Quantitative Estimation on the Safety Effect of Traffic Composition on Freeways

被引：0
|
作者：
Wen H. [1 ]
Sun J. [1 ]
Zeng Q. [1 ]
Zhang X. [1 ]
机构：
[1] School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510641, Guangdong
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2018年 / 46卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Conditional autoregressive model; Freeway; Spatial correlation; Traffic composition; Traffic safety;
D O I：
10.3969/j.issn.1000-565X.2018.06.001
中图分类号：
学科分类号：
摘要：
To analyze the impact of traffic composition on freeway safety deeply, the roadway, traffic and crash data on Kaiyang Freeway in Guangdong Province in 2014 were collected. The vehicles were classified into five categories according to the toll standard. A Bayesian hierarchical model and a conditional autoregressive (CAR) model were developed to correlate roadway-related and traffic-related attributes with crash frequency on freeway segments. Bayesian methods were used to estimate the parameters and to compare the models. The results of model comparison show that the CAR model, which accounts for the spatial correlation across adjacent freeway segments, outperforms the Bayesian hierarchical model. The parameter estimates in the CAR model suggest that there are 15.5% and 24.4% decreases in expected crash frequency on the freeway per 1% increase of Categories 1 (e.g., automobile) and 3 (e.g., medium coach and medium truck) vehicles, respectively. Moreover, crash frequency is found higher on longer freeway segments with more averaged daily traffic, bigger curvature and steeper grade. © 2018, Editorial Department, Journal of South China University of Technology. All right reserved.
引用
收藏
页码：1 / 7
页数：6
相关论文
共 24 条
  • [1] Gao Y., Dong X.-Y., Tian F., Present situation analysis on highway traffic safety and management countermeasures, Journal of Safety Science and Technology, 11, 10, pp. 110-115, (2015)
  • [2] Hu J.-B., Li R., Ma Y., Safety threshold evaluation method for expressway tunnel lighting in entrance section, China Journal of Highway and Transport, 27, 3, pp. 92-99, (2014)
  • [3] Lin S., Liu J.-B., Yan Y., Et al., Safety evaluation modelof long-steep downgrade section for mountain Highway Based on Driving workload, Journal of Traffic and Transportation Engineering, 13, 6, pp. 99-106, (2013)
  • [4] Guo Y.-Y., Liu P., Wu Y., Et al., Safety evaluation of mountain freeway tunnels' traffic operation environment, Journal of Wuhan University of Technology, 35, 7, pp. 53-58, (2013)
  • [5] Wu Y., Qian Z.-B., Wang J.-J., Et al., Traffic safety assessment and sensitivity analysis on expressway, Journal of Chang'an University(Natural Science Edition), 34, 4, pp. 134-141, (2014)
  • [6] Ahmed M., Huang H., Abdel-Aty M., Et al., Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway, Accident Analysis & Prevention, 43, 4, pp. 1581-1589, (2011)
  • [7] Yu R., Abdel-Aty M., Ahmed M., Bayesian random effect models incorporating real-Time weather and traffic data to investigate mountainous freeway hazardous factors, Accident Analysis & Prevention, 50, pp. 371-376, (2013)
  • [8] Ma X., Chen F., Chen S., Modeling crash rates for a mountainous highway by using refined-scale panel data, Transportation Research Record, 2515, pp. 10-16, (2015)
  • [9] Yu R., Xiong Y., Abdel-Aty M., A correlated random parameter approach to investigate the effects of weather conditions on crash risk for a mountainous freeway, Transportation Research Part C, 50, pp. 68-77, (2015)
  • [10] Fan T.-X., Wang X.-S., Modeling safety of urban arterials and identification of impact factors, Journal of Transportation Information and Safety, 34, 1, pp. 64-70, (2016)
123