PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Analyzing rear-end crash severity for a mountainous expressway in China via a classification and regression tree with random forest approach

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
To understand the contributory factors to rear-end accident severity on mountainous expressways, a total of 1039 rear-end accidents, occurring on G5 Jingkun Expressway from Hechizhai to Qipanguan in Shaanxi, China over the period of 2012 to 2017, were collected, and a non-parametric Classification and Regression Tree (CART) model was used to explore the relationship between severity outcomes and driver factors, vehicle characteristics, roadway geometry and environmental conditions. Then the random forest model was introduced to examine the accuracy of variable selection and rank their importance. The results show that driver’s risky driving behaviours, vehicle type, radius of curve, angle of deflection, type of vertical curve, time, season, and weather are significantly associated with rear-end accident severity. Speeding and driving while drunk and fatigued are more prone to result in severe consequences for such accidents and driving while fatigued is found to have the highest fatality probability, especially during the night period (18:00-24:00). The involvement of heavy trucks increases the injury probability significantly, but decreases the fatality probability. In addition, adverse weather and sharp curve with radius less than 1000 m are the most risk combination of factors. These findings can help agencies more effectively establish stricter regulations, adopt technical measures and strengthen safety education to ensure driver's driving safety on mountainous expressways for today and tomorrow.
Rocznik
Strony
591--604
Opis fizyczny
Bibliogr. 14 poz., il., tab.
Twórcy
  • Chang’an University, College of Transportation Engineering, Shaanxi, China
autor
  • Chang’an University, College of Transportation Engineering, Shaanxi, China
Bibliografia
  • [1] L.Wang, et al., “Driver injury severity analysis of crashes in a western China’s rural mountainous county: taking crash compatibility difference into consideration”. Journal of Traffic and Transportation Engineering (English Edition), 2020, DOI: 10.1016/j.jtte.2020.12.002.
  • [2] Y. Wang and C.G. Prato, “Determinants of injury severity for truck crashes on mountain expressways in China: a case-study with a partial proportional odds model”. Safety Science, vol. 119, pp. 100-107, 2019, DOI: 10.1016/j.ssci.2019.04.011.
  • [3] Y. Wang, Y. Luo, and F. Chen, “Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China”. European Transport Research Review, vol. 11, article 26, 2019, DOI: 10.1186/s12544-019-0366-4.
  • [4] Y. Wang, H. Zhang, and N. Shi, “Factors contributing to the severity of heavy truck crashes: a comparative study of Jiangxi and Shaanxi, China”. Jordan Journal of Civil Engineering, vol. 15, no. 1, pp. 41-51, 2021.
  • [5] M. I. Sameen and B. Pradhan, “Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS”. Geomatics, Natural Hazards and Risk, vol. 8, no. 2, pp. 733-747, 2017, DOI: 10.1080/19475705.2016.1265012.
  • [6] A. Ahmadi, et al., “Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods”. Journal of Transportation Safety & Security, vol. 12, no. 4, pp. 522-546, 2020, DOI: 10.1080/19439962.2018.1505793.
  • [7] Z. Sun, et al., “Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenariobased discretization”. PLoS ONE, vol. 15, no. 8, article e0237408, 2020, DOI: 10.1371/journal.pone.0237408.
  • [8] C. Chen, at al., “A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes”. Accident Analysis & Prevention, vol. 80, pp. 76-88, 2015, DOI: 10.1016/j.aap.2015.03.036.
  • [9] W.Y. Loh, “Classification and regression trees”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 14-23, 2011, DOI: 10.1002/widm.8.
  • [10] A.T. Kashani, R. Rabieyan, and M.M. Besharati, “A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers”. Journal of Safety Research, vol. 51, pp. 93-98, 2014, DOI: 10.1016/j.jsr.2014.09.004.
  • [11] R. Harb, et al., “Exploring precrash maneuvers using classification trees and random forests”. Accident Analysis & Prevention, vol. 41, no. 1, pp. 98-107, 2009, DOI: 10.1016/j.aap.2008.09.009.
  • [12] L. Breiman, “Random forests”. Machine Learning, vol. 45, no. 1, pp. 5-32, 2001, DOI: 10.1023/A:1010933 404324.
  • [13] Y. Peng, et al., “Investigation on the injuries of drivers and copilots in rear-end accidents between trucks based on real world accident data in China”. Future Generation Computer Systems, vol. 86, pp. 1251-1258, 2018, DOI: 10.1016/j.future.2017.07.065.
  • [14] X. Li, et al., “A rear-end collision risk assessment model based on drivers’ collision avoidance process under influences of cell phone use and gender - a driving simulator based study”. Accident Analysis & Prevention, vol. 97, pp. 1-18, 2016, DOI: 10.1016/j.aap.2016.08.021.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a52d109-fab9-46f8-b6ac-36cd944ba8b2
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.