scholarly journals K-Means Clustering untuk Data Kecelakaan Lalu Lintas Jalan Raya di Kecamatan Pelaihari

2018 ◽  
Vol 5 (5) ◽  
pp. 613 ◽  
Author(s):  
Winda Aprianti ◽  
Jaka Permadi
Keyword(s):  
Data Mining ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Distance Measure ◽  
Data Traffic ◽  
First Aid Training ◽  
Accident Data ◽  
Special Concern ◽  
Clustering Data ◽  
Minor Injuries

<p>Kecelakaan lalu lintas di jalan raya masih menjadi penyumbang tingginya angka kematian di Indonesia, sehingga menjadi perhatian khusus bagi kepolisian di negara ini. Termasuk Kepolisian Resor (Polres) Tanah Laut, yang telah membuktikan perhatian tersebut dengan membentuk komunitas korban kecelakaan lalu lintas dan Pelatihan Pertolongan Pertama Gawat Darurat (PPGD). Tahapan awal pencegahan kecelakaan lalu lintas adalah dengan mengetahui faktor-faktor penyebab kecelakaan lalu lintas yang diperoleh melalui analisa data kecelakaan. Analisa tersebut dapat dilakukan dengan data mining, yaitu <em>K-Means Clustering.</em> <em>K-Means Clustering</em> mengelompokkan data menjadi beberapa <em>cluster</em> sesuai karakteristik data tersebut. Data kecelakaan lalu lintas dibagi menjadi 2 dataset, yakni dataset 1 dan dataset 2. Hasil <em>cluster </em>penerapan <em>K-means clustering </em>terhadap dataset 1 dan dataset 2 kemudian dilakukan pengujian <em>silhoutte coefficient </em>untuk mencari hasil <em>cluster </em>dengan kualitas terbaik<em>. </em>Pengujian <em>silhoutte coefficient</em> secara berurutan menghasilkan <em>distance measure </em>paling optimal yakni <em>clustering </em>dengan 4 <em>cluster</em> untuk dataset 1 dan <em>clustering </em>dengan 2 <em>cluster</em> untuk dataset 2. Selain memperoleh <em>cluster </em>dengan kualitas terbaik, penganalisaan data juga menghasilkan beberapa informasi kecelakaan lalu lintas yang sering terjadi, yakni faktor penyebab dan korban kecelakaan adalah pengemudi, umur korban adalah 9 sampai 28 tahun, dan keadaan korban kecelakaan adalah luka ringan.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p><em>Traffic accidents on the highway are still contribute to the high mortality rate in Indonesia, which are becoming a special concern for the police. Including the Police of Tanah Laut Resort where prove themselves by established The Community of Traffic Accident Victims and Emergency First Aid Training. The first prevention of traffic accidents is knowing the factors causing traffic accidents which is obtained through the analysis of traffic accident’s data. It can be done through data mining, i.e. K-Means Clustering, which is clustering data into clusters according to characteristics of the data. Traffic accident data is divided into two datasets, namely dataset 1 and dataset 2. After obtaining the cluster results, the next step is to calculate silhoutte coefficient which is used to find the best quality cluster result. The result of testing silhoutte coefficient are clustering with 4 clusters for dataset 1 and clustering with 2 clusters for dataset 2. Analyzing data in this research also produces some information on traffic accidents that often occur, namely the causes and victims of accidents are drivers, the age of the victims is between 9 and 28 years old, and the circumstance of the accidents victims are minor injuries.</em></p>

scholarly journals Data Mining Untuk Mengetahui Tingkat Loyalitas Konsumen Terhadap Merek Kendaraan Bermotor dan Pola Kecelakaan Lalulintas di DIY

2011 ◽  
Vol 5 (3) ◽  
pp. 1
Author(s):  
Agus Sasmito Ariwibowo ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Brand Loyalty ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Police Department ◽  
Mining Method ◽  
Data Traffic ◽  
Rule Based ◽  
Accident Data ◽  
Using Data

Abstract— The data of vehicle sales and traffic accident can be processed into information that is important for vehicle dealers and the Police Department. Those important information researched are the level of consumer loyalty to the vehicle brands and to predict the vehicle’s brands that will be purchased by a consumer. The study also tries to analyze the traffic accident data to find out is there any link between the occurrence of an accident to a certain brand of vehicle.                This research implementing data mining method called ‘rule based classification’ to establish the sales of vehicles rules by which can be used to classify consumer into group level of brand loyalty and also estimate the brand of the next vehicle’s brand that will be purchased by the consumer. This research will process the data traffic accident by using data mining techniques called Apriori Method. Apriori Method is used to identify a pattern of accidents based on brand, type of vehicles, and the vehicle’s color. The results are used to estimate whether there is any correlation between the occurrences of a traffic accident to a particular brand.                The result can help companies or vehicle dealers to obtain information about the level of the consumer’s brand loyalty to the dealer’s brand and to predict the brand that the consumer would be buy for the next vehicle. The result can also help the Police Department to find out whether there is any correlation between the occurrence of traffic accidents to the brand, type and the color of vehicle. Keywords— rule based classification, apriori, brand loyalty, traffic accident.

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 Empirical Modelling for exploring the factors contributing to disability severity from road traffic accidents in Thailand

1970 ◽  
Vol 6 (2) ◽  
pp. 176-185
Author(s):  
Jaratsri Rungrattanaubol ◽  
Anamai Na-udom ◽  
Antony Harfield
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Empirical Modelling ◽  
Standard Data ◽  
Accident Data ◽  
Computer Based

This paper introduces a computer-based model for predicting the severity of injuries in road traffic accidents. Using accident data from surveys at hospitals in Thailand, standard data mining techniques were applied to train and test a multilayer perceptron neural network. The resulting neural network specification was loaded into an interactive environment called EDEN that enables further exploration of the computer-based model. Although the model can be used for the classification of accident data in terms of injury severity (in a similar way to other data mining tools), the EDEN tool enables deeper exploration of the underlying factors that might affect injury severity in road traffic accidents. The aim of this paper is to describe the development of the computer-based model and to demonstrate the potential of EDEN as an interactive tool for knowledge discovery.

scholarly journals MOBILE-WEB-BASE VOLUNTEERED GEOGRAPHIC INFORMATION APPLICATION AND GEOMETRIC ACCURACY ANALYSIS FOR TRAFFIC ACCIDENTS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 375-378
Author(s):  
H. K. Sevinc ◽  
I. R. Karas ◽  
E. Demiral
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Geographic Information ◽  
Google Earth ◽  
Geographical Information ◽  
Accuracy Analysis ◽  
Geometric Accuracy ◽  
Web Based ◽  
Mobile Web ◽  
Accident Data

Abstract. The users can contribute to geographic information through platforms such as Wikimapia and OpenStreetMap. They can also generate data by themselves with their applications in cyber worlds like Google Earth. This study is primarily designed to be a guide regarding Volunteered Geographical Information (VGI) and to evaluate the geometric accuracy of data collected from volunteers on application. The main purpose of this study is to present basic information about Volunteered Geographical Information (VGI), why users are tending to use VGI, the accuracy of the data entered by the user, to examine the examples of use in various fields, to learn about geographic information systems and to compare this phenomenon and also by developing a VGI application to examine the similarity between the actual data and the data collected from volunteer users. A mobile and web-based application have been developed to collect traffic accident data from volunteer users. The geometric accuracy analysis was performed by comparing the data collected with this application with the data obtained from the General Directorate of Security.

scholarly journals Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

2016 ◽  
Vol 28 (4) ◽  
pp. 415-424 ◽  
Author(s):  
Draženko Glavić ◽  
Miloš Mladenović ◽  
Aleksandar Stevanovic ◽  
Vladan Tubić ◽  
Marina Milenković ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Future Research ◽  
Practical Application ◽  
Accident Prediction ◽  
Accident Data ◽  
Road Characteristics ◽  
The Relationship

Over the last three decades numerous research efforts have been conducted worldwide to determine the relationship between traffic accidents and traffic and road characteristics. So far, the mentioned studies have not been carried out in Serbia and in the region. This paper represents one of the first attempts to develop accident prediction models in Serbia. The paper provides a comprehensive literature review, describes procedures for collection and analysis of the traffic accident data, as well as the methodology used to develop the accident prediction models. The paper presents models obtained by both univariate and multivariate regression analyses. The obtained results are compared to the results of other studies and comparisons are discussed. Finally, the paper presents conclusions and important points for future research. The results of this research can find theoretical as well as practical application.

scholarly journals Traffic accident analysis based on C4.5 algorithm in WEKA

2019 ◽  
Vol 272 ◽  
pp. 01035 ◽  
Author(s):  
Jiajia Li ◽  
Jie He ◽  
Ziyang Liu ◽  
Hao Zhang ◽  
Chen Zhang
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Depth Information ◽  
Highway Traffic ◽  
Safety Issues ◽  
C4.5 Algorithm ◽  
Accident Data ◽  
The Impact

At present, China is in a period of steady development of highways. At the same time, traffic safety issues are becoming increasingly serious. Data mining technology is an effective method for analysing traffic accidents. In-depth information mining of traffic accident data is conducive to accident prevention and traffic safety management. Based on the data of Wenli highway traffic accidents from 2006 to 2013, this study selected factors including time factor, linear factor and driver characteristics as research indicators, and established the decision tree using C4.5 algorithm in WEKA to explore the impact of various factors on the accident. According to the degree of contribution of each variable to the classification effect of the model, various modes affecting the type of the accident are obtained and the overall prediction accuracy is about 80%.

Analysis Traffic Accident Prone Areas Using Cluster Analysis in Bogor City Based on WebGIS

2021 ◽  
pp. 55-62
Author(s):  
Lulu Lutfi Latifah
Keyword(s):  
Cluster Analysis ◽  
Information System ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Cluster Algorithm ◽  
Geographical Information ◽  
Prone Area ◽  
Accident Data ◽  
The City ◽  
City Police

Traffic accidents are a problem that occurs in various regions in Indonesia, especially in the city of Bogor. Based on traffic accident data obtained from the Laka Unit, the Bogor City Police experienced fluctuating movements. The use of accident data is also not optimal. This makes it difficult to see areas that have a level of vulnerability. To solve this problem, in this study an analysis was made to determine the areas prone to traffic accidents by utilizing the Geographical Information System to map the distribution of locations. The method used to analyze the accident area is using the K-Means Cluster Algorithm. The results of the research conducted showed that the highest level of vulnerability from 2014 to 2019 was in the sub-district of Tanah Sareal on Jalan K.H. Sholeh Iskandar. Several incidents of laka occurred on curves, bypasses, and in and out of vehicles. The result of this research is in a beautiful traffic accident prone area in the form of WebGIS.

The Traffic Accidents Analysis Base on Association Rule Mining of Yuxi City in China

2014 ◽  
Vol 1030-1032 ◽  
pp. 2161-2165
Author(s):  
Shao Yun Song
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Traffic Accidents ◽  
Relationship Matrix ◽  
Rule Mining ◽  
Small Cities ◽  
Accident Data ◽  
Analysis System ◽  
The Relationship

According to the characteristics of small cities accident in China, selectively build data mining models. The algorithm focus on mining association rules to small cities accidents analysis system. Experiments show that the algorithm is superior to other algorithms. In this paper, the relationship matrix algorithm by using association rules on accident data, data mining, and mining results were analyzed to verify the effectiveness of the system.

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