Profiling of High-Frequency Accident Locations by Use of Association Rules

2003 ◽  
Vol 1840 (1) ◽  
pp. 123-130 ◽  
Author(s):  
Karolien Geurts ◽  
Geert Wets ◽  
Tom Brijs ◽  
Koen Vanhoof
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Traffic Safety ◽  
High Frequency ◽  
Traffic Accidents ◽  
Low Frequency ◽  
Large Data ◽  
Data Mining Algorithm ◽  
Road Accidents ◽  
Black Spots

In Belgium, traffic safety is one of the government's highest priorities. The identification and profiling of black spots and black zones (geographical locations with high concentrations of traffic accidents) in terms of accident-related data and location characteristics must provide new insights into the complexity and causes of road accidents, which, in turn, provide valuable input for governmental actions. Association rules were used to identify accident-related circumstances that frequently occur together at high-frequency accident locations. Furthermore, these patterns were analyzed and compared with frequently occurring accident-related characteristics at low-frequency accident locations. The strength of this approach lies with the identification of relevant variables that make a strong contribution toward obtaining a better understanding of accident circumstances and the discerning of descriptive accident patterns from more discriminating accident circumstances to profile black spots and black zones. This data-mining algorithm is particularly useful in the context of large data sets for road accidents, since data mining can be described as the extraction of information from large amounts of data. The results showed that human and behavioral aspects are of great importance in the analysis of frequently occurring accident patterns. These factors play an important role in identifying traffic safety problems in general. However, the accident characteristics that were the most discriminating between high-frequency and low-frequency accident locations are mainly related to infrastructure and location.

scholarly journals Improving Association Rule Mining Using Clustering-Based Data Mining Model for Traffic Accidents

2021 ◽  
Vol 8 (3) ◽  
pp. 65-70
Author(s):  
Mohamad Mohamad Shamie ◽  
Muhammad Mazen Almustafa
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Traffic Accidents ◽  
Clustering Algorithms ◽  
Large Data ◽  
Association Rules Mining ◽  
Transactional Databases ◽  
Accident Data ◽  
New Research

Data mining is a process of knowledge discovery to extract the interesting, previously unknown, potentially useful, and nontrivial patterns from large data sets. Currently, there is an increasing interest in data mining in traffic accidents, which makes it a growing new research community. A large number of traffic accidents in recent years have generated large amounts of traffic accident data. The mining algorithms had a great role in determining the causes of these accidents, especially the association rule algorithms. One challenging problem in data mining is effective association rules mining with the huge transactional databases, many efforts have been made to propose and improve association rules mining methods. In the paper, we use the RapidMiner application to design a process that can generate association rules based on clustering algorithms.

EFFECTIVE APPROACH TO DETECTING ROADS FOR PRIORITY PLANNING FOR ROAD SAFETY MEASURES

2019 ◽  
pp. 52-57
Author(s):  
Liydmila Nagrebelna ◽  
Olga Belenchuk ◽  
Oleksii Petrashenko
Keyword(s):  
Traffic Safety ◽  
Road Safety ◽  
Traffic Accidents ◽  
Safety Management ◽  
Complex Problem ◽  
Road Accident ◽  
Road Accidents ◽  
Safety Measures ◽  
Limit Values ◽  
Black Spots

The basic approaches for identifying dangerous road sections for prioritizing road safety measures are outlined. The effectiveness of the result depends on how well the areas where the road safety measures need to be implemented are identified. Suggestions for identifying dangerous places on the roads according to the statistics of traffic accidents using the methods of probability theory are given. On the basis of the analysis of statistics on roads with different number of adventures, limit values of the admissible number of adventures on sections of roads of different length are established. It is proved that it is necessary to create a comprehensive approach to solving a complex problem – improving road safety. Оne of the important approaches for the definition of dangerous road sections according to the data of road accidents, which is proposed by the authors, is the method of detection of sections (places) of concentration of road accidents (black spots). The purpose of this article is to: introduce an approach in road safety management to reduce the number of road accidents and the severity of their consequences on Ukraine’s highways by first implementing measures to improve road conditions and improve road organization. The effectiveness of the result in reducing the number of traffic accidents depends on the areas so clearly identified that, in the first place, it is necessary to implement measures to improve road safety. That is why this approach was introduced in traffic safety management. The purposeful financing of measures, aimed primarily at eliminating such sites, will help to reduce the number of road accidents and the severity of their consequences. Keywords: road safety, methods of analysis, dangerous road sections, place of concentration of road accidents, black spots, road accident.

scholarly journals Road Transport Accidents in Nigeria and the Role of Automotive Active Safety System

2016 ◽  
Vol 11 (1) ◽  
pp. 49-54
Author(s):  
Olasunkanmi Oriola Akinyemi ◽  
Hezekiah O Adeyemi ◽  
Olusegun Jinadu
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Transportation Network ◽  
Road Transport ◽  
Road Accident ◽  
Active Safety ◽  
Road Accidents ◽  
Road Transportation ◽  
Road Traffic Safety

Abstract Analysis of road traffic accidents revealed that most accidents are as a result of drivers’ errors. Over the years, active safety systems (ASS) were devised in vehicle to reduce the high level of road accidents, caused by human errors, leading to death and injuries. This study however evaluated the impacts of ASS inclusions into vehicles in Nigeria road transportation network. The objectives was to measure how ASS contributed to making driving safer and enhanced transport safety. Road accident data were collected, for a period of eleven years, from Lagos State Ministry of Economic Planning and Budget, Central Office of Statistics. Quantitative analysis of the retrospective accident was conducted by computing the proportion of yearly number of vehicles involved in road accident to the total number of vehicles for each year. Results of the analysis showed that the proportion of vehicles involved in road accidents decreased from 16 in 1996 to 0.89 in 2006, the injured persons reduced from 15.58 in 1998 to 0.3 in 2006 and the death rate diminished from 4.45 in 1998 to 0.1 in 2006. These represented 94.4 %, 95 % and 95 % improvement respectively on road traffic safety. It can therefore be concluded that the inclusions of ASS into design of modern vehicles had improved road safety in Nigeria automotive industry.

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

scholarly journals Personalized web search on e-commerce using ontology based association mining

2017 ◽  
Vol 7 (1.1) ◽  
pp. 286
Author(s):  
B. Sekhar Babu ◽  
P. Lakshmi Prasanna ◽  
P. Vidyullatha
Keyword(s):  
Data Mining ◽  
Web Search ◽  
Large Data ◽  
Association Mining ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Web Data ◽  
Data Mining Technique ◽  
Web Data Mining ◽  
The Web

 In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

scholarly journals Road Traffic Accidents Analysis Using Data Mining Techniques

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
G. Janani ◽  
N. Ramya Devi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
Rule Mining ◽  
Data Mining Techniques

Road Traffic Accidents (RTAs) are a major public concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury. Most of the analysis of road accident uses data mining techniques which provide productive results. The analysis of the accident locations can help in identifying certain road accident features that make a road accident to occur frequently in the locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. Data analysis has the capability to identify different reasons behind road accidents. In the existing system, k-means algorithm is applied to group the accident locations into three clusters. Then the association rule mining is used to characterize the locations. Most state of the art traffic management and information systems focus on data analysis and very few have been done in the sense of classification. So, the proposed system uses classification technique to predict the severity of the accident which will bring out the factors behind road accidents that occurred and a predictive model is constructed using fuzzy logic to predict the location wise accident frequency.

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Asep Budiman Kusdinar ◽  
Daris Riyadi ◽  
Asriyanik Asriyanik
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Data Mining Techniques ◽  
Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While  the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is  to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.  

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

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