Clustering Algorithms to Analyze the Road Traffic Crashes
Mahnaz Rafia Islam, Israt Jahan Jenny, Moniruzzaman Nayon, Md. Rajibul, Islam, Md Amiruzzaman, M. Abdullah-Al-Wadud
TL;DR
This paper evaluates clustering techniques for analyzing road traffic crashes, recommending DBSCAN and OPTICS for better identification of accident-prone areas based on real-world data.
Contribution
It compares various clustering methods and advocates for DBSCAN and OPTICS as more effective solutions for traffic crash analysis.
Findings
DBSCAN and OPTICS outperform other clustering methods in accuracy and efficiency
Real-life North Carolina accident data validates the effectiveness of the recommended algorithms
Improved identification of accident-prone zones facilitates targeted traffic safety interventions
Abstract
Selecting an appropriate clustering method as well as an optimal number of clusters in road accident data is at times confusing and difficult. This paper analyzes shortcomings of different existing techniques applied to cluster accident-prone areas and recommends using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) to overcome them. Comparative performance analysis based on real-life data on the recorded cases of road accidents in North Carolina also show more effectiveness and efficiency achieved by these algorithms.
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Traffic and Road Safety · Human Mobility and Location-Based Analysis