Investigation of injury severity in urban expressway crashes: A case study from Beijing
Autoři:
Quan Yuan aff001; Xuecai Xu aff002; Junwei Zhao aff003; Qiang Zeng aff004
Působiště autorů:
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China
aff001; School of Civil Engineering and Mechanics, Huazhong University of Science and Technology Wuhan, China
aff002; School of Automobile, Chang’an University, Xi’an, China
aff003; School of Transportation, South China University of Science and Technology, Guangzhou, China
aff004
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227869
Souhrn
Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways.
Klíčová slova:
Autumn – Data management – Roads – Seasons – Traffic safety – Transportation infrastructure – Urban areas – Winter
Zdroje
1. Lord D, Mannering FL. The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transp Res Part A. 2010; 44(5): 291–305.
2. Savolainen PT, Mannering FL, Lord D. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accid Anal Prev. 2011; 43(5): 1666–1676. doi: 10.1016/j.aap.2011.03.025 21658493
3. Mannering FL, Bhat CR. Analytic methods in accident research: Methodological frontier and future directions. Analytic Meth Accid Res. 2014; 1: 1–22.
4. Chen F, Chen S. Injury severties of truck drivers in single- and multi-vehicle accidents on rural highway. AccidAnal Prev. 2011; 43(5):1677–1688.
5. Chen F, Song M, Ma X. Investigation on the injury severity of drivers in rear-end collisions between cars using a random parameters bivariate ordered probit model. J Env Res Pub Health. 2019; 16(14): 2632.
6. Sun J, Li T, Li F, Chen F. Analysis of safety factors for urban expressways considering the effect of congestion in Shanghai, China. Accid Anal Prev. 2016; 95(Pt B):503–511. doi: 10.1016/j.aap.2015.12.011 26721569
7. Al-Ghamdi AS. Using logistic regression to estimate the influence of accident factors on accident severity. Accid Anal Prev. 2002; 34:729–741. doi: 10.1016/s0001-4575(01)00073-2 12371778
8. Yu R, Abdel-Aty M. Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data. Accid Anal Prev. 2014; 62: 161–167. doi: 10.1016/j.aap.2013.08.009 24172082
9. Michalaki P, Quddus MA, Pitfield D, Huetson A. Exploring the factors affecting motorway accident severity in England using the generalized ordered logistic regression model. J Safety Res. 2015; 55: 89–97. doi: 10.1016/j.jsr.2015.09.004 26683551
10. Yoon S, Kho SY, Kim D. Effect of regional characteristics on injury severity in local bus crashes: use of hiercharcical ordered model. Transp Res Rec. 2017; 2647: doi: 10.3141/2647-01
11. Rezapour M, Moomen M, Ksaibati K. Ordered logistic models of influencing factors on single and multiple-vehicle downgrade crashes: A case study in Wyoming. J Safety Res. 2019; 68:107–118. doi: 10.1016/j.jsr.2018.12.006 30876502
12. Washington SP, Karlaftis MG, Mannering FL. Statistical and Econometric Methods for Transportation Data Analysis. 2nd ed. Washington, D.C.: CRC Press; 2011.
13. Xie Y, Zhang Y, Liang F. Crash injury severity analysis using Bayesian ordered probit models. J Transp Eng. 2009; 135(1):18–25.
14. Xu X, Xie S, Wong SC, Xu P, Huang H, Pei X. Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model. J Adv Transp. 2016; 50: 2015–2028.
15. Zeng Q, Sun J, Wen H. Bayesian hierarchical modeling monthly crash counts on freeway segments with temporal correlation. J Adv Transp. 2017; 1953: 1–8.
16. Yuan Q, Xu X, Xu M, Zhao J, Li Y. The role of striking and struck vehicles in side crashes between vehicles: Bayesian bivariate probit analysis in China. Accid Anal Prev. 2020, 134: 105324. Available from: https://doi.org/10.1016/j.aap.2019.105324. doi: 10.1016/j.aap.2019.105324 31648116
17. Abegaz T, Berhance Y, Worku A, Assrat A, Assefa A. Effects of excessive speeding and falling asleep while driving on crash injury severity in Ethiopia: A generalized ordered logit model analysis. Accid Anal Prev. 2014; 71: 15–21. doi: 10.1016/j.aap.2014.05.003 24866353
18. Jiang K, Park SH, Kang S, Song KH, Kang S, Chung S. Evaluation of pedestrian safety pedestrian crash hot spots and risk factors for injury severity. Transp Res Rec. 2013; 2393: 104–116.
19. Yuan Q, Chen H. Factor comparison of passenger-vehicle to vulnerable road user crashes in Beijing, China. Int J Crashworthiness. 2017; 22(3):260–270.
20. Morgan A, Mannering F. The effects of road-surface conditions, age, and gender on driver-injury severities. Accid.Anal. Prev. 2011; 43(5):1852–1863. doi: 10.1016/j.aap.2011.04.024 21658514
21. Eluru N, Bagheri M, Miranda-Moreno LF, Fu L. A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings. Accid Anal Prev. 2012; 47:119–127. doi: 10.1016/j.aap.2012.01.027 22342959
22. Ma C, Hao W, Xiang W, Yan W. The impact of aggressive dirving behavior on driver-injury severity at highway-rail grade crossings accidents. J Adv Transp. 2018; Available from: DOI: doi: 10.1155/2018/9841498
23. Yu R, Quddus M, Wang X, Yang K. Impact of data aggregation approaches on the relationships between operating speed and traffic safety. Accid Anal Prev. 2018; 120: 304–310. doi: 10.1016/j.aap.2018.06.007 30195137
24. Xiao D, Xu X, Duan L. Spatial-temporal analysis of injury severity with geographically weighted panel logistic regression model. J AdvTransp. 2019; Vol. 2019, Article ID 8521649. Available from: https://doi.org/10.1155/2019/8521649.
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Je libo čepici místo mozkového implantátu?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy