Structural equation modelling of potential risk factors for pedestrian accidents in rural and urban roads

References

• Abay, K. A. (2013). Examining pedestrian-injury severity using alternative disaggregate models. Research in Transportation Economics, 43(1), 123–136. https://doi.org/10.1016/j.retrec.2012.12.002
   Web of Science ®Google Scholar
• Abdel-Aty, M. A., & Abdelwahab, H. T. (2000). Exploring the relationship between alcohol and the driver characteristics in motor vehicle accidents. Accident Analysis & Prevention, 32(4), 473–482. https://doi.org/10.1016/S0001-4575(99)00062-7
   PubMed Web of Science ®Google Scholar
• Abdul Manan, M. M., Várhelyi, A., Çelik, A. K., & Hashim, H. H. (2018). Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia. IATSS Research, 42(4), 207–220. https://doi.org/10.1016/j.iatssr.2017.11.001
   Web of Science ®Google Scholar
• Ackaah, W., Apuseyine, B. A., & Afukaar, F. K. (2020). Road traffic crashes at night-time: Characteristics and risk factors. International Journal of Injury Control and Safety Promotion, 27(3), 392–398. https://doi.org/10.1080/17457300.2020.1785508
   PubMed Web of Science ®Google Scholar
• Aidoo, E. N., Amoh-Gyimah, R., & Ackaah, W. (2013). The effect of road and environmental characteristics on pedestrian hit-and-run accidents in Ghana. Accident Analysis & Prevention, 53, 23–27. https://doi.org/10.1016/j.aap.2012.12.021
   PubMed Web of Science ®Google Scholar
• Al-Ghamdi, A. S. (2002). Pedestrian–vehicle crashes and analytical techniques for stratified contingency tables. Accident Analysis & Prevention, 34(2), 205–214. https://doi.org/10.1016/S0001-4575(01)00015-X
   PubMed Web of Science ®Google Scholar
• Al-Ghamdi, A. S. (2003). Analysis of traffic accidents at urban intersections in Riyadh. Accident Analysis & Prevention, 35(5), 717–724. https://doi.org/10.1016/S0001-4575(02)00050-7
   PubMed Web of Science ®Google Scholar
• Al-Madani, H., & Al-Janahi, A. (2006). Personal exposure risk factors in pedestrian accidents in Bahrain. Safety Science, 44(4), 335–347. https://doi.org/10.1016/j.ssci.2005.10.009
   Web of Science ®Google Scholar
• Al-Mahameed, F. J., Qin, X., Schneider, R. J., & Shaon, M. R. R. (2019). Analyzing pedestrian and bicyclist crashes at the corridor level: Structural equation modeling approach. Transportation Research Record: Journal of the Transportation Research Board, 2673(7), 308–318. https://doi.org/10.1177/0361198119845353
   Web of Science ®Google Scholar
• Araar, A., & El Tayeb, A. A. (2013). Mining road traffic accident data to improve safety in Dubai. Journal of Theoretical and Applied Information Technology, 47, 911–925.
   Google Scholar
• Austin, P. C., & Steyerberg, E. W. (2015). The number of subjects per variable required in linear regression analyses. Journal of Clinical Epidemiology, 68(6), 627–636. https://doi.org/10.1016/j.jclinepi.2014.12.014
   PubMed Web of Science ®Google Scholar
• Aziz, H. M. A., Ukkusuri, S. V., & Hasan, S. (2013). Exploring the determinants of pedestrian-vehicle crash severity in New York City. Accident Analysis & Prevention, 50, 1298–1309. https://doi.org/10.1016/j.aap.2012.09.034
   PubMed Web of Science ®Google Scholar
• Ballesteros, M. F., Dischinger, P. C., & Langenberg, P. (2004). Pedestrian injuries and vehicle type in Maryland, 1995–1999. Accident Analysis & Prevention, 36(1), 73–81. https://doi.org/10.1016/S0001-4575(02)00129-X
   PubMed Web of Science ®Google Scholar
• Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. https://doi.org/10.1016/j.im.2019.05.003
   Web of Science ®Google Scholar
• Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates.
   Google Scholar
• Daniels, S., Brijs, T., Nuyts, E., & Wets, G. (2010). Externality of risk and crash severity at roundabouts. Accident Analysis & Prevention, 42(6), 1966–1973. https://doi.org/10.1016/j.aap.2010.06.001
   PubMed Web of Science ®Google Scholar
• Distefano, N., Leonardi, S., & Pulvirenti, G. (2018). Factors with the greatest influence on drivers’ judgment of roundabouts safety. An analysis based on web survey in Italy. IATSS Research, 42(4), 265–273. https://doi.org/10.1016/j.iatssr.2018.04.002
   Web of Science ®Google Scholar
• Etehad, H., Yousefzadeh-Chabok, S., Davoudi-Kiakalaye, A., Moghadam, D. A., Hemati, H., & Mohtasham-Amiri, Z. (2015). Impact of road traffic accidents on the elderly. Archives of Gerontology and Geriatrics, 61(3), 489–493. https://doi.org/10.1016/j.archger.2015.08.008
   PubMed Web of Science ®Google Scholar
• George, Y., Athanasios, T., & George, P. (2017). Investigation of road accident severity per vehicle type. Transportation Research Procedia, 25, 2076–2083. https://doi.org/10.1016/j.trpro.2017.05.401
   Google Scholar
• Habibovic, A., Tivesten, E., Uchida, N., Bärgman, J., & Ljung Aust, M. (2013). Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM). Accident Analysis & Prevention, 50, 554–565. https://doi.org/10.1016/j.aap.2012.05.034
   PubMed Web of Science ®Google Scholar
• Han, I. (2013). Fuzzy estimation of vehicle speed in pedestrian collision accidents. International Journal of Automotive Technology, 14(3), 385–393. https://doi.org/10.1007/s12239-013-0042-y
   Web of Science ®Google Scholar
• Hassan, H. M., & Abdel-Aty, M. A. (2011). Analysis of drivers’ behavior under reduced visibility conditions using a structural equation modeling approach. Transportation Research Part F: Traffic Psychology and Behaviour, 14(6), 614–625. https://doi.org/10.1016/j.trf.2011.07.002
   Web of Science ®Google Scholar
• Hatcher, L. (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling (1st ed.). SAS Publishing.
   Google Scholar
• Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17(1), 82–109. https://doi.org/10.1080/10705510903439003
   Web of Science ®Google Scholar
• Höck, M., & Ringle, C. M. (2006). Strategic networks in the software industry: An empirical analysis of the value continuum. IFSAM VIIIth World Congress. http://www.ibl-unihh.de/IFSAM06.pdf
   Google Scholar
• Hongqi, L. (2013). Analysis of intersection accidents of mountainous highway. Procedia – Social and Behavioral Sciences, 96, 205–209. https://doi.org/10.1016/j.sbspro.2013.08.027
   Google Scholar
• Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
   Web of Science ®Google Scholar
• Huang, J., Peng, Y., Yang, J., Otte, D., & Wang, B. (2018). A study on correlation of pedestrian head injuries with physical parameters using in-depth traffic accident data and mathematical models. Accident Analysis & Prevention, 119, 91–103. https://doi.org/10.1016/j.aap.2018.07.012
   PubMed Web of Science ®Google Scholar
• Iranian Legal Medicine Organization. (2018). Iranian Legal Medicine Organization. http://www.lmo.ir/web_directory/53999-%D8%AA%D8%B5%D8%A7%D8%AF%D9%81%D8%A7%D8%AA.html
   Google Scholar
• Jabbari, P., Khaloei, M., & MacKenzie, D. (2018). Locating fast charging stations for safe and reliable intercity electric vehicle travel in Washington [Paper presentation]. 97th Annual Meeting of the Transportation Research Board, Washington, D.C. https://trid.trb.org/view/1629759
   Google Scholar
• Jabbari, P., Khaloei, M., & MacKenzie, D. (2019). Estimating potential demand for long-distance electric vehicle travel in Washington State [Paper presentation]. Transportation Research Board 98th Annual Meeting of the Transportation Research Board. https://trid.trb.org/view/1573197
   Google Scholar
• Kadali, B. R., & Vedagiri, P. (2013). Effect of vehicular lanes on pedestrian gap acceptance behaviour. Procedia – Social and Behavioral Sciences, 104, 678–687. https://doi.org/10.1016/j.sbspro.2013.11.162
   Google Scholar
• Khaloei, M., Ranjbari, A., & MacKenzie, D. (2019). Analyzing the shift in travel modes’ market shares with the deployment of autonomous vehicle technology [Paper presentation]. 99th Annual Meeting of the Transportation Research Board, Washington, D.C. https://digital.lib.washington.edu:443/researchworks/handle/1773/45288
   Google Scholar
• Khattak, A., & Tung, L.-W. (2015). Severity of Pedestrian Crashes at Highway-Rail Grade Crossings. Journal of the Transportation Research Forum, 54, 91–100. https://doi.org/10.22004/ag.econ.241830
   Google Scholar
• Kim, K., Brunner, I. M., & Yamashita, E. (2008). Modeling fault among accident-involved pedestrians and motorists in Hawaii. Accident Analysis & Prevention, 40(6), 2043–2049. https://doi.org/10.1016/j.aap.2008.08.021
   PubMed Web of Science ®Google Scholar
• Kong, C., & Yang, J. (2010). Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China. Accident Analysis & Prevention, 42(4), 987–993. https://doi.org/10.1016/j.aap.2009.11.006
   PubMed Web of Science ®Google Scholar
• Kummeneje, A.-M., & Rundmo, T. (2019). Risk perception, worry, and pedestrian behaviour in the Norwegian population. Accident Analysis & Prevention, 133, 105294. https://doi.org/10.1016/j.aap.2019.105294
   PubMed Web of Science ®Google Scholar
• Kupek, E. (2006). Beyond logistic regression: Structural equations modelling for binary variables and its application to investigating unobserved confounders. BMC Medical Research Methodology, 6(1), 13. https://doi.org/10.1186/1471-2288-6-13
   PubMedGoogle Scholar
• Layegh, M., Mirbaha, B., & Rassafi, A. A. (2020). Modeling the pedestrian behavior at conflicts with vehicles in multi-lane roundabouts (a cellular automata approach). Physica A: Statistical Mechanics and Its Applications, 556, 124843. https://doi.org/10.1016/j.physa.2020.124843
   Web of Science ®Google Scholar
• Lee, C., & Abdel-Aty, M. (2005). Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida. Accident Analysis & Prevention, 37(4), 775–786. https://doi.org/10.1016/j.aap.2005.03.019
   PubMed Web of Science ®Google Scholar
• Lee, J.-Y., Chung, J.-H., & Son, B. (2008). Analysis of traffic accident size for Korean highway using structural equation models. Accident Analysis & Prevention, 40(6), 1955–1963. https://doi.org/10.1016/j.aap.2008.08.006
   PubMed Web of Science ®Google Scholar
• Lefrandt, L., Sulistio, H., Wicaksono, A., Djakfar, L., & Otok, B. W. (2016). Model movement pedestrian satisfaction in Manado using structural equation modeling. Journal of Applied Environmental and Biological Sciences, 6(1), 31–37.
   Google Scholar
• Lipovac, K., Vujanic, M., Maric, B., & Nesic, M. (2013). The influence of a pedestrian countdown display on pedestrian behavior at signalized pedestrian crossings. Transportation Research Part F: Traffic Psychology and Behaviour, 20, 121–134. https://doi.org/10.1016/j.trf.2013.07.002
   Web of Science ®Google Scholar
• Ma, M., Yan, X., Huang, H., & Abdel-Aty, M. (2010). Safety of public transportation occupational drivers: Risk perception, attitudes, and driving behavior. Transportation Research Record: Journal of the Transportation Research Board, 2145(1), 72–79. https://doi.org/10.3141/2145-09
   Google Scholar
• Mako, E., & Szakonyi, P. (2016). Evaluation of human behavior at pedestrian crossings. Transportation Research Procedia, 14, 2121.
   Google Scholar
• Mohamed, M., & Bromfield, N. F. (2017). Attitudes, driving behavior, and accident involvement among young male drivers in Saudi Arabia. Transportation Research Part F: Traffic Psychology and Behaviour, 47, 59–71. https://doi.org/10.1016/j.trf.2017.04.009
   Web of Science ®Google Scholar
• Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37. https://doi.org/10.1016/j.ssci.2012.11.001
   Web of Science ®Google Scholar
• Nowakowska, M. (2014). Random forests in the evaluation of threat for pedestrian accidents in towns [Paper presentation]. 24th ICTCT Workshop, 13.
   Google Scholar
• Nuñez Velasco, J. P., Farah, H., van Arem, B., & Hagenzieker, M. P. (2019). Studying pedestrians’ crossing behavior when interacting with automated vehicles using virtual reality. Transportation Research Part F: Traffic Psychology and Behaviour, 66, 1–14. https://doi.org/10.1016/j.trf.2019.08.015
   Web of Science ®Google Scholar
• Oikawa, S., & Matsui, Y. (2017). Features of serious pedestrian injuries in vehicle-to-pedestrian accidents in Japan. International Journal of Crashworthiness, 22(2), 202–213. https://doi.org/10.1080/13588265.2016.1244230
   Web of Science ®Google Scholar
• Organisation for Economic Co-operation and Development (OECD) Staff, International Traffic Safety Data and Analysis Group, & International Transport Forum Staff. (2017). Road Safety Annual Report 2017. Organization for Economic Cooperation & Development Rowman& Littlefield Publishers, Incorporated [Distributor].
   Google Scholar
• Papadimitriou, E., Lassarre, S., & Yannis, G. (2016). Introducing human factors in pedestrian crossing behaviour models. Transportation Research Part F: Traffic Psychology and Behaviour, 36, 69–82. https://doi.org/10.1016/j.trf.2015.11.003
   Web of Science ®Google Scholar
• Potoglou, D., Carlucci, F., Cirà, A., & Restaino, M. (2018). Factors associated with urban non-fatal road-accident severity. International Journal of Injury Control and Safety Promotion, 25(3), 303–310. https://doi.org/10.1080/17457300.2018.1431945
   PubMed Web of Science ®Google Scholar
• Quimby, A. R., & Watts, G. R. (1981). Human factors and driving performance. Transport and Road Research Laboratory (TRRL). https://trid.trb.org/view/174634
   Google Scholar
• Reeves, K., Chandan, J. S., & Bandyopadhyay, S. (2019). Using statistical modelling to analyze risk factors for severe and fatal road traffic accidents. International Journal of Injury Control and Safety Promotion, 26(4), 364–371. https://doi.org/10.1080/17457300.2019.1635625
   PubMed Web of Science ®Google Scholar
• Salamati, K., Schroeder, B. J., Geruschat, D. R., & Rouphail, N. M. (2014). Event-based modeling of driver yielding behavior to pedestrians at two-lane roundabout approaches. Transportation Research Record, 2013(2389), 1–11. https://doi.org/10.3141/2389-01
   PubMedGoogle Scholar
• Serag, M. S. (2014). Modelling pedestrian road crossing at uncontrolled mid-block locations in developing countries. International Journal for Computational Civil and Structural Engineering, 4(3), 274–285.
   Google Scholar
• Shesterov, E., & Mikhailov, A. (2017). Accident rates at signalized intersections. Transportation Research Procedia, 20, 613–617. https://doi.org/10.1016/j.trpro.2017.01.099
   Google Scholar
• Sheykhfard, A., & Haghighi, F. (2018). Behavioral analysis of vehicle-pedestrian interactions in Iran. Scientia Iranica, 25(4), 1968–1976. https://doi.org/10.24200/sci.2017.4201
   Web of Science ®Google Scholar
• Sheykhfard, A., & Haghighi, F. (2019). Performance analysis of urban drivers encountering pedestrian. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 160–174. https://doi.org/10.1016/j.trf.2018.12.019
   Web of Science ®Google Scholar
• Sheykhfard, A., Haghighi, F. R., Soltaninejad, M., & Karji, A. (2020). Analyzing Drivers' Mental Patterns Using Q-Methodology. Journal of Transportation Technologies, 10(2), 169. https://doi.org/10.4236/jtts.2020.102011
   Google Scholar
• Sheykhfard, A., & Haghighi, F. (2020a). Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran. Journal of Safety Research, 72, 1–8. https://doi.org/10.1016/j.jsr.2019.11.002
   PubMed Web of Science ®Google Scholar
• Sheykhfard, A., & Haghighi, F. (2020b). Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV). Accident Analysis & Prevention, 144, 105661. https://doi.org/10.1016/j.aap.2020.105661
   PubMed Web of Science ®Google Scholar
• Simončič, M. (2001). Road accidents in Slovenia involving a pedestrian, cyclist or motorcyclist and a car. Accident Analysis & Prevention, 33(2), 147–156. https://doi.org/10.1016/S0001-4575(00)00025-7
   PubMed Web of Science ®Google Scholar
• Sullivan, J. M., & Flannagan, M. J. (2011). Differences in geometry of pedestrian crashes in daylight and darkness. Journal of Safety Research, 42(1), 33–37. https://doi.org/10.1016/j.jsr.2010.11.005
   PubMed Web of Science ®Google Scholar
• Sze, N. N., & Wong, S. C. (2007). Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accident Analysis & Prevention, 39(6), 1267–1278. https://doi.org/10.1016/j.aap.2007.03.017
   PubMed Web of Science ®Google Scholar
• Tarka, P. (2018). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52(1), 313–354. https://doi.org/10.1007/s11135-017-0469-8
   PubMedGoogle Scholar
• Theofilatos, A., & Efthymiou, D. (2012). Investigation of pedestrians’ accident patterns in Greater Athens area. Procedia – Social and Behavioral Sciences, 48, 1897–1906. https://doi.org/10.1016/j.sbspro.2012.06.1164
   Google Scholar
• Thomas, M., Williams, T., & Jones, J. (2020). The epidemiology of pedestrian fatalities and substance use in Georgia, United States, 2007–2016. Accident Analysis & Prevention, 134, 105329. https://doi.org/10.1016/j.aap.2019.105329
   PubMed Web of Science ®Google Scholar
• Tian, Z. (2013). Speed-accident relationship at urban signalized intersections. Procedia – Social and Behavioral Sciences, 96, 1383–1388. https://doi.org/10.1016/j.sbspro.2013.08.157
   Google Scholar
• Transportation Research Board, & National Academies of Sciences, Engineering, & Medicine (2014). Analysis of naturalistic driving study data: Safer glances, driver inattention, and crash risk (T. Victor, Ed.). The National Academies Press.
   Google Scholar
• Troutt, M. D. (2004). Regression, 10k rule of thumb for. In Encyclopedia of statistical sciences. American Cancer Society.
   Google Scholar
• Värnild, A., Tillgren, P., & Larm, P. (2020). What types of injuries did seriously injured pedestrians and cyclists receive in a Swedish urban region in the time period 2003–2017 when Vision Zero was implemented? Public Health, 181, 59–64. https://doi.org/10.1016/j.puhe.2019.11.019
   PubMed Web of Science ®Google Scholar
• Verzosa, N., & Miles, R. (2016). Severity of road crashes involving pedestrians in Metro Manila, Philippines. Accident Analysis & Prevention, 94, 216–226. https://doi.org/10.1016/j.aap.2016.06.006
   PubMed Web of Science ®Google Scholar
• Vignali, V., Cuppi, F., Acerra, E., Bichicchi, A., Lantieri, C., Simone, A., & Costa, M. (2019). Effects of median refuge island and flashing vertical sign on conspicuity and safety of unsignalized crosswalks. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 427–439. https://doi.org/10.1016/j.trf.2018.10.033
   Web of Science ®Google Scholar
• Wang, D., Liu, Q., Ma, L., Zhang, Y., & Cong, H. (2019). Road traffic accident severity analysis: A census-based study in China. Journal of Safety Research, 70, 135–147. https://doi.org/10.1016/j.jsr.2019.06.002
   PubMed Web of Science ®Google Scholar
• Wang, Y., & Zhang, W. (2017). Analysis of roadway and environmental factors affecting traffic crash severities. Transportation Research Procedia, 25, 2119–2125. https://doi.org/10.1016/j.trpro.2017.05.407
   Google Scholar
• Wanvik, P. O. (2009). Effects of road lighting: An analysis based on Dutch accident statistics 1987-2006. Accident Analysis & Prevention, 41(1), 123–128. https://doi.org/10.1016/j.aap.2008.10.003
   PubMed Web of Science ®Google Scholar
• WHO. (2018). Global status report on road safety 2018. WHO. Retrieved December 19, 2019, from http://www.who.int/violence_injury_prevention/road_safety_status/2018/en/
   Google Scholar
• Zajac, S. S., & Ivan, J. N. (2003). Factors influencing injury severity of motor vehicle–crossing pedestrian crashes in rural Connecticut. Accident Analysis & Prevention, 35(3), 369–379. https://doi.org/10.1016/S0001-4575(02)00013-1
   PubMed Web of Science ®Google Scholar
• Zhou, H., Romero, S. B., & Qin, X. (2016). An extension of the theory of planned behavior to predict pedestrians' violating crossing behavior using structural equation modeling. Accident Analysis & Prevention, 95(Pt B), 417–424. https://doi.org/10.1016/j.aap.2015.09.009
   PubMedGoogle Scholar
• Zou, T., Khaloei, M., & MacKenzie, D. (2020). Effects of charging infrastructure characteristics on electric vehicle preferences of U.S. private car owners: A comparative analysis between new and used car buyers [Paper presentation]. 99th Annual Meeting of the Transportation Research Board, Washington, D.C. https://digital.lib.washington.edu:443/researchworks/handle/1773/45289
   Google Scholar