Methods for Identifying Truck Crash Hotspots
Joint Authors
Qu, Wenrui
Liu, Shaojie
Zhao, Qun
Qi, Yi
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The goal of this study was to develop a new method for identifying the actual risky spots by using the geographic information system (GIS).
For this purpose, in this study, three different methods for detecting hotspots are developed, i.e., (1) the annual average daily traffic (AADT) normalization method, (2) AK crashes (A is the incapacitating crash, and K is the fatal crash) percentage method, and (3) distribution difference method.
To evaluate the performances of these three hotspot detection methods along with a baseline method that only considered the frequency of crashes, we applied these three methods to identify the top 20 hotspots for truck crashes in two representative areas in Texas.
The results indicated that (1) all three proposed methods produced more reasonable results than the baseline method, and (2) the “distribution difference” method outperformed the other methods.
American Psychological Association (APA)
Qu, Wenrui& Liu, Shaojie& Zhao, Qun& Qi, Yi. 2020. Methods for Identifying Truck Crash Hotspots. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1175367
Modern Language Association (MLA)
Qu, Wenrui…[et al.]. Methods for Identifying Truck Crash Hotspots. Journal of Advanced Transportation No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1175367
American Medical Association (AMA)
Qu, Wenrui& Liu, Shaojie& Zhao, Qun& Qi, Yi. Methods for Identifying Truck Crash Hotspots. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1175367
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175367