scholarly journals LDA-CBOW-Based Mining Model for Risky Driving Behavior in Traffic Accidents

2021 ◽  
Vol 2138 (1) ◽  
pp. 012024
Author(s):  
Tuo Shi ◽  
Na Wang ◽  
Lei Zhang
Keyword(s):  
Traffic Management ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Driving Behavior ◽  
Text Data ◽  
Risky Driving ◽  
Short Text ◽  
Accident Data ◽  
Model Efficiency ◽  
Dependence Relationship

Abstract Traffic accident data of traffic management department is recorded in unstructured text form, which contains a large number of characteristic descriptions related to risky driving behavior. However, such data has short text length and abundant professional vocabulary. Many text mining techniques cannot effectively analyze such text data. This paper proposes an improved LDA algorithm based on CBOW—LDA-CBOW model for the study of traffic accident text data containing illegal behaviors. This model can better extract the topics of traffic accident data and filter the keywords under the corresponding topics, which provides a better way to study the dependence relationship between traffic data and illegal behaviors. Experiments show that compared to other models, this model can better extract related topics of traffic accident data with higher model efficiency and better robustness.

scholarly journals A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Lin Hu ◽  
Xingqian Bao ◽  
Hequan Wu ◽  
Wenguang Wu
Keyword(s):  
Cluster Analysis ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Driving Behavior ◽  
Accident Risk ◽  
Driving Style ◽  
Driving Experience ◽  
Accident Data ◽  
Highly Correlated

Traffic accidents are often related to the driver’s driving behavior, which is mainly decided by his or her characters. In order to explore the correlation of traffic accident risk with driver characters, the age, driving experience, and driving style were statistically analyzed based on the China In-Depth Accident Study (CIDAS) database. Taking the number of casualties in the accident as evaluation indicators, the grey cluster analysis was used to classify the drivers into four accident risk ranks: low, medium to low, medium to high, and high. The results show that drivers aged 18–30 years are more likely to induce accidents; drivers with 6–10 years of driving experience have the highest risk to accidents, followed by drivers with 4-5 years of driving experience; and the driving style is also highly correlated with accident risk tendency.

scholarly journals Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jinshuan Peng ◽  
Yiming Shao
Keyword(s):  
Neural Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Time Window ◽  
Driving Behavior ◽  
Practical Significance ◽  
Accurate Identification ◽  
Risky Driving ◽  
Bayesian Filter ◽  
Identification Rate

Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

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 Research on Risky Driving Behavior of Novice Drivers

2019 ◽  
Vol 11 (20) ◽  
pp. 5556
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Xiaoyan Zhu ◽  
Yule Luo ◽  
Yi Luo
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Risky Behaviors ◽  
Driving Behavior ◽  
Risky Driving ◽  
Novice Drivers ◽  
Driving Skills ◽  
Planned Behavior Theory

Novice drivers have become the main group responsible for traffic accidents because of their lack of experience and relatively weak driving skills. Therefore, it is of great value and significance to study the related problems of the risky driving behavior of novice drivers. In this paper, we analyzed and quantified key factors leading to risky driving behavior of novice drivers on the basis of the planned behavior theory and the protection motivation theory. We integrated the theory of planned behavior (TPB) and the theory of planned behavior (PMT) to extensively discuss the formation mechanism of the dangerous driving behavior of novice drivers. The theoretical analysis showed that novice drivers engage in three main risky behaviors: easily changing their attitudes, overestimating their driving skills, and underestimating illegal driving. On the basis of the aforementioned results, we then proposed some specific suggestions such as traffic safety education and training, social supervision, and law construction for novice drivers to reduce their risky behavior.

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 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 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 Learning Drivers’ Behavior From Social Networking

2019 ◽  
Vol 63 (1) ◽  
pp. 1792-1796
Author(s):  
Yueqing Li ◽  
Acyut Kaneria ◽  
Chao Qian ◽  
Brian Craig
Keyword(s):  
Economic Growth ◽  
Text Mining ◽  
Social Networking ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Driving Behavior ◽  
Daily Activities ◽  
Inexpensive Method ◽  
Traffic Incidents ◽  
The World

In today’s fast developing civilization, transportation plays an important part in people’s economic growth and daily activities. This study analyzes the driving behavior and accidents related to traffic accidents using Twitter tweets as a tool for text mining. Active users when encounter any traffic incidents, post instant messages on Twitter. Various analyses were performed on these tweets and was represented graphically using tableau analy-sis software and Rstudio. This method proved to be an effective and inexpensive method to study peoples’ real time approach on traffic accident throughout the world. It proved to be a strong approach towards learning traf-fic accident behaviors.

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.

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