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Density-based clustering for road accident data analysis

Now days, Road accidents due to traffic are increasingly being recognized as key issue for transportation agencies as well as common people. A considerable unexpected output of transportation systems is road accidents with injuries and loss of lives. In order to suggest safe driving, precise study of road traffic data is serious to discover elements that are related to mortal accidents. In this research paper, we discover factors behind road traffic accidents problem solving by data mining algorithms together with DBSCAN and Parallel Frequent mining algorithm. We initially divide the accident places into k clusters depends on their accident frequency with DBSCAN algorithm. Next, parallel frequent mining algorithm is apply on these clusters to disclose the association between dissimilar attributes in the traffic accident data for realize the features of these places and analyzing in advance them to spot different factors that affect the road accidents in different locations. The main objective of accident data is to recognize the key issues in the area of road safety. The efficiency of prevention accidents based on consistency of the composed and predictable road accident data using with appropriate methods. Road accident dataset is used and implementation is carried by using Weka tool. The outcomes expose that the combination of DBSCAN and parallel frequent mining explores the accidents data with patterns and expect future attitude and efficient accord to be taken to decrease accidents.


*The
Road traffic accidents are a key issue concern for transportation leading traders as well as common people. Road accidents are damage the public life with multi-level of injuries (Kumar and Toshniwal, 2015). The number of factors that influence these incidents like Environmental conditions, motorway design, and type of accident, driver characteristics, and vehicle attributes (Karlaftis and Tarko, 1998). The key objective of accident data analysis recognizes the major parameters associated to road traffic accidents (Savolainen et al., 2011). However, various natures of accident data generate the task analysis is tough context. The key problem in this accident data analysis disturbs the human life. Thus heterogeneity have to be measured during data analysis (Depaire et al., 2008), a few correlation between the data may remain out of sight. Although, researchers used partition of the data to decrease this heterogeneity using few measures such as professional knowledge, but there is no security that will guide to a best possible partition which consists of similar type of clusters of road accidents. Data partition has been used broadly to overcome this dissimilarity of the accident data (Kumar and Toshniwal, 2015).
In order to provide safe driving instructions, cautious road traffic of statistics is critical to discover variables that are related to mortal accidents. Data analysis has the ability to recognize the various logics behind road accidents (Ma and Kockelman, 2006). In this paper, we are building data mining methods to make out high-frequency accident places and additional data to identify the different factors that influence road accidents at different locations. We initially split the accident places into m clusters with the support of accident frequency via DBSCAN clustering algorithm (Jones et al., 1991). The frequent pattern mining algorithm is imposed on these for expose the connection between dissimilar attributes of accident data with dissimilar places. Hence, our major accent will be the understanding of the results. The key idea of this research inspect the responsibility of human, vehicle, and infrastructure-with correlated factors in accident sternness by applying data mining learning techniques on road accident information (Miaou and Lum, 1993). Fig. 1 shows proposed system architecture.
Road accidents are major issue of fatality and disability across the world. Road accident can be considered as an event in which a vehicle bumps with other vehicle, person or other objects. A road accident not only provides property damage but it may lead to partial or full disability and sometimes can be fatal for human being. Increasing of road accidents is not a fine sign for the safety transportation. The only solution requires for accident data analysis to know diverse reasons of road accidents corresponding preventive measures taken. Different research studies used various techniques to examine road accident data by data mining methods and offer fruitful outcomes. Different other research use data mining methods to simplify road accident data and data mining methods are novel and superior to classical statistical techniques. Although, both the techniques offer good results that surely helpful for traffic accident prediction, expose that different road accident information exists and should be detached prior to the analysis of data. They also suggested that use of suitable clustering techniques to the analysis of accident data reduces the accidents and reveal the hidden data. TRW (2014) presented in its account every year; there are 0.4 million accidents in India, this is huge accident rate. This statement shows that there is a negative tendency of accidents from 2012 to 2013; however, as accidents are erratic and can take place in any type of conditions, there is no security that this leaning will assist in future also. Kononov and Janson (2002) declared that efficient connection between accident frequency and additional variables such as geometry of road, road side situations, traffic in sequence and vehicle position can assist to increase effective accident avoidance. Lee et al. (2002) presented that statistical design were a fine choice for analyze road accidents with geometric factors. Chen and Jovanis (2000) stated that analyzing huge dimensional datasets using classic statistical methods may outcome in certain issues such as inadequate data in great contingency tables. Statistical models have own design specific assumptions these can lead to few erroneous outcomes. Due to these drawbacks of statistical methods, data mining methods are being used to examine road accidents. Data mining is a combination of methods to extract new and unseen information from huge datasets. Barai (2003) discussed different ways of data mining in transportation like road accident data analysis. Several data mining methods like clustering algorithms, classification and association rule mining are broadly used for road accidents data analysis (Tan, 2006).
The rest of paper is prepared as follows: Section 2 states brief description road accident problems are analyzed in facts. In section 3, propose road accident data framework. Section 4 presents a comparison on road accident analysis techniques. Conclusion of the study is presented in section 5.

Reasons for road accidents
Different reasons for road accidents are: 1. Road Users -lack of care, High speed rash driving, abuse of traffic rules, sleep, fatigue and alcohol etc. 2. Vehicle -Defects such as brakes failure, steering system of vehicle, tire burst, and lighting system. 3. Road Condition -Skidding surface of roads, pot holes on roads. 4. Road design -imperfect geometric design of roads, insufficient breadth of roads, awkward curve design, improper traffic maintenance and poor lighting. 5. Environmental factors -critical weather conditions like smoke, snow, mist and heavy rainfall which bound the normal visibility and makes driving is not safe (https://www.coursehero.com).
Figs. 2 and 3 display road accidents in India, 2017. It clearly shows that Road user's mistakes are the most important factor accountable for accidents. Drivers fault for 79% of total accidents in 2017. Within in the type of drivers fault, accidents are exceeding lawful speed accounted for a large share of 55.6%. The environmental conditions and road design problems appears to be trivial; they account only 1.9% and 2.6% of total accidents. The reason of accidents due to defects in road condition and motor vehicle condition is negligible comparison with drivers fault. They accounted only 1.8% and 1.4% of total road accidents.

Road accidental injuries and deaths
There has been advance in road accidental deaths in India over the last few years. Road accidental deaths have increased 9 times, from 14,500 in 1970 to 138,400 in 2017. In comparison to 2005, injuries in 2017 are superior by 53,000 and 87,000, respectively. Table 1 represents all about information. From 2005 to 2013, fatalities have increased of 5% rate per year while the population of the country has larger than before at rate of 1.4% per year. Consequently, road accidental deaths per one lack people, has greater than before 8.9 in 2005 and 11.2 in 2017. In India fatality risk is very high level compared with developed countries. This type of risk in India is high than in the United Kingdom, Sweden. Road accidental deaths occurred due to one lack vehicles, as of 87.5 in 1970 to 8.6 in 2017, it is still quite high compare with developed countries.

Road accidental distribution based on age, time and sex
The road accident distribution based on age (statistics are not mentioned) is clearly shows that the most creative age group, 25-40 years, is the flat to road accident fatality in India. Age group of 20-42 years comprises 23% of Indian population, faces roughly 37% of total road accidents. During the previous 10 years from 2005 to 2017, number of fatalities faced by this age cluster has also improved significantly. The middle age (30-40) group 12% of the total population, but fatality faces 21%. So age group 30-59 years, the inexpensively energetic age group, is the most susceptible population cluster in India. Half of the road accidents are faced by this group of people which counts for less than 1/3 of the entire population. Sex wise allocation of injuries and road accidental deaths in India for the year 2005 and 2017 presents that the males for 85.2% of all fatalities 81.1% of injuries in 2017. Past 10 years, total number of fatalities by males has improved by 68.3%.

Materials and methods
Data pre-processing is the primary step for remove noise from given input. In second phase attribute selection done by DBSCAN algorithm. parallel frequent mining algorithm is apply on these clusters to disclose the association between dissimilar attributes in traffic accident data for realize the features of these places and analyzing in advance those to spot different factors that affect the road accidents. Finally visualize the patterns of performance evaluation as shown in the Fig. 4.

Fig. 4: Proposed system architecture
Cluster analysis split the data components into different groups in a way that maximizes the homogeneity of components within the different clusters. This technique is known as an unsupervised learning algorithm as the accurate number of clusters and their shapes are unknown. Generally, cluster analysis is a procedure of repetitively maximizing the intra cluster components. These similarity-based clustering methods calculate similarity using a specific distance function and measures for components with qualitative. The wellknown among similarity-based method is densitybased approach (Madhulatha, 2012).

Density-based road accident analysis
DBSCAN is a density-based clustering algorithm and it is designed to overcome large data sets with noise and is capable of determining different sizes and shapes. Density-based means that cluster are connected points where the density of points is equal to or more than a threshold. If the density is less than the threshold, the data are considered as noise. When a data set is given, DBSCAN divides it into segments of clusters and a set of noise points (https://algorithmicthoughts.wordpress.com). The density threshold condition is that there should be at least MinPts number of points in ε-neighborhood. Clusters contain core points and boundary points. A core point is a point that meets the density condition, and a boundary point is a point that does not meet the density condition but is close enough to one or more core point's ε-neighborhood. Points that are not core points or boundary points are considered as noise. Below is the pseudo code, prepared as functions for road accident data analysis. The function of regionQuery ( ) proceeds the points within the n-dimensional sphere. The function expandCluster ( ) returns for every points in the sphere, the DBSCAN algorithm is presented below in The idea behind DBSCAN and its developments is the notion that points are assigned to the similar group if they are density-reachable from every other cluster. To know this model, we will go through the definitions used in DBSCAN and associated algorithms. Clustering starts with dataset E containing a set of point's p ∈ E. DBSCAN estimates the density around a point using the concept ofneighborhood (http://technodocbox.com).
1. -Neighborhood. The -neighborhood, N (a), of a data point p is the set of points within a specified radius around p.
where d is some distance measure and ∈ R +. Note that the point p is always in its own -neighborhood, i.e., a ∈ M (a) always holds. Following this definition, the size of the neighborhood |M (a)| can be seen as not normalized kernel density estimate around p using a uniform kernel and a bandwidth of . DBSCAN uses minPts, detect dense areas for classify the points in a dataset into core, border, or noise points of the cluster. That is, p is a core point and q is in its neighborhood.
4. Density-reachable, A point p is reach to density from q if their exist in E in sequence of points (a1, a2, ..., an) with b = a1 and a = an such that ai+1 directly density reach from ai ∀ i 2 {1, 2, ..., n − 1}. 5. Connected density, A point a ∈ E is connected density to a point b ∈ E if there is a point o ∈ E, both a and b is density-reachable from o.
In the DBSCAN algorithm, core points of the same cluster, self-governing of the sequence in which the points in the dataset are computed. It is dissimilar for all border points in a cluster. Border points might be density-reachable from core points in clusters and the algorithm assigns them to the primary of these clusters computed which depends on assemble of the data points and the execution of the algorithm (http://technodocbox.com).

Parallel frequent association mining
Association rule mining is an extremely popular data mining method that extracts attractive and hidden relations between dissimilar attributes in a huge dataset. Association rule mining generates different rules that illustrate the underlying patterns in the dataset. The FP-growth algorithm using for the issue of discovery frequent patterns recursively add the suffix. This algorithm uses minimum frequent items as a suffix; it is well selection for the process reduce the search cost and extracts the frequent

add P to cluster C for each point P' in sphere_points if P' is not visited mark P' as visited sphere_points' = regionQuery(P', epsilon) if sizeof(sphere_points') >= min_points sphere_points = sphere_points joined with sphere_points' if P' is not yet member of any cluster add P' to cluster C regionQuery (P, epsilon): return all points within the n-dimensional sphere centered at P with radius epsilon (including P).
Output: k cluster groups patterns (Pandya and Rustom, 2017). The FP growth algorithm is shown in Fig. 6.
P(K) is the probability of the label which is assumed constant, given by NK/T where NK is the number of images of the class K, and T is the total number of images across all classes (www.cse.iitm.ac.in).
We guess equal number of training data items for all classes i.e., NKi = NKj. the above assumption P (FPi | K) can be rewritten as / NK. The probability of observing a frequent pattern P (FPi) is NFPi / T i.e., the number of data items on which FPi fired regardless of the label, separated by the total number of images (www.cse.iitm.ac.in).
Substituting all of these in the above equation we have, The above outcome displays that for testing a label, the operator sets should be ordered according to the ascending order of . This is for an operator set to get a better score in this phase, either the frequency of observing the operator set FPi for the particular label is high or that the probability of the operator set FPi firing for other classes is less (www.cse.iitm.ac.in).
There are different clustering algorithms exist in the literature. The objective of clustering algorithm is to partition the data into different clusters such that the objects within a group are similar to every other object in other clusters are diverse from each other. DBSCAN clustering method, after that we can use FP growth algorithm of association rule mining for computing the clusters in Fig. 7.
Data pre-processing is the primary step for remove noise from given dataset. Next level attributes selection done by DBSCAN algorithm. It can be constructing as a groups based on attributes. parallel frequent mining algorithm is apply on these clusters to disclose the association between dissimilar attributes in traffic accident data for realize the features of these places and analyzing in advance those to spot different factors that affect the road accidents. Finally visualize the patterns of performance evaluation.

Frequent Pattern Mining Algorithm Algorithm FP-growth (FPT, S, P) // FPT -Tree on Frequent Items // S-Minimum Support and P-Current Item set Suffix. Begin
1.

If FPT is a single path do 2.
For every C of nodes in path do a. Inform all patterns C ∪ P; Else b.
For every item i in FPT do Begin i. Produce pattern Pi = set i ∪ P; ii.
Inform pattern Pi as frequent; End 3.
Use pointer to extract condition prefix paths for item one; 4.
Construct conditional Frequent Pattern Tree FPTi from condition 5.
From prefix paths after eliminating infrequent items; 6.
If ( Table 2 shows the sample road accident dataset with different parameters of road surface, Lightening conditions, weather conditions, casualty class, sex of casualty, age of casualty and type of vehicle. These parameters most helpful for finding the reasons behind the accidents on roads. In order to suggest safe driving, precise study of road traffic data is serious to discover elements that are related to mortal accidents.

Results and discussion
A diversity of data mining methods, algorithms and tools are proposed for road traffic accident data analysis accident location tracking, prediction and identification of different contributory factors that affect the accident cruelty levels. Garib et al. (1997) they have been construct statistical design using stepwise regression analysis method for guessing incident duration.
The result analysis displays that over 85% of differences in occurrence duration can be predicted by the eight factors implicated in the regression model. DBSCAN and Frequent pattern mining algorithms are used for clustering, and the following clusters are constructed.
Cluster 1 represents the traffic clusters in such a way accidents occur because of high traffic. Cluster 2 represents the time of accident cluster in which accidents happen during day and night time. Cluster 3 represents the age of the drivers cluster. Cluster 4 presents the accident occurred every month. Cluster 5 states the weather condition at the time of accident. Cluster 6 is the lightening condition issue on the roads. Cluster 7 describes about type of accident the road condition. Cluster 8 describes the speed limit of vehicles at the time of accident. Fmeasure is used for cluster analysis because it throughput node-based analysis using the following equations.
Cluster based analysis findings and road accident dataset analysis are compared. The outcome reveal that the mixture of DBSCAN clustering and frequent pattern mining is extremely inspirational as it generates important data that would remain hidden, if no partition has been performed prior to produce frequent item sets. Weka is data mining software that uses a collection of machine learning algorithms. These algorithms can be applied directly to the data. Table 3 shows data mining algorithms, Comparison for road accident analysis of different methodologies, classifiers and their result. Fig. 8 shows the graphical representation of Table  3 values. Table 3 statistical results prove the DBSCAN with combination of FP growth generates better results compare to other methods. In this combination of methodology datasets with altering densities are tricky. So they can be working aggressively up to datasets are not alter.

Conclusion
Data mining has been verified as a reliable method in analyzing road accident data. So many authors used data mining method for analyzing road accident data of different countries. The data mining methods like association rule mining, clustering and classification are broadly used recognized multiple reasons that affect the serious of road accidents. In this scenario present safe driving suggestions and careful road traffic data analysis is dangerous to discover factors that are strongly related to destructive accidents. In this research, we locate so many factors behind road accidents, these accidents are analysis by using data mining algorithms like DBSCAN and Parallel Frequent mining algorithm. We initially split the accident places into k clusters based on their frequency of accident results by means of DBSCAN algorithm. Next, parallel frequent mining algorithm is exposing the association between dissimilar attributes in accident data, when it is applied on clusters. Understand the features of these places and additionally analyzing them to recognize different factors affect the road accidents at different locations. The major objectives of road accident data are scrutiny to recognize the key issues in the area of road safety. The efficiency of accident avoidance depends considerably on the reliability of composed and estimated data and the appropriateness of the methods. Road accident dataset is used and execution is carried by using Weka tool. The outcomes reveal that dataset for road accident and its analysis using DBSCAN and FP mining algorithm demonstrate that this procedure can be reused on new accident data with extra attributes to recognize different factors connected with road accidents.