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Safety investigation of traffic crashes incorporating spatial correlation effects
| Title: | Safety investigation of traffic crashes incorporating spatial correlation effects. |
 33 views 16 downloads |
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| Name(s): |
Alkahtani, Khalid, Author Abdel-Aty, Mohamed, Committee Chair Radwan, Essam, Committee Member Eluru, Naveen, Committee Member Lee, JaeYoung, Committee Member Zheng, Qipeng, Committee Member University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2018 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | One main interest in crash frequency modeling is to predict crash counts over a spatial domain of interest (e.g., traffic analysis zones (TAZs)). The macro-level crash prediction models can assist transportation planners with a comprehensive perspective to consider safety in the long-range transportation planning process. Most of the previous studies that have examined traffic crashes at the macro-level are related to high-income countries, whereas there is a lack of similar studies among lower- and middle-income countries where most road traffic deaths (90%) occur. This includes Middle Eastern countries, necessitating a thorough investigation and diagnosis of the issues and factors instigating traffic crashes in the region in order to reduce these serious traffic crashes. Since pedestrians are more vulnerable to traffic crashes compared to other road users, especially in this region, a safety investigation of pedestrian crashes is crucial to improving traffic safety. Riyadh, Saudi Arabia, which is one of the largest Middle East metropolises, is used as an example to reflect the representation of these countries' characteristics, where Saudi Arabia has a rather distinct situation in that it is considered a high-income country, and yet it has the highest rate of traffic fatalities compared to their high-income counterparts. Therefore, in this research, several statistical methods are used to investigate the association between traffic crash frequency and contributing factors of crash data, which are characterized by 1) geographical referencing (i.e., observed at specific locations) or spatially varying over geographic units when modeled; 2) correlation between different response variables (e.g., crash counts by severity or type levels); and 3) temporally correlated. A Bayesian multivariate spatial model is developed for predicting crash counts by severity and type. Therefore, based on the findings of this study, policy makers would be able to suggest appropriate safety countermeasures for each type of crash in each zone. | |
| Identifier: | CFE0007148 (IID), ucf:52324 (fedora) | |
| Note(s): |
2018-08-01 Ph.D. Engineering and Computer Science, Civil, Environmental and Construction Engineering Doctoral This record was generated from author submitted information. |
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| Subject(s): |
Multivariate spatial Macro-Level Analysis Crash type models Crash severity models Spatial correlation Bayesian models Poisson Lognormal CAR |
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| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0007148 | |
| Restrictions on Access: | public 2018-08-15 | |
| Host Institution: | UCF |
