The Role of Data Visualization and Analytics of Highway Accidents

Abstract

Thailand has been ranked as one of the most dangerous countries in terms of death from road accidents, representing ineffective road safety policies. The crucial mission of the Thai government is to provide safety and reduce accidents for road users on the highway system. This paper aims to explore the potential of using Business Intelligence (BI) in accident analysis. The availability of open accident data provides an opportunity for the BI, which can provide an advanced platform for conducting data visualization and analytics in both spatial and temporal dimensions in order to illustrate when and where the accidents occur. The accident data and provincial data were combined by using the Talend Data Integration tool. The combined data was then loaded into a MySQL database for data visualization using Tableau. The dashboard was designed and created by using Tableau as an analytical visualization tool to provide insights into highway accidents. This system is advised to be adopted by the Thai government, which can be used for data visualization and analytics to provide a mechanism to formulate strategy options and formulate appropriate contingency plans to improve the accident situation.

Road Crash Data Visualisation and Analytics Using Tableau for Mountainous Roadway Areas in Cameron Highlands, Malaysia

“…Research findings are presented in the following section for better insight and discussion. Sunkpho & Wipulanusat (2020) identified that many transport agencies adopt Tableau software as the visual analytics platform. The following indicate five (5) descriptive and frequency analysis types analysed in this research using Tableau software.…”

“…Concerning road crash issues, few studies were conducted using secondary data focusing on the mountainous road network (e.g. Shaadan et al, 2021;Sunkpho & Wipulanusat, 2020). A study comparing mountainous and non-mountainous crash characteristics in Sabah, Malaysia found among factors that increase the odds of crashes along mountainous roads compared with non-mountainous roads including horizontal curve sections, single-vehicle crashes and weekend crashes (Rusdi et al, 2017).…”

“…However, limited studies examined specific area road crashes and crash characteristics. Therefore, user-friendly and efficient analysis techniques are essential for understanding big data and gaining meaningful insights regarding data structure, trends, causes, impact, and patterns (Sunkpho & Wipulanusat, 2020). Thus, this research uses data analytics and visualization technique to explore road crash data in Cameron Highlands compared to non-mountainous areas to increase knowledge and understanding about road crash scenarios and patterns, particularly in rural and urban areas.…”