Methods for Identifying Truck Crash Hotspots
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 m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/1751350 |
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| _version_ | 1832566464086802432 |
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| author | Wenrui Qu Shaojie Liu Qun Zhao Yi Qi |
| author_facet | Wenrui Qu Shaojie Liu Qun Zhao Yi Qi |
| author_sort | Wenrui Qu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-29705940714648d9b5b33cbbccba0175 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-29705940714648d9b5b33cbbccba01752025-02-03T01:04:03ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/17513501751350Methods for Identifying Truck Crash HotspotsWenrui Qu0Shaojie Liu1Qun Zhao2Yi Qi3School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), University Road 3501, Changqing District, Jinan, Shandong, ChinaDepartment of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, Texas 77004-9986, USADepartment of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, Texas 77004-9986, USADepartment of Transportation Studies, Texas Southern University, 3100 Cleburne Street, Houston, Texas 77004-9986, USAThe 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.http://dx.doi.org/10.1155/2020/1751350 |
| spellingShingle | Wenrui Qu Shaojie Liu Qun Zhao Yi Qi Methods for Identifying Truck Crash Hotspots Journal of Advanced Transportation |
| title | Methods for Identifying Truck Crash Hotspots |
| title_full | Methods for Identifying Truck Crash Hotspots |
| title_fullStr | Methods for Identifying Truck Crash Hotspots |
| title_full_unstemmed | Methods for Identifying Truck Crash Hotspots |
| title_short | Methods for Identifying Truck Crash Hotspots |
| title_sort | methods for identifying truck crash hotspots |
| url | http://dx.doi.org/10.1155/2020/1751350 |
| work_keys_str_mv | AT wenruiqu methodsforidentifyingtruckcrashhotspots AT shaojieliu methodsforidentifyingtruckcrashhotspots AT qunzhao methodsforidentifyingtruckcrashhotspots AT yiqi methodsforidentifyingtruckcrashhotspots |