EVALUVATINGROAD TRAFFIC ACCIDENTS USING DATAMININGTECHNOLOGY ABSTRACT:-Roadtraffic safety is an important perturbation for government transportauthorities as well ascommon people.
Road accidents are ambivalent and not ableto be predict the incidents. Andtheir survey requires the information affectingthem. Road accidents cause difficulties which are get bigger at an alarmingrate. Controlling the traffic accidents on roads is a crucial task.
To givesafe driving suggestions, clear and careful study of roadway traffic data iscritical to find out the variables that are nearly to fatal accidents.Increasing the number of vehicles from past few years has put lot of pressureon the existing roads and ultimately resulting in increasing the road accidents.A road traffic accident is any harm due to collision originating from,terminating with or involving a vehicle partially or fully on a public road.I. INTRODUCTION:- In modern life, accidents havebecome daily happening. Every day we hear the news of theaccident on thetelevision, or through internet .During accident many people die at the spot, someothers may injured very severely.
By witnessing an accident one can understandthe horror of it. There are several reasons for road accidents, some of themare increasing the number of vehicles, careless driving, violating trafficrules etc. Whenever a road accident occur there are various types of damagetakes place ,which could be in the form of human beings, infrastructure whichis damage to the government and many other administration damages .
Poorroadway maintenance also contributes accidents. But still many people continueto neglect and ignore the danger involved in the accidents. In this paper weare analyzing some methods and algorithms to find out the problems occur inroad accidents.Section1 elucidate literature survey,Section 2 elucidate conclusion. LITERATURE SURVEYThe paper 1 describes the associationrule mining, its classifications and the atmospheric components like roadwaysurface, climate, and light condition do not strongly influence the fatal accidentrate. But the human factors like being alcoholic or not, and the impact havestrongly affect on the fatal accident rate. A common mechanism to recognize therelations between the data stored in huge database and plays a very significantrole in repeated object set mining is association rule mining algorithm. Aclassical association rule mining method is the Apriori algorithm whose mainaim is to identify repeated object sets to analyze the roadway traffic data.
Classification in data mining methodology focus atbuilding a classifier modelfrom a training data set that is used to classify records of unrevealed classlabels. The Naïve Bayes technique is one of the probability-based methods forclassification and is based on the Bayes’ hypothesis with the probability ofself-rule between every set of variables.The author applies statistics analysis and FatalAccident Reporting System(FARS) tosolve this problem. From the clustering result someregions have larger fatal rate but some others have smaller. When driving withinthose risky or dangerous states,people take more attention. When the taskperformed, data seems never to be sufficient to make a strong choice.
If non-fatalaccident data, weather condition data, mileage data, and so on are available,more test could be executed thus more advice could be made from the data.In paper 2, K-modes clusteringtechniqueis a framework that is used as an initialwork for divisionof differentroad accidents on road network. Then association rule mining are used torecognize the various situations that are related with the occurrence of anaccident for the entire data set (EDS) and the clusters recognized by K-modesclustering algorithm. Six clusters (C1toC6) are used based on propertiesaccident type, road type, lightning on road and road feature identified by Kmodes clustering method. On each cluster association rule mining is applied aswell as on EDS to create rules.
Powerful methods with higher raise values are takenfor the inspection. Rules for various clusters disclose the situations relatedwith the accidents within that cluster. These rules are compared with the rulescreated for the EDS and resemblance shows that association rules for EDS does notdisclose correct data that can be related with an accident. If more feature arepresented large information can be identified that is associated with anaccident. To buildup our methodology, we also performed analysis of all clustersand EDS on monthly or hourly basis. The results of analysis assist methodologythat performing clustering prior to analysis helps to identify better anduseful results that cannot obtained without using cluster analysis.The paper 3 performsstatisticaland empirical analysis on State Highways and Ordinary District Roads accidentaldatasets.
The need of the study is to analyze the traffic accident data of SH’sand ODR’s to assign the black spots and accidental elements, part to controlthe harm caused by the accidents. The basic necessity of the analysis is tocheck the traffic associated dataset through Exploratory VisualizationTechniques, K-means and KNN Algorithms using Rstudio.. The term accident blackspot in management of road traffic safety defines a place where accidents arebeen focus historically and to analyze the accidental data using exploratoryvisualization techniques and machine learning algorithms. These techniques andalgorithms are used on the traffic accidental dataset to get the desired outputin order to reduce the accident frequency. Exploratory Visualization Technique is atechnique to anatomize and examine the sets of data in order to abridge andencapsulate the important characteristics with visual and pictorial method.Exploratory Visualization analysis can be performed using scatter plot,correlation analysis, barplot, clustered barplot, histogram, pie chart etc.
Machinelearning concentrates on algorithm designing and makes predictions on sets ofdata. It includes Supervised (KNN Algorithm) and Unsupervised learning (K-meansAlgorithm).This paper present result by resembling the above three mining techniques and assigns the causeof accident, accident prone area, analyze the time of accident, examine thecause of accident and scrutinize the litigators vehicle.
In paper 4, describesabout a frame work that uses K-mode clustering technique as aprimary task fordividing 11574 accidents on road network of Dehradun (India) from 2009 to 2014.Then an association mining rule are used to find out the various context associatedwith instance of an accident for both the whole data set and clusters find out byK-modes clustering algorithm. Then compare the findings from cluster basedanalysis and entire data set. The results shows that the amalgamation of k modeclustering and association mining rule is very encouraging, as it producesimportant facts that would remain hidden if no segmentation has been performedprior to generate association rules. Also a trend analysis has been performedon each clusters and entire data set.
By trendanalysis it shows that beforeanalysis, prior segmentation of data is very important. This paper put forwarda frame work based on cluster analysis using k-mode algorithm and associationmining rule. By using cluster analysis as a primary task can group the data intodifferent homogeneous parts. It is the first time that both association andclustering rule are used together to analyze the data’s for road accidents. Theoutput of the study proves that by using cluster analysis as a primary task, itcan help in removing heterogeneity to some extent in the road accident data.)Based on attributes accident type, road type, lightning on road and roadfeature, K -modes clustering find six cluster (C1–C6).
Association mining rulehave been applied on each cluster as well as on entire data set to generaterules. For this analysis strong rules with high lift values are used.The paper 5describes purposeof data mining methods in the field of road accident investigation. . Associationrules are used to identify the patterns and rules that are subjected the causethe occurrence of road accidents. An efficient method for updating the indexyear after year could be designed.
Additionally, further analysis of trafficsafety data using data mining techniques are allowed.Cluster analysis evaluates data objects without consulting acommon class label. The objects are clustered or arranged on the basis of maximizingthe intra class similarity and minimizing the interclass similarity.
Outlieranalysis: A database having data objects that do not satisfies the generalbehavior or model of the data. These data objects are also called outliers.Evolution analysis which defines and models consistencies or trends forobjects whose behavior changes over time.We are currently build up byconsidering several issues, changes in clash occurrence may have someaftereffect for traffic safety measures in certain countries. The determinationof specific precautionary measures to overcome clashes requires study of otherfactors such as the identification of specific road sections that need work,etc..It analyzed the traffic accident using data mining technique that could possiblyreduce the fatality rate.
Using a road safety database enables to reduce thefatality by implementing road safety programs at local and national levels. The paper 6 describes data miningtechniques to analyze high-frequency accident locations and further identifydifferent factors that affect road accidents at specifying locations. We firstpartitioned the accident locations into k groups based on their accidentfrequency poll using k-means clustering algorithm. Association rule miningalgorithm is used to reveal the correlation between different elements in theaccident data and understand the characteristics of these locations. Hence, themajor significance will be the evaluation of the outcomes. Data mining has been provenas a reliable technique to analyzing road accident data.
Several data miningtechniques such as clustering, classification and association rule mining arewidely used in the literature to identify reasons that affect the severity ofroad accidents. It is the first time that k-means algorithm is used to identify high- andlow-frequency accident locations based on accident count as it provides sometechnical measures to divide the accident locations based on thresholdvalues.The road accident dataset and its analysis using k-means clustering and association rule mining algorithmshows that this approach can be reused on other accident data with moreattributes to identify various other factors associated with road accidents.In paper 7 describestheresults from analysis of traffic accidents on the Finnish roads by applyinglarge scale data mining methods. The set of data collected from road trafficaccidents are vast, multidimensional and diverse.The Finnish Road Administrationbetween 2004 and 2008 data was collected for this study.
This set of datacontain more than 83000 accidents and 1203 of which are fatal. The main aim ofthis is to examinethe usability of robust clustering, association and frequentitemsets, and visualization methods to the roadtraffic accident analysis. Theoutput shows that the pick out data miningmethods are able to produceintelligible patterns from the data, detectingmore information that could beincreased with more detailed and comprehensive data sets.Most of the fatalaccidents occur due to the condition of single roadway mainroads outside built-upareas where the permitted speed varies typically between 80-100km/h. Aged andyoung drivers have large contribution to the high risk accidents inhighways.Most of the surveys reported that one of the major reason for accidents amongyoung people are consumption of alcohol.
From the analysis it is understandthat failure of roads and end user groups are responsible for accidents atcertain limit. This paper 8 is to represent a Traffic Accident Reportand Analysis System (TARAS) through data mining using Clustering technique. Detectthe causes of accidents is the main aim of this paper. The transport departmentof government of India produced the dataset for the study contains trafficaccident records of the yearand look into the performance of J48. Theclassification accuracy on the test result discloses the three cases such asaccident, vehicle and casualty. Genetic Algorithms is used for the futureselection to lower the measurements of the dataset.
.More detailed area specificinformation from accident locations and circumstances are needed. With the helpof this paper, the analysis can be done and therefore preventive measures canbe taken.
It can help the government to keep track of records of the accidents,causes of accident, vehicle number, vehicle owner’s name and address.. With thecurrent data it is possible to identify the risky road segments and the roaduser groups responsible for accidents in certain environments. The viewer oruser can also make their own account for viewing the site .you can view thedata about causality .Our system will provide the graphical view of theaccidents with respect to the data entered into the system according to theperiod .
This system will provide the solutions as accidents causes. So thatwith the help of thissystem government can take the necessary actionsaccordingaccidents cases.1) Accurate Location ofaccident2) GPS integration3) Government ID Authenticationfor user Data4) Advanced Filtertechnique Accident Solutionprediction.The paper9 describes application of data miningtechniques on road accidents by usingmachine learning algorithms that determines accident rate in the future todecrease clash deaths and wounds.
The accident dataset contains traffic accidentreport of various cities examined by using machine learningalgorithms topredict the accident rate.It implemented hybrid approach that performed withhigheraccuracy rate as compared to other methods to be described. The machinelearning techniques is used for to reduce accidents and saves life.We have toexpand the classification accuracy of road trafficaccidents types; data qualityhas to be added.In paper 10 describesabout a method called Innovators Marketplace on Data Jackets. InnovatorsMarketplace on Data Jackets used to externalize the value of data through ally.
For analyzing the rate of traffic accidents on urban area methods such as factor analysis, structureequation modeling and data mining are used here. To construct traffic accidentrisk evaluation model different indexes such as total number of accidentsreported, fatality rate injury rate arecombined. To identify the connection between different factors populationstructure information, vehicle information, road characters are used. InHere wefocused on urban data, applied structural equation modeling to find outtheimportant factors associated with traffic accident. Important factors are population structure,vehicle information, structure of road etc. This paper describes six factors byconstructing an accident risk causal framework based on urban data andthecomponent factor sets of each feature and influence on traffic accident.CONCLUSIONIn this paper, we havecollected multiple researchers’ works together in single document as review anddiscussed about the contribution towards impact of road and traffic accident onhuman life and society.
This survey highlights the number of approaches used toavoid the accident happened in various countries and cities. The study on roadtraffic accident cause can identify the key factor rapidly and efficiently andprovide instructional methods to the traffic accident prevention and roadtraffic accident reduction, which could greatly reduce personal casualty andproperty loss by road traffic accidents. Meanwhile, it would be helpful forimproving the efficiency and security service level of the road transportationsystem. The paper also discussing about various data mining techniques which isproved supporting to resolve traffic accident severity problem and concludewhich one could be optimal technique in road traffic accident scenario. Thebrief survey will also help us to find better mining technique in this kind ofproblem.
In the expansion phase, it’s our endeavour to sketch better work toresolve traffic accident severity problem.REFERENCE1 Analysisof Road Traffic Fatal Accidents Using Data Mining TechniquesLiling Li,Sharad Shrestha, Gongzhu Hu2Analysing road accident data using associationrule miningSachin Kumar; DurgaToshniwa3BlackSpot and Accidental Attributes Identification on State Highways and OrdinaryDistrict Roads Using Data Mining Techniques.Gagandeep Kaur 4 A data mining framework to analyzeroad accident data Sachin Kumar, Durga Toshniwal5 Anoverview of data mining in road traffic and accident analysis K. Jayasudha Dr.
C.Chandrasekar6 A data mining approach tocharacterize road accident locations Sachin KumarEmail author,Durga Toshniwal7 Mining road trafficaccidents Sami Ayramo,PasiPirtala,Janne Kauttonen,Kashif Naveed,Tommi Karkk ainen8Traffic Accident Report Analysis using DataMining TechniquesMrs. Kanchan Gawande1 Ambikesh Pandey9 A Radical Approachto Forecast the Road AccidentUsing Data Mining TechniqueAnupamaMakkar ,HarpreetSingh Gill10 Evaluating model oftraffic accident rate on urban dataJianshi Wang,Yukio Ohsawa