road ◽  
Accident Data ◽  
Established Fact ◽  
Time Required

Explosive development of computer technologies and their availability made it possible to extensively focus nowadays on emerging state-of-the-art technologies, digitalization, artificial intelligence, and automated systems, including in the field of road safety. It would be reasonable to implement some technical devices in this respect to remove human factor and automate some procedures completed at the scene of a road accident. Automatically filled up road accident inspection records and, mainly, diagrams of the accident will reduce time required for the examining inspector and remove human factor. Ultimately, an automated road accident data sheet is suggested to be established. To tackle the issues above requires a technique to determine whether the produced damages to the car body result from the same road accident. The fact remains that there are circumstances when even vehicle trace examination would not do the job, in case of multiple corrosive damage to the body. In view of the above, a technique designed to determine whether the damages produced are caused at the same point of time gains its ground. A technique for a time-related corrosion examination is offered herein to cut expenditures for diagnostics and expert examination of road accidents. That will also eliminate the matters of argument with respect to the road accident evaluation in court. Among added benefits of the technique are that it is simple, quick to implement, and requires no human involvement. It is a well-established fact that each chemical element or a mixture of substances has its own timeinvariant color attributes which allows to determine availability of one or another substance during corrosion of metal surfaces, by emission from the surface in question.

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 Statistical Analysis and Development of Accident Prediction Model of Road Safety Conditions in Hisar City

2021 ◽  
Vol 889 (1) ◽  
pp. 012034
Author(s):  
Keshav Bamel ◽  
Sachin Dass ◽  
Saurabh Jaglan ◽  
Manju Suthar
Keyword(s):  
Traffic Accidents ◽  
Linear Regression Analysis ◽  
Severity Index ◽  
Road Accident ◽  
P Value ◽  
Road Accidents ◽  
Accident Prediction ◽  
New Information ◽  
Margin Of Error ◽  
Accident Data

Abstract The severity of road accidents is a big problem around the world, particularly in developing countries. Recognizing the major contributing variables can help reduce the severity of traffic accidents. This research uncovered new information as well as the most substantial target-specific factors related to the severity of road accidents. T-stat, P-value, Significance and other test values are determined to check the dependency of dependent variable on independent variable in order to obtain the most significant road accident variables. In this research, a comparative analysis of accident data from Hisar and Haryana are compared. According to the findings, Haryana’s accident severity index (46.20) was higher in 2019 than Hisar’s (36.01), while Hisar had fewer accidents per lakh population (33.34) than Haryana (38.40). The outcomes of the study were used to develop an effective and precise accident predicting model is developed for Hisar city and state Haryana using a statistical method. Four models were created using linear regression analysis, two each for Hisar and Haryana. These models produce good results with a margin of error that is within acceptable bounds (0-5%), allowing them to be used to predict future traffic accidents and deaths.

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.

An Effective Methodology for Road Accident Data Collection in Developing Countries

2014 ◽  
pp. 103-114
Author(s):  
Muhammad Adnan ◽  
Mir Shabbar Ali
Keyword(s):  
Developing Countries ◽  
Data Collection ◽  
Police Department ◽  
Road Accident ◽  
Common Phenomenon ◽  
Road Accidents ◽  
Initial Information ◽  
Unique Data ◽  
Success Stories ◽  
Accident Data

Underreporting of road accidents has been widely accepted as a common phenomenon. In many developing countries this remains a critical problem as inappropriate information regarding road accidents does not provide a base to analyse its root causes. Therefore, effectiveness of implemented interventions are always questionable. In Pakistan, responsibility of collecting initial information regarding road accidents lies with the Police Department; however, reported figures are reflecting underestimation of the situation. This chapter reports the effectiveness of prevailing approaches for recording accident information in developing countries like Pakistan, India and Bangladesh, etc. Furthermore, it presents a unique methodology that has been adopted in Karachi for recording road accident information through an institute established on the notions of public-private partnership. Various features of that unique data collection mechanism are presented along with the discussion of some success stories, where the collected data has contributed significantly in improving road safety conditions.

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