Portuguese two-lane highways: modelling crash frequencies for different temporal and spatial aggregation of crash data
Abstract
The identification of contributory factors to crash frequencies observed in different highway facilities can aid transportation and traffic management agencies to improve road traffic safety. In spite of the strategic importance of the national Portuguese road network, there are no recent studies concerned with either the identification of contributory factors to road crashes or Crash Prediction Models (CPMs) for this type of roadway. This study presents an initial contribution to this problem by focusing on the national roads NR-14, NR-101 and NR-206, which are located in Northern region of Portugal. They are two-lane single carriageway rural roads. This study analysed the crash frequencies, Average Annual Daily Traffic (AADT) and geometric characteristics of 88 two-lane road segments. The selected segments were 200-m-long and did not cross through urbanized areas. The fixed length of 200 meters corresponds to the road length used in Portugal to define a critical point. Data regarding the annual crash frequency and the AADT were available from 1999 to 2010. Due to the high number of zero-crash records in the initial database, the data were explored to identify the best statistical modelling approach to be adopted. The Generalized Estimating Equations (GEE) procedure was applied to 10 distinctive databases formed by grouping the original data in time and space. The results show that the different observations within each road segment present an exchangeable correlation structure type. This paper also analyses the impact of the sample size on the model’s capability of identifying the contributing factors to crash frequencies. The major contributing factors identified for the two-lane highways studied were the traffic volume (expressed in AADT), lane width, vertical sinuosity, and Density of Access Points (DAP). Acceptable CPM was identified for the highways considered, which estimated the total number of crashes for 400-m-long segments for a cumulative period of two years.
First published online 18 August 2015
Keyword : crash contributory factors, generalized estimating equations, crash prediction models, two-lane highways, longitudinal data
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Anastasopoulos, P. C.; Mannering, F. L. 2009. A note on modeling vehicle accident frequencies with random-parameters count models, Accident Analysis & Prevention 41(1): 153–159. http://dx.doi.org/10.1016/j.aap.2008.10.005
Anastasopoulos, P. C.; Mannering, F. L.; Shankar, V. N.; Had-dock, J. E. 2012. A study of factors affecting highway accident rates using the random-parameters tobit model, Accident Analysis & Prevention 45: 628–633. http://dx.doi.org/10.1016/j.aap.2011.09.015
ANSR. 2009. Acidentes Rodoviários. Observatório de Seguran-ça Rodoviária [Road Crashes. Road Safety Observatory]. Autoridade Nacional de Segurança Rodoviária (ANSR) [National Authority for Road Safety], Lisboa, Portugal (Portuguese).
Ballinger, G. A. 2004. Using generalized estimating equations for longitudinal data analysis, Organizational Research Methods 7(2): 127–150. http://dx.doi.org/10.1177/1094428104263672
Cafiso, S.; Di Graziano, A.; Di Silvestro, G.; La Cava, G.; Persaud, B. 2010. Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables, Accident Analysis &Prevention 42(4): 1072–1079. http://dx.doi.org/10.1016/j.aap.2009.12.015
Caliendo, C.; Guida, M.; Parisi, A. 2007. A crash-prediction model for multilane roads, Accident Analysis & Prevention39(4): 657–670. http://dx.doi.org/10.1016/j.aap.2006.10.012
Carson, J.; Mannering, F. 2001. The effect of ice warning signs on ice-accident frequencies and severities, Accident Analysis & Prevention 33(1): 99–109. http://dx.doi.org/10.1016/S0001-4575(00)00020-8
Couto, A.; Ferreira, S. 2011. A note on modeling road accident frequency: a flexible elasticity model, Accident Analysis &Prevention 43(6): 2104–2111. http://dx.doi.org/10.1016/j.aap.2011.05.033
Dinu, R. R.; Veeraragavan, A. 2011. Random parameter models for accident prediction on two-lane undivided highways in India, Journal of Safety Research 42(1): 39–42. http://dx.doi.org/10.1016/j.jsr.2010.11.007
El-Basyouny, K.; Sayed, T. 2009. Accident prediction models with random corridor parameters, Accident Analysis &Prevention 41(5): 1118–1123. http://dx.doi.org/10.1016/j.aap.2009.06.025
FHWA-TFHRC. 2003. Interactive Highway Safety Design Model (IHSDM): Crash Prediction Module (CPM) Engineer’s Manual. EUA: IHSDM. Federal Highway Admininstation(FHWA), Turner-Fairbank Highway Re-search Center (TFHRC). 66 p. Available from Internet: http://www.wsdot.wa.gov/publications/fulltext/design/IHSDM/CPM_EM.pdf
Gomes, S. V. 2012. The influence of the infrastructure characteristics in urban road accidents occurrence, Procedia – Social and Behavioral Sciences 48: 1611–1621. http://dx.doi.org/10.1016/j.sbspro.2012.06.1136
Gomes, S. V.; Cardoso, J. L. 2012. Safety effects of low-cost engineering measures. An observational study in a Portuguese multilane road, Accident Analysis & Prevention 48: 346–352. http://dx.doi.org/10.1016/j.aap.2012.02.004
Gomes, S. V.; Geedipally, S. R.; Lord, D. 2012. Estimating the safety performance of urban intersections in Lisbon, Portugal, Safety Science 50(9): 1732–1739. http://dx.doi.org/10.1016/j.ssci.2012.03.022
Halekoh, U.; Højsgaard, S.; Yan, J. 2006. The R package geepack for generalized estimating equations, Journal of Statistical Software 15(2): 1–11.
Harwood, D. W.; Council, F. M.; Hauer, E.; Hughes, W. E.; Vogt, A. 2000. Prediction of the Expected Safety Per-formance of Rural Two-Lane Highways. Publication No FHWA-RD-99-207. US Department of Transportation, Federal Highway Administration (FHWA). 200 p. Available from Internet: http://www.fhwa.dot.gov/publications/research/safety/99207/99207.pdf
Hauer, E. 2004. Statistical road safety modeling, Transportation Research Record 1897: 81–87. http://dx.doi.org/10.3141/1897-11
Joshua, S. C.; Garber, N. J. 1990. Estimating truck accident rate and involvements using linear and Poisson regression models, Transportation Planning and Technology 15(1): 41–58. http://dx.doi.org/10.1080/03081069008717439
Kumara, S. S. P.; Chin, H. C. 2003. Modeling accident occurrence at signalized tee intersections with special emphasis on excess zeros, Traffic Injury Prevention 4(1): 53–57. http://dx.doi.org/10.1080/15389580309852
Liang, K.-Y.; Zeger, S. L. 1986. Longitudinal data analysis using generalized linear models, Biometrika 73(1): 13–22. http://dx.doi.org/10.1093/biomet/73.1.13
Lord, D.; Bonneson, J. 2007. Development of accident modification factors for rural frontage road segments in Texas, Transportation Research Record 2023: 20–27. http://dx.doi.org/10.3141/2023-03
Lord, D.; Mahlawat, M. 2009. Examining application of aggregated and disaggregated poisson-gamma models subjected to low sample mean bias, Transportation Research Record2136: 1–10. http://dx.doi.org/10.3141/2136-01
Lord, D.; Manar, A.; Vizioli, A. 2005a. Modeling crash-flow-density and crash-flow-V/C ratio relationships for rural and urban freeway segments, Accident Analysis & Prevention37(1): 185–199. http://dx.doi.org/10.1016/j.aap.2004.07.003
Lord, D.; Mannering, F. 2010. The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives, Transportation Research Part A: Policy and Practice 44(5): 291–305. http://dx.doi.org/10.1016/j.tra.2010.02.001
Lord, D.; Persaud, B. 2000. Accident prediction models with and without trend: application of the generalized estimating equations procedure, Transportation Research Record1717: 102–108. http://dx.doi.org/10.3141/1717-13
Lord, D.; Washington, S.; Ivan, J. N. 2007. Further notes on the application of zero-inflated models in highway safety, Accident Analysis & Prevention 39(1): 53–57. http://dx.doi.org/10.1016/j.aap.2006.06.004
Lord, D.; Washington, S. P.; Ivan, J. N. 2005b. Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory, Accident Analysis & Prevention 37(1): 35–46. http://dx.doi.org/10.1016/j.aap.2004.02.004
Milton, J.; Mannering, F. 1998. The relationship among high-way geometrics, traffic-related elements and motor-vehicle accident frequencies, Transportation 25(4): 395–413. http://dx.doi.org/10.1023/A:1005095725001
Pan, W. 2001. Akaike’s information criterion in generalized estimating equations, Biometrics 57(1): 120–125. http://dx.doi.org/10.1111/j.0006-341X.2001.00120.x
Poch, M.; Mannering, F. 1996. Negative binomial analysis of intersection-accident frequencies, Journal of Transportation Engineering 122(2): 105–113. http://dx.doi.org/10.1061/(ASCE)0733-947X(1996)122:2(105)
Shankar, V.; Milton, J.; Mannering, F. 1997. Modeling accident frequencies as zero-altered probability processes: an empirical inquiry, Accident Analysis & Prevention 29(6): 829–837. http://dx.doi.org/10.1016/S0001-4575(97)00052-3
Thomas, P.; Morris, A.; Otte, D.; Breen, J. 2003. Real-world accident data-coordinated methodologies for data collection to improve vehicle and road safety, in Proceedings: 18th International Technical Conference on the Enhanced Safety of Vehicles, 19–22 May 2003, Nagoya, Japan, 1–10.
Vangeneugden, T.; Molenberghs, G.; Verbeke, G.; Demé-trio, C. G. B. 2011. Marginal correlation from an extended random-effects model for repeated and overdispersed counts, Journal of Applied Statistics 38(2): 215–232. http://dx.doi.org/10.1080/02664760903406405
Venkataraman, N.; Ulfarsson, G. F.; Shankar, V. N. 2013. Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type, Accident Analysis & Prevention 59, 309–318. http://dx.doi.org/10.1016/j.aap.2013.06.021
Wang, X.; Abdel-Aty, M. 2006. Temporal and spatial analyses of rear-end crashes at signalized intersections, Accident Analysis & Prevention 38(6): 1137–1150. http://dx.doi.org/10.1016/j.aap.2006.04.022
WHO. 2013. Global Status Report on Road Safety 2013: Sup-porting a Decade of Action. World Health Organization (WHO), Geneva, Switzerland. 318 p. Available from Internet: http://www.iru.org/cms-filesystem-action/policies/sustainable_development/road_safety/gsrrs_en.pdf
Wooldridge, J. M. 2010. Econometric Analysis of Cross Section and Panel Data. The MIT Press. 1096 p.
Zhang, Y.; Xie, Y.; Li, L. 2012. Crash frequency analysis of different types of urban roadway segments using generalized additive model, Journal of Safety Research 43(2): 107–114. http://dx.doi.org/10.1016/j.jsr.2012.01.003
Zheng, B. 2000. Summarizing the goodness of fit of generalized linear models for longitudinal data, Statistics in Medicine19(10): 1265–1275. http://dx.doi.org/10.1002/(SICI)1097-0258(20000530) 19:10<1265::AID-SIM486>3.0.CO;2-U