Road Traffic Accident Black Spot Determination by Using Kernel Density Estimation Algorithm and Cluster Statistical Significant Evaluation (9824) |
| Khanh Giang Le (Viet Nam), Pei Liu and Liang-Tay Lin (Chinese Taipei) |
Mr. Khanh Giang Le
PhD Student
Ph.D program of Civil and Hydraulic Engineering
College of Construction and Development
Feng Chia University, Taiwan
NA
Hanoi
Viet Nam
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| Corresponding author Mr. Khanh Giang Le (email: khanhgiang298[at]gmail.com, tel.: NA) |
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| [ abstract ] [ paper ] [ handouts ] |
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Published on the web 2019-02-28
Received 2018-10-01 / Accepted 2019-02-01 |
| This paper is one of selection of papers published for the FIG Working Week 2019 in Hanoi, Vietnam and has undergone the FIG Peer Review Process. |
FIG Working Week 2019
ISBN 978-87-92853-90-5 ISSN 2307-4086
https://www.fig.net/resources/proceedings/fig_proceedings/fig2019/index.htm
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Abstract |
| Determining road collision black spot locations plays an important role in reducing significantly the number of traffic accidents. The article presents a new procedure that identifies road traffic accident black spot locations by using GIS-based kernel density estimation algorithm, evaluates the statistical significance of resulting collision clusters, and then arranging them in accordance with their significance. The results of the paper show that the approach was effective and exact in identifying road traffic accident black spot in Hanoi, Vietnam, simultaneously these hot spots were ranked according to their level of dangerousness. These outcomes will not only enable transport authorities to know comprehensively the reasons for each collision but also to help them manage and deal with hazardous areas according to the prior order in case of limited expense and allocate traffic safety sources suitably. |
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| Keywords: Geoinformation/GI; Cartography; Risk management; Traffic Accident (TA), Black Spots (BS), Geographic Information System (GIS), Kernel Density Estimation (KDE), Local Moran’s I. |
