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.

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.

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 Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

scholarly journals Identification of traffic accident risk-prone areas under low-light conditions

2015 ◽  
Vol 15 (9) ◽  
pp. 2059-2068 ◽  
Author(s):  
K. Ivan ◽  
I. Haidu ◽  
J. Benedek ◽  
S. M. Ciobanu
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Pearson Correlation ◽  
Injury Rate ◽  
Spatial Relation ◽  
Accident Risk ◽  
Light Conditions ◽  
Low Light ◽  
The Impact ◽  
The City

Abstract. Besides other non-behavioural factors, low-light conditions significantly influence the frequency of traffic accidents in an urban environment. This paper intends to identify the impact of low-light conditions on traffic accidents in the city of Cluj-Napoca, Romania. The dependence degree between light and the number of traffic accidents was analysed using the Pearson correlation, and the relation between the spatial distribution of traffic accidents and the light conditions was determined by the frequency ratio model. The vulnerable areas within the city were identified based on the calculation of the injury rate for the 0.5 km2 areas uniformly distributed within the study area. The results show a strong linear correlation between the low-light conditions and the number of traffic accidents in terms of three seasonal variations and a high probability of traffic accident occurrence under the above-mentioned conditions at the city entrances/exits, which represent vulnerable areas within the study area. Knowing the linear dependence and the spatial relation between the low light and the number of traffic accidents, as well as the consequences induced by their occurrence, enabled us to identify the areas of high traffic accident risk in Cluj-Napoca.

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 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 Analisis Perilaku Pengemudi Truk Serta Kontribusinya Pada Kecelakaan

2021 ◽  
Vol 23 (1) ◽  
pp. 79-87
Author(s):  
Budi Dwi Hartanto
Keyword(s):  
Path Analysis ◽  
Direct Effect ◽  
Human Error ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Driving Behavior ◽  
Truck Drivers ◽  
Accident Risk ◽  
Aggressive Driving ◽  
The Relationship

ABSTRAKIn Indonesia, the death rate due to road traffic accidents is still quite high, with some of these accidents involving trucks. Several studies stated that the main cause of traffic accidents is human error. Therefore, research related to the behavior of truck drivers and their contribution to accidents is necessary.There are four variables used in this study, namely green driver (X1), multitasking driving (X2), aggressive driving (Y), and accidents (Z). Path analysis is used to describe the relationship and influence between variables.The results of the analysis show that the green driver variable and the multitasking driving variable simultaneously have a direct effect on aggressive driving behavior, but the two variables have no direct effect on the level of accident risk. Green drivers and multitasking driving have an indirect effect on the level of accident risk through the level of aggressive driving behavior which functions as an intervening variable.ABSTRAKDi Indonesia tingkat kematian yang diakibatkan  kecelakaan lalu lintas jalan masih cukup tinggi, dimana sebagian dari kecelakaan tersebut melibatkan kendaraan angkutan barang (truk). Beberapa penelitian menyebutkan bahwa penyebab utama terjadinya kecelakaan lalu lintas adalah human error. Oleh sebab maka penelitian terkait dengan perilaku pengemudi truk serta kontribusinya pada kecelakaaan perlu untuk dilakukan.Terdapat empat variabel yang digunakan dalam penelitian ini yaitu variabel usia muda serta minim pengalaman (X1), mengemudi dalam kondisi multitasking (X2), mengemudi secara agresif (Y), dan potensi terjadinya kecelakaan (Z). Untuk menggambarkan hubungan dan pengaruh antar variabel digunakan analisis jalur (path analysis).Dari hasil analisis diketahui bahwa variabel usia muda serta minim pengalaman dan variabel mengemudi dalam kondisi multitasking secara simultan berpengaruh langsung terhadap perilaku mengemudi agresif, namun kedua variabel tidak berpengaruh langsung terhadap tingkat resiko kecelakaan. Usia muda serta minim pengalaman dan mengemudi dalam kondisi multitasking berpengaruh tidak langsung terhadap tingkat resiko kecelakaan melalui tingkat perilaku mengemudi agresif yang berfungsi sebagai variabel intervening

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.

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