摘要Pedestrian safety remains a crucial issue, considering that pedestrian fatalities are increasing faster than motorist fatalities. In 2016, pedestrian fatalities reached nearly 6000 in the United States, which is the highest annual record of pedestrian traffic fatalities in more than two decades. In Louisiana, pedestrian fatalities reached 110 in 2015, nearly 15% of total traffic fatalities. In the same year, the pedestrian fatality rate per 100,000 populations in Louisiana reached a higher level (2.18) than the national average (1.67). While pedestrian crashes most frequently occur in urban areas, the fatal pedestrian crash rate is 1.5 in urban areas and 3.8 in rural areas, and the rural population is only 26.8%. To reduce pedestrian crashes, this paper presents an analysis of Louisiana pedestrian crashes (2006-2015) and investigates the pedestrian safety problem. In addition, the statistical relationship between pedestrian injury severity and contributing factors, including demographics, pedestrian behavior, and the built environment for urban and rural areas, is established using multinomial logit models. Fatal and severe crashes are strongly related to the alcohol or drug use and elderly age of pedestrians, regardless of their urban or rural locations. However, the pedestrian crashes in urban and rural areas have different characteristics. Variables such as pedestrian crossing/entering road away from intersections, walking in the roadway, dark-unlighted conditions, and speed limits greater than 60 mph are significant only in rural areas but not in urban areas. The findings of this study demonstrate some unique characteristics of Louisiana pedestrian crashes, which can help select the targeted countermeasures.
Abstract：Pedestrian safety remains a crucial issue, considering that pedestrian fatalities are increasing faster than motorist fatalities. In 2016, pedestrian fatalities reached nearly 6000 in the United States, which is the highest annual record of pedestrian traffic fatalities in more than two decades. In Louisiana, pedestrian fatalities reached 110 in 2015, nearly 15% of total traffic fatalities. In the same year, the pedestrian fatality rate per 100,000 populations in Louisiana reached a higher level (2.18) than the national average (1.67). While pedestrian crashes most frequently occur in urban areas, the fatal pedestrian crash rate is 1.5 in urban areas and 3.8 in rural areas, and the rural population is only 26.8%. To reduce pedestrian crashes, this paper presents an analysis of Louisiana pedestrian crashes (2006-2015) and investigates the pedestrian safety problem. In addition, the statistical relationship between pedestrian injury severity and contributing factors, including demographics, pedestrian behavior, and the built environment for urban and rural areas, is established using multinomial logit models. Fatal and severe crashes are strongly related to the alcohol or drug use and elderly age of pedestrians, regardless of their urban or rural locations. However, the pedestrian crashes in urban and rural areas have different characteristics. Variables such as pedestrian crossing/entering road away from intersections, walking in the roadway, dark-unlighted conditions, and speed limits greater than 60 mph are significant only in rural areas but not in urban areas. The findings of this study demonstrate some unique characteristics of Louisiana pedestrian crashes, which can help select the targeted countermeasures.
孙铭, 孙小端. Pedestrian Crash Analysis Urban and Rural Areas Louisiana[J]. Journal of Highway and Transportation Research and Development, 2020, 14(1): 102-110.
SUN Ming, SUN Xiaoduan. Pedestrian Crash Analysis Urban and Rural Areas Louisiana. Journal of Highway and Transportation Research and Development, 2020, 14(1): 102-110.
[1] National Center for Statistics and Analysis. Traffic Safety Facts:2016 Data[R]. Washington, DC:National Highway Traffic Safety Administration, 2018.
[2] National Center for Statistics and Analysis. 2016 Fatal Motor Vehicle Crashes:Overview[R]. Washington, DC:National Highway Traffic Safety Administration, 2017.
[3] RETTING R. Pedestrian Traffic Fatalities by State 2016:Preliminary Data[R]. Washington, DC:Governors Highway Safety Association, 2017.
[4] Dangerous by Design 2014[R]. Washington, DC:Smart Growth America, 2014. (2014-05-01)[2016-10-13].
[5] CAMPBELL B J, ZEGEER C V, HUANG H H, CYNECKI M J. A Review of Pedestrian Safety Research in the United States and Abroad[R]. Washington, DC:Federal Highway Administration, 2003.
[6] HALL J W, BROGAN J D, KONDREDDI M. Pedestrian Safety on Rural Highways[R]. Washington, DC:Federal Highway Administration, 2004.
[7] IVAN J N, GÅRDER P E, ZAJAC S S. Finding Strategies to Improve Pedestrian Safety in Rural Areas[R]. Storrs, Connecticut Transportation Institute, 2000.
[8] OSSENBRUGGEN P J, PENDHARKAR J, IVAN J. Roadway Safety in Rural and Small Urbanized Areas[J]. Accident Analysis and Prevention, 2001, 3(4):485-498.
[9] CARTER D L, COUNCIL F M. Factors Contributing to Pedestrian and Bicycle Crashes on Rural Highways[C]. 86th Annual Meeting of the Transportation Research Board. Washington, DC:TRB, 2007.
[10] ROTHMAN L, HOWARD A W, CAMDEN A, MACARTHUR C. Pedestrian Crossing Location Influences Injury Severity in Urban Areas[J]. Injury Prevention, 2012, 18(6):365-370.
[11] ISLAM S, JONES S L. Pedestrian At-Fault Crashes on Rural and Urban Roadways in Alabama[J]. Accident Analysis and Prevention, 2014, 72:267-276.
[12] POUR-ROUHOLAMIN M, ZHOU H. Investigating the Risk Factors Associated with Pedestrian Injury Severity in Illinois[J]. Journal of Safety Research, 2016, 57:9-17.
[13] ABDUL-AZIZ H M, UKKUSURI S V, HASAN S. Exploring the Determinants of Pedestrian-Vehicle Crash Severity in New York City[J]. Accident Analysis and Prevention, 2013, 50:1298-1309.
[14] SARKAR S, TAY R, HUNT J. Logistic Regression Model of Risk of Fatality in Vehicle-Pedestrian Crashes on National Highways in Bangladesh[J]. Transportation Research Record:Journal of the Transportation Research Board, 2011, 2264:128-137.
[15] BALLESTEROS M F, DISCHINGER P C, LANGENBERG P. Pedestrian Injuries and Vehicle Type in Maryland[J]. Accident Analysis and Prevention, 2004, 36(1):73-81.
[16] LEE C, ABDEL-ATY M. Comprehensive Analysis of Vehicle-Pedestrian Crashes at Intersections in Florida[J]. Accident Analysis and Prevention, 2005, 37(4):775-786.
[17] ELURU N, BHAT C R, HENSHER D A. A Mixed Generalized Ordered Response Model for Examining Pedestrian and Bicyclist Injury Severity Level in Traffic Crashes[J]. Accident Analysis and Prevention, 2008, 40(3):1033-1054.
[18] MCFADDEN D, TRAIN K. Mixed MNL Models for Discrete Response[J]. Journal of Applied Econometrics, 2000, 15(5):447-470.
[19] KIM K, YAMASHITA E Y. Using A K-Means Clustering Algorithm to Examine Patterns of Pedestrian Involved Crashes in Honolulu, Hawaii[J]. Journal of Advanced Transportation, 2007, 41(1):69-89.
[20] KIM J-K, KIM S, ULFARSSON G, PORRELLO L. Bicyclist Injury Severities in Bicycle-Motor Vehicle Accidents[J]. Accident Analysis and Prevention, 2007, 39:238-251.
[21] TORAN P A, MORIDPOUR S, YAY R, RAJABIFARD A. A Partial Proportional Odds Model for Pedestrian Crashes at Mid-Blocks in Melbourne Metropolitan Area[C]. 5th International Conference on Transportation and Traffic Engineering. Lucerne, Switzerland:ICTTE, 2016.
[22] BARIEDDY R, POUR-ROUHOLAMIN M, ZHOU H, QI Y. Factors Contributing to Injury Severity of Pedestrian Crashes at Uncontrolled Locations in Illinois[C]. 96th Annual Meeting of the Transportation Research Board. Washington DC:TRB, 2017.
[23] SASIDHARAN L, WU K F, MENENDEZ M. Exploring the Application of Latent Class Cluster Analysis for Investigating Pedestrian Crash Injury Severities in Switzerland[J]. Accident Analysis and Prevention, 2015, 85:219-228.
[24] SAVOLAINEN P T, MANNERING F L, LORD D, QUDDUS M A. The Statistical Analysis of Highway Crash-Injury Severities:A Review and Assessment of Methodological Alternatives[J]. Accident Analysis and Prevention, 2011, 43(3):1666-1676.
[25] AL-GHAMDI A. Using Logistic Regression to Estimate the Influence of Accident Factors on Accident Severity[J]. Accident Analysis and Prevention, 2002, 34(6):729-741.
[26] BEDARD M, GUYATT G, STONES M, HIRDES J. The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities[J]. Accident Analysis and Prevention, 2002, 34(6):717-727.
[27] CARSON J. MANNERING F. The Effect of Ice Warning Signs on Ice-Accident Frequencies and Severities[J]. Accident Analysis and Prevention, 2001, 33(1):99-109.
[28] ISLAM S, MANNERING F. Driver Aging and Its Effect on Male and Female Single-Vehicle Accident Injuries:Some Additional Evidence[J]. Journal of Safety Research, 2006, 37(3):267-276.
[29] SHANKAR V, MANNERING F. An Exploratory Multinomial Logit Analysis of Single-Vehicle Motorcycle Accident Severity[J]. Journal of Safety Research, 1996, 27(3):183-194.
[30] ULFARSSON G, MANNERING F. Differences in Male and Female Injury Severities in Sport-Utility Vehicle, Minivan, Pickup and Passenger Car Accidents[J]. Accident Analysis and Prevention, 2004, 36(2):135-147.
[31] VALENT F, SCHIAVA F, SAVONITTO C, GALLO T, BRUSAFERRO S, BARBONE F. Risk Factors for Fatal Road Traffic Accidents in Udine, Italy[J]. Accident Analysis and Prevention, 2002, 34(1):71-84.
[32] YAU K K W. Risk Factors Affecting the Severity of Single Vehicle Traffic Accidents in Hong Kong[J]. Accident Analysis and Prevention, 2004, 36(3):333-340.
[33] ZHANG J, LINDSAY J, CLARKE K, ROBBINS G, MAO Y. Factors Affecting the Severity of Motor Vehicle Traffic Crashes Involving Elderly Drivers in Ontario[J]. Accident Analysis and Prevention, 2000, 32(1):117-125.
[34] DEPAIRE B, WETS G, VANHOOF K. Traffic Accident Segmentation by Means of Latent Class Clustering[J]. Accident Analysis and Prevention, 2008, 40(4):1257-1266.
[35] MOHAMED G M, SAUNIER N, MIRANDA-MORENO L F, UKKUSURI S V. A Clustering Regression Approach:A Comprehensive Injury Severity Analysis of Pedestrian-Vehicle Crashes in New York, US and Montreal, Canada[J]. Safety Science, 2013, 54:27-37.
2020, Vol. 14 
