Investigating the effects of monthly weather variations on Connecticut freeway crashes from 2011 to 2015

被引:31
作者
Zhao, Shanshan [1 ]
Wang, Kai [1 ]
Liu, Chenhui [2 ]
Jackson, Eric [1 ]
机构
[1] Univ Connecticut, Connecticut Transportat Inst, Connecticut Transportat Safety Res Ctr, 270 Middle Turnpike,Unit 5202, Storrs, CT 06269 USA
[2] CNR, Turner Fairbank Highway Res Ctr, 6300 Georgetown Pike, Mclean, VA 22101 USA
关键词
Motor vehicle traffic; Motor vehicle crash; Weather; Crash by type; Crash by severity; PREDICTION MODEL; TIME; ACCIDENTS; IMPACTS; SAFETY;
D O I
10.1016/j.jsr.2019.09.011
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Introduction: The objective of this research is to investigate the effects of monthly weather conditions on traffic crash experience on freeways, considering the interactions between weather, traffic volumes, and roadway conditions. Methods: Data from the state of Connecticut from 2011to 2015 were used. Random parameters negative binomial models with first-order, autoregressive covariance were estimated for representative types of freeway crashes (front-to-rear, sideswipe-same-direction, and fixed-object), most severe crashes (i.e., fatal and injury crashes), and non-injury crashes (i.e., property-damage-only crashes). Results: Major findings are that variations in monthly traffic volumes, roadway geometry, and weather conditions explain much of the variations in monthly traffic crashes. Time effects exist in the panel monthly data for all types of crashes. Taking into account this effect improves model prediction results. When the raw weather measures are highly correlated, using dimension reduction techniques helps to extract more interpretable weather factors. By considering the interaction effects between roadway condition variables, additional findings were found. In general, lower temperature, more heavy fog days, decreased precipitation, lower wind speed, higher monthly traffic volumes, and narrower inside shoulder were found to be associated with higher monthly crashes. The effects of area type and outside shoulder width change dramatically as the number of through lanes changes. Practical applications: The findings of this research could help researchers and general readers gain a better understanding of the effects of monthly weather conditions and other roadway factors on freeway crashes and give engineers practical guidelines on improving freeway safety. (C) 2019 National Safety Council and Elsevier Ltd. All rights reserved.
引用
收藏
页码:153 / 162
页数:10
相关论文
共 38 条
[1]   Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data [J].
Abdel-Aty, Mohamed A. ;
Pemmanaboina, Rajashekar .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2006, 7 (02) :167-174
[2]   Real-time assessment of fog-related crashes using airport weather data: A feasibility analysis [J].
Ahmed, Mohamed M. ;
Abdel-Aty, Mohamed ;
Lee, Jaeyoung ;
Yu, Rongjie .
ACCIDENT ANALYSIS AND PREVENTION, 2014, 72 :309-317
[3]   Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data [J].
Ahmed, Mohamed M. ;
Abdel-Aty, Mohamed ;
Yu, Rongjie .
TRANSPORTATION RESEARCH RECORD, 2012, (2280) :51-59
[4]   A note on modeling vehicle accident frequencies with random-parameters count models [J].
Anastasopoulos, Panagiotis Ch. ;
Mannering, Fred .
ACCIDENT ANALYSIS AND PREVENTION, 2009, 41 (01) :153-159
[5]  
[Anonymous], 2006, IMPACT DRIVER INATTE
[6]  
[Anonymous], 2010, HIGHWAY SAFETY MANUA
[7]  
[Anonymous], CLIM DAT ONL
[8]   WIND-INDUCED ACCIDENTS OF ROAD VEHICLES [J].
BAKER, CJ ;
REYNOLDS, S .
ACCIDENT ANALYSIS AND PREVENTION, 1992, 24 (06) :559-575
[9]   Studying the effect of weather conditions on daily crash counts using a discrete time-series model [J].
Brijs, Tom ;
Karlis, Dimitris ;
Wets, Geert .
ACCIDENT ANALYSIS AND PREVENTION, 2008, 40 (03) :1180-1190
[10]  
Cai Q., 2017, ACCIDENT ANAL PREVEN