Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones
The Empirical Analysis of Beijing
Bibliographic Data
| ID | 15468302 |
|---|---|
| Authors | Zhiyuan Sun (0000-0002-4724-7508, Beijing University of Technology), Jianyu Wang (0000-0001-9111-1846, Tsinghua University, corresponding author), Yanyan Chen (0000-0002-7198-6044, Beijing University of Technology), Huapu Lu (0000-0003-1408-3161, Tsinghua University) |
| Year | 2018 |
| Volume | 15 |
| Issue | 12 |
| Pages | 2722-2722 |
| Publication date | 2018-12-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph15122722 |
| PMID | 30513896 |
| OpenAlex | W2902794664 |
| Language | EN |
| Citations received | 7 |
| References cited | 22 |
The objective of this study was to identify influence factors on injury severity of traffic accidents and discuss the differences in urban functional zones in Beijing. A total of 3982 sets of accident data in Beijing were analyzed from the perspective of whole city and different urban functional zones. From the aspects of accident attribute, occurrence time, infrastructure, management status, and environmental condition, the influence factors set of injury severity of traffic accidents in Beijing are set up in this paper, which include 17 influence factors. Based on Pearson's chi-squared test, factors are preselected. On the basis of binary logistic regression analysis, the impact of the value of influence factors on injury severity of traffic accidents is calibrated. Based on classification and regression tree analysis, the impact of influence factors is analyzed. Through Pearson's chi-squared test and binary logistic regression analysis, it is found that there are similarities and differences among different urban functional zones. There are two common influence factors, including accident type and cross-section position, and six personalized influence factors, including lighting conditions, visibility, signal control, road physical isolation facility, occurrence period and road type, and the other nine weak influence factors. The results of binary logistic regression analysis and classification and regression tree analysis are basically the same. The factors that should be paid attention to in different urban functional zones and the value of the factors that need special attention are determined by synthesizing two methods
Beijing · China · Environmental health · Geography · Injury prevention · Occupational safety and health · Poison control · Suicide prevention · Agriculture and Farm Safety · Human Factors and Ergonomics · Injury Epidemiology and Prevention · Medicine · Traffic and Road Safety
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| Citations per year | 1.17 |
|---|---|
| Citation span | 2020 - 2021 (2) |
| Highly cited | No |