Data Mining and Visualization to Understand Accident-Prone Areas

Rizvee, Md. Mashfiq; Amiruzzaman, Md; Islam, Md. Rajibul

doi:10.1007/978-981-16-0586-4_12

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…In fact, traffic accident data analysis is really challenging because there are many different causes and types of accidents in various situations [5,6]. This leads to many valuable information being hidden while analyzing traffic accident data sets if only traditional analysis methods are used.…”

Section: Introductionmentioning

confidence: 99%

“…Determining accident hotspots can be aided by clustering accident locations [5,8]. The authorities may then be able to evaluate each of those hotspots independently and implement efficient preventive measures with the support of that hotspot analysis [9].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, drivers can avoid accidents if they know hotspot locations [10]. The accuracy of hotspot clusters affects awareness, caution, safety, and accident avoidance [5,8,10].…”

Section: Introductionmentioning

confidence: 99%

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Applying a two-step cluster algorithm in traffic accident data analysis

Khanh Giang,

Ho Thi Lan,

Van Manh

et al. 2024

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Cluster analysis is often employed as the initial stage in organizing heterogeneous data into homogeneous groups. Choosing an effective clustering approach and an ideal number of clusters in a traffic accident dataset might be complex and challenging. This study aims to evaluate the effectiveness of k-means and two-step cluster methods. Subsequently, the two-step cluster method and GIS are applied to analyze the traffic accident datasets from 2015 to 2017 in Hanoi, Vietnam. First, according to the Silhouette score, the two-step cluster method achieved a higher score of 0.563, while the k-means method scored 0.341. A higher Silhouette score indicates more well-defined clusters. Second, the research suggests combining the two-step cluster method with GIS for analyzing traffic accident datasets. The outcome identifies five typical types of accidents in Hanoi. In addition, the locations of various accident types were visually illustrated on a map, enabling traffic officials to recommend precise and urgent countermeasures. Importantly, the clustering results reveal that the two-step cluster method exhibits a significantly higher rate of homogeneous data in the clusters compared to the k-means method. This study demonstrates that the two-step cluster method is not only more effective than the k-means method in terms of clustering ability but also in data pre-processing. The study's results enable authorities to gain a more detailed understanding of typical traffic accident patterns in Hanoi. Besides, the employed methods could potentially be applied to other regions, providing an additional avenue for analysis

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying a two-step cluster algorithm in traffic accident data analysis

Khanh Giang,

Ho Thi Lan,

Van Manh

et al. 2024

View full text Add to dashboard Cite

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“…Indeed, the variety of factors and accident kinds that occur in various contexts makes it difficult to analyze traffic accident datasets [13,14]. Generally, because of the variety of traffic accident data such as it can include both numerical and categorical types, some important relationships may be obscured.…”

Section: Introductionmentioning

confidence: 99%

“…Identifying the locations of highfrequency accidents, or "hotspots", is one of the most crucial tasks in the effort to decrease the number of traffic accidents [25][26][27][28]. The locations of traffic accident hotspots can be determined via the locations of accident clusters [13,29]. Subsequent analysis of these hotspots enables traffic administrators to evaluate them more accurately aiming to prevent fatalities in hotspots and to establish suitable preventive solutions aiming to improve road safety [30].…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Le,

Tran,

2023

Sustainability

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Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

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