Risk perceptions of pedestrians for traffic and road features

References

• Agran, P. F., Winn, D. G., Anderson, C. L., Tran, C., & Del Valle, C. P. (1996). The role of the physical and traffic environment in child pedestrian injuries. Pediatrics, 98(6), 1096–1103. https://doi.org/10.1542/peds.98.6.1096
   PubMed Web of Science ®Google Scholar
• Allison, P. D., & Christakis, N. (1994). Logit models for sets of ranked items. Sociological Methodology, 24, 199–228. https://doi.org/10.2307/270983
   Web of Science ®Google Scholar
• Beggs, S., Cardell, S., & Hausman, J. (1981). Assessing the potential demand for electric cars. Journal of Econometrics, 17(1), 1–19. https://doi.org/10.1016/0304-4076(81)90056-7
   Web of Science ®Google Scholar
• Bogue, S., Paleti, R., & Balan, L. (2017). A modified rank ordered logit model to analyze injury severity of occupants in multivehicle crashes. Analytic Methods in Accident Research, 14, 22–40. https://doi.org/10.1016/j.amar.2017.03.001
   Web of Science ®Google Scholar
• Butchart, A., Kruger, J., & Lekoba, R. (2000). Perceptions of injury causes and solutions in a Johannesburg township: Implications for prevention. Social Science & Medicine (1982), 50(3), 331–344. https://doi.org/10.1016/s0277-9536(99)00272-5
   PubMed Web of Science ®Google Scholar
• Chapman, R. G., & Staelin, R. (1982). Exploiting rank ordered choice set data within the stochastic utility model. Journal of Marketing Research, 19(3), 288–301. https://doi.org/10.2307/3151563
   Web of Science ®Google Scholar
• Cho, G., Rodríguez, D. A., & Khattak, A. J. (2009). The role of the built environment in explaining relationships between perceived and actual pedestrian and bicyclist safety. Accident; Analysis and Prevention, 41(4), 692–702. https://doi.org/10.1016/j.aap.2009.03.008
   PubMed Web of Science ®Google Scholar
• Gårder, P. E. (2004). The impact of speed and other variables on pedestrian safety in Maine. Accident; Analysis and Prevention, 36(4), 533–542. https://doi.org/10.1016/S0001-4575(03)00059-9
   PubMed Web of Science ®Google Scholar
• Guo, H., Gao, Z., Yang, X., & Jiang, X. (2011). Modeling pedestrian violation behavior at signalized crosswalks in China: A hazards-based duration approach. Traffic Injury Prevention, 12(1), 96–103. https://doi.org/10.1080/15389588.2010.518652
   PubMed Web of Science ®Google Scholar
• Hausman, J. A., & Ruud, P. A. (1987). Specifying and testing econometric models from rank-ordered data. Journal of Econometrics, 34(1-2), 83–104. https://doi.org/10.1016/0304-4076(87)90068-6
   Web of Science ®Google Scholar
• Humpel, N., Owen, N., Iverson, D., Leslie, E., & Bauman, A. (2004). Perceived environment Attributes, residential location, and walking for particular purposes. American Journal of Preventive Medicine, 26(2), 119–125. https://doi.org/10.1016/j.amepre.2003.10.005
   PubMed Web of Science ®Google Scholar
• Kadali, R. D. B., & Vedagiri, D. P. (2020). Role of number of traffic lanes on pedestrian gap acceptance and risk taking behaviour at uncontrolled crosswalk locations. Journal of Transport & Health, 19, 100950. https://doi.org/10.1016/j.jth.2020.100950
   Web of Science ®Google Scholar
• Khatoon, M., Tiwari, G., & Chatterjee, N. (2013). Impact of grade separator on pedestrian risk taking behavior. Accident; Analysis and Prevention, 50, 861–870. https://doi.org/10.1016/j.aap.2012.07.011
   PubMed Web of Science ®Google Scholar
• Koepsell, T., McCloskey, L., Wolf, M., Moudon, A., Buchner, D., Kraus, J., & Patterson, M. (2002). Crosswalk markings and the risk of pedestrian-motor vehicle collisions in older pedestrians. JAMA, 288(17), 2136–2143. https://doi.org/10.1001/jama.288.17.2136
   PubMed Web of Science ®Google Scholar
• Landis, B. W., Vattikuti, V. R., Ottenberg, R. M., McLeod, D. S., & Guttenplan, M. (2001). Modeling the roadside walking environment: A pedestrian level of service. TRB Paper No. 01-0511.
   Google Scholar
• Leden, L. (2002). Pedestrian risk decrease with pedestrian flow. A case study based on data from signalized intersections in Hamilton. Accident; Analysis and Prevention, 34(4), 457–464. https://doi.org/10.1016/s0001-4575(01)00043-4
   PubMed Web of Science ®Google Scholar
• Lee, C., & Abdel-Aty, M. (2005). Comprehensive analysis of vehicle–pedestrian crashes at intersections in Florida. Accident; Analysis and Prevention, 37(4), 775–786. https://doi.org/10.1016/j.aap.2005.03.019
   PubMed Web of Science ®Google Scholar
• Lightstone, A. S., Dhillon, P. K., Peek-Asa, C., & Kraus, J. F. (2001). A geographic analysis of motor vehicle collisions with child pedestrians in Long Beach, California: Comparing intersection and mid-block incident locations. Injury Prevention : journal of the International Society for Child and Adolescent Injury Prevention, 7(2), 155–160. https://doi.org/10.1136/ip.7.2.155
   PubMedGoogle Scholar
• Ling, Z., Ni, Y., Cherry, C., & Li, K. (2013). Pedestrian Level of Service at Signalized Intersections in China Using Contingent field survey and Pedestrian Crossing Video Simulation. Transportation Research Record: Journal of the Transportation Research Board.
   Google Scholar
• Long, J., & Freese, J. (2003). Regression models for categorical dependent variables (2nd ed.). Stata Press.
   Google Scholar
• Loukaitou-Sideris, A., Liggett, R., & Sung, H. (2007). Death on the crosswalk: A Study of Pedestrian-Automobile Collisions in Los Angeles. Journal of Planning Education and Research, 26(3), 338–351. https://doi.org/10.1177/0739456X06297008
   Web of Science ®Google Scholar
• Luce, R. D. (1959). Individual choice behavior: A theoretical analysis. John Wiley and Sons, Inc.
   Google Scholar
• Marisamynathan, S., & Vedagiri, P. (2019). Pedestrian perception-based level-of-service model at signalized intersection crosswalks. Journal of Modern Transportation, 27(4), 266–281. https://doi.org/10.1007/s40534-019-00196-5
   Web of Science ®Google Scholar
• McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In: Zarembka, P. (Ed.), Frontiers in econometrics (pp. 105–142). Academic Press.
   Google Scholar
• Ministry of Road Transport & Highways (MoRTH). (2019). Road Accidents in India-2019.
   Google Scholar
• Ministry of Road Transport & Highways (MoRTH). (2020). Road Accidents in India-2020.
   Google Scholar
• Mukherjee, D., & Mitra, S. (2019). A comparative study of safe and unsafe signalized intersections from the view point of pedestrian behavior and perception. Accident; Analysis and Prevention, 132, 105218. https://doi.org/10.1016/j.aap.2019.06.010
   PubMed Web of Science ®Google Scholar
• Mukherjee, D., Mitra, S., Mukherjee, D., & Mitra, S. (2022). The role of built environment on pedestrian safety and pedestrian activity at urban signalized intersections. Transportation in Developing Economies, 8(2), 33. https://doi.org/10.1007/s40890-022-00168-1
   Web of Science ®Google Scholar
• Pour-Rouholamin, M., & Zhou, H. (2016). Investigating the risk factors associated with pedestrian injury severity in Illinois. Journal of Safety Research, 57, 9–17. https://doi.org/10.1016/j.jsr.2016.03.004
   PubMed Web of Science ®Google Scholar
• Punj, G. N., & Staelin, R. (1978). The choice process for graduate business schools. Journal of Marketing Research, 15(4), 588–598. https://doi.org/10.1177/002224377801500408
   Web of Science ®Google Scholar
• Qi, Y., & Guo, A. (2017). Pedestrian safety under permissive left-turn signal control. International Journal of Transportation Science and Technology, 6(1), 53–62. https://doi.org/10.1016/j.ijtst.2017.05.002
   Google Scholar
• Rankavat, S., & Tiwari, G. (2015). Association Between Built Environment and Pedestrian Fatal Crash Risk in Delhi, India. Transportation Research Record: Journal of the Transportation Research Board, 2519(1), 61–66. https://doi.org/10.3141/2519-07
   Google Scholar
• Rankavat, S., & Tiwari, G. (2016a). Pedestrians perceptions for utilization of pedestrian facilities – Delhi, India. Transportation Research Part F: Traffic Psychology and Behaviour, 42(3), 495–499. https://doi.org/10.1016/j.trf.2016.02.005
   Google Scholar
• Rankavat, S., & Tiwari, G. (2016b). Pedestrians risk perception of traffic crash and built environment features – Delhi India. Safety Science, 87, 1–7. https://doi.org/10.1016/j.ssci.2016.03.009
   Web of Science ®Google Scholar
• RITES. (2010). Transport demand forecast study for DoT. GNCTD.
   Google Scholar
• Rundmo, T. (1996). Associations between risk perceptions and safety. Safety Science, 24(3), 197–209. https://doi.org/10.1016/S0925-7535(97)00038-6
   Web of Science ®Google Scholar
• Sajed, Y., Shafabakhsh, G., & Bagheri, M. (2019). Hotspot location indentification using accident data, traffic and geometric characteristics. Engineering Journal, 23(6), 191–207. http://www.engj.org/ https://doi.org/10.4186/ej.2019.23.6.191
   Google Scholar
• Schneider, R. J., Ryznar, R. M., & Khattak, A. J. (2004). An accident waiting to happen: A spatial approach to proactive pedestrian planning. Accident; Analysis and Prevention, 36(2), 193–211. https://doi.org/10.1016/s0001-4575(02)00149-5
   PubMed Web of Science ®Google Scholar
• Sisiopiku, V. P., & Akin, D. (2003). Pedestrian behavior and perceptions towards various pedestrian facilities: An examination based on observation and survey data. Transportation Research Part F: Traffic Psychology and Behaviour, 6(4), 249–274. https://doi.org/10.1016/j.trf.2003.06.001
   Google Scholar
• Tiwari, G., Bangdiwala, S., Saraswat, A., & Gaurav, S. (2007). Survival analysis: Pedestrian risk exposure at signalized intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 10(2), 77–89. https://doi.org/10.1016/j.trf.2006.06.002
   Web of Science ®Google Scholar
• Tiwari, G., & Goel, R. (2022). Road safety in India: Status report 2021. Technical Report. https://doi.org/10.13140/RG.2.2.24311.50082
   Google Scholar
• UIDAI. (2020). Unique Identification Authority of India, Government of India.
   Google Scholar
• UP Police. (2021). Reported Crashes in UP—2015–19.
   Google Scholar