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Abstract
ABSTRACT This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 − 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi’s weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.
Highlights
Road Traffic Accident (RTA) is one of the most complicated issues over the world
The aim of our study is to present an advanced process of identifying RTA hotspots
The article has shown that the combined analysis of space and time in identifying RTA hotspots enables traffic authorities to capture the situation accurately and timely
Summary
Road Traffic Accident (RTA) is one of the most complicated issues over the world. The locations, which are identified by a high accident occurrence compared with the other locations, are known as hotspots or black spots (Dereli and Erdogan 2017). Past studies show that the occurrences of RTA are not random in space and time. These locations identified by several key factors such as geometric design, traffic volume, surroundings, or severe weather conditions, etc. In order to effectively build accident preventive plans, it is really vital to determine potential dangerous locations associated with accident occurrence time (Harirforoush 2017)
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