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Chauhan, Ritvik
- Pedestrian safety analysis at urban midblock section under mixed traffic conditions using time to collision as surrogate safety measure
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Authors
Affiliations
1 Department of Civil Engineering, Dr S.&S.S. Ghandhy Government Engineering College, Surat 395 001, India, IN
2 Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India, IN
1 Department of Civil Engineering, Dr S.&S.S. Ghandhy Government Engineering College, Surat 395 001, India, IN
2 Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India, IN
Source
Current Science, Vol 123, No 9 (2022), Pagination: 1117-1128Abstract
Pedestrians are the most vulnerable road users, and pedestrian safety has become a major concern among researchers in recent years due to the increasing number of road fatalities. Conflict analysis using surrogate safety measures (SSMs) helps study pedestrian safety, as there are several limitations with collision data. Moreover, it is a cost-effective technique compared to historical crash data analysis. The present study analyses pedestrian safety at urban midblock crosswalks using time-to-collision (TTC) as SSM. The data for the present study were collected from four different midblock pedestrian crossing locations in different cities in the western part of India using the videographic technique. The trajectory of pedestrians and vehicles was extracted for micro-level analysis of pedestrian–vehicle interactions. The trajectory data were further used to calculate TTC at regular time intervals during the interaction of pedestrians and vehicles. Two different types of pedestrian road crossing behaviour, viz. vehicle pass first and pedestrian pass first were identified, and TTC analysis was carried out differently for each scenario. The variation of TTC based on gender and vehicle category was analysed to evaluate the influence of such parameters on pedestrian safety. The generalized linear mixed model approach was used to develop linear regression models for TTC based on empirical data. The threshold values for TTC were used to define various safety levels of pedestrians using a clustering approachKeywords
Conflict analysis, mixed traffic condition, pedestrian, safety, time to collision, urban midblock.Full Text
References
- Kadali, B. R. and Vedagiri, P., Pedestrian quality of service at unprotected mid-block crosswalk locations under mixed traffic conditions: towards quantitative approach. Transport, 2016, 33(2),302–314.
- WHO, Global status report on road safety. World Health Organiza-tion, Geneva, Switzerland, 2018, pp. 1–424.
- Jain, U. and Rastogi, R., Pedestrian crossing warrants – a review of global practices. Curr. Sci., 2016, 111(6), 1016–1027.
- Mohan, D., Tsimhoni, O., Sivak, M. and Flannagan, M., Road safetyin India: challenges and opportunities. In UMTR1-2009-1, The University of Michigan Transportation Research Institute, 2009, pp. 1–57.
- Jiang, X., Wang, W. and Bengler, K., Intercultural analyses of time-to-collision in vehicle–pedestrian conflict on an urban mid-block crosswalk. IEEE Trans. Intell. Transp. Syst., 2015, 16, 1048–1053.
- Cafiso, S., Alfonso, M. and Rojas, R., Crosswalk safety evaluation using a pedestrian risk index as traffic conflict measure. In Third International Conference Road Safety and Simulation, Indianapolis Indiana, United States, 2015, pp. 1–15.
- Zhang, Y., Yao, D., Tony, Z. and Qiu, L. P., Vehicle–pedestrian in-teraction analysis in mixed traffic condition. In International Con-ference on Transportation Information and Safety, Wuhan, China,2011, pp. 552–559.
- Fu, T., Miranda-Moreno, L. and Saunier, N., A novel framework toevaluate pedestrian safety at non-signalized locations. Accid. Anal. Prev., 2018, 111, 23–33.
- Saunier, N., Sayed, T. and Lim, C., Probabilistic collision predic-tion for vision-based automated road safety analysis. IEEE Conf. Intell. Transp. Syst. Proc., 2007, 872–878.
- Ismail, K., Sayed, T., Saunier, N. and Lim, C., Automated analysis of pedestrian–vehicle conflicts using video data. Transp. Res. Rec., 2009, 2140(1), 44–54.
- Hayward, J. C., Near-miss determination through use of a scale of danger. Highway Res. Board, 1972, 24–35.
- Nadimi, N., Ragland, D. R. and Mohammadian Amiri, A., An eval-uation of time-to-collision as a surrogate safety measure and a pro-posal of a new method for its application in safety analysis. Transp. Lett., 2020, 12, 491–500.
- Retting, R., Ferguson, S. and McCartt, A., A review of evidence-based traffic engineering measures designed to reduce pedestrian– motor vehicle crashes. Am. J. Public Health, 2003, 93, 1456–1463.
- Ukkusuri, S., Miranda-Moreno, L. F., Ramadurai, G. and Isa-Tava-rez, J., The role of built environment on pedestrian crash frequency. Saf. Sci., 2012, 50, 1141–1151.
- Zegeer, C. et al., Index for assessing pedestrian safety at intersec-tions. Transp. Res. Rec. J. Transp. Res. Board, 2006, 1982, 76–83.
- Haleem, K., Alluri, P. and Gan, A., Analyzing pedestrian crash injuryseverity at signalized and non-signalized locations. Accid. Anal. Prev., 2015, 81, 14–23.
- Sinha, S. N. and Sengupta, S. K., Road traffic accident fatalities inPort Moresby: a ten-year survey. Accid. Anal. Prev., 1989, 21, 297–301.
- Zegeer, C. V., Stewart, J. R., Huang, H. and Lagerwey, P., Safety effects of marked versus unmarked crosswalks at uncontrolled lo-cations analysis of pedestrian crashes in 30 cities. Transp. Res. Rec. J. Transp. Res. Board, 2001, 1773(1), 56–58.
- Dai, D., Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment. J. Transp. Geogr., 2012, 24, 206–214.
- Svensson, Å. and Hydén, C., Estimating the severity of safety rela-ted behaviour. Accid. Anal. Prev., 2006, 38, 379–385.
- Lobjois, R. and Cavallo, V., The effects of aging on street-crossingbehavior: from estimation to actual crossing. Accid. Anal. Prev., 2009, 41, 259–267.
- Hannah, C., Spasić, I. and Corcoran, P., A computational model of pedestrian road safety: the long way round is the safe way home. Accid. Anal. Prev., 2018, 121, 347–357.
- Evans, D. and Norman, P., Predicting adolescent pedestrians’ road-crossing intentions: an application and extension of the theory of planned behaviour. Health Educ. Res., 2003, 18, 267–277.
- Holland, C. and Hill, R., The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations. Accid. Anal. Prev., 2007, 39, 224–237.
- Oxley, J., Fildes, B., Ihsen, E., Charlton, J. and Day, R., Differences in traffic judgements between young and old adult pedestrians. Accid. Anal. Prev., 1997, 29, 839–847.
- Wu, J., Radwan, E. and Abou-Senna, H., Assessment of pedestrian-vehicle conflicts with different potential risk factors at midblock crossings based on driving simulator experiment. Adv. Transp. Stud., 2018, 44, 33–46.
- Chrysler, S. T., Ahmad, O. and Schwarz, C. W., Creating pedestriancrash scenarios in a driving simulator environment. Traffic Inj. Prev., 2015, 16, 12–17.
- Kaparias, I. et al., Development and implementation of a vehicle–pedestrian conflict analysis method: adaptation of a vehicle–vehicle technique. Transp. Res. Rec.: J. Transp. Res. Board, 2010, 75–82.
- Zhang, Y., Yao, D., Qiu, T. Z., Peng, L. and Zhang, Y., Pedestrian safety analysis in mixed traffic conditions using video data. IEEE Trans. Intell. Transp. Syst., 2012, 13, 1832–1844.
- Zhang, Y., Yao, D., Qiu, T. Z. and Peng, L., Scene-based pedestriansafety performance model in mixed traffic situation. IET Intell. Transp. Syst., 2014, 8, 209–218.
- Alhajyaseen, W. K. M. and Iryo-Asano, M., Studying critical pede-strian behavioral changes for the safety assessment at signalized crosswalks. Saf. Sci., 2017, 91, 351–360.
- Hagiwara, T., Hamaoka, H., Yaegashi, T., Miki, K., Ohshima, I. and Naito, M., Estimation of time lag between right-turning vehicles and pedestrians approaching from the right side. Transp. Res. Rec. J. Transp. Res. Board, 2008, 2069, 65–76.
- Ismail, K., Sayed, T., Saunier, N. and Lim, C., Automated analysis of pedestrian–vehicle conflicts using video data. Transp. Res. Rec.J. Transp. Res. Board, 2009, 2140, 44–54.
- Ismail, K., Sayed, T. and Saunier, N., Automated analysis of pedes-trian–vehicle conflicts context for before-and-after studies. Transp Res. Rec. J. Transp. Res. Board, 2010, 2198, 52–64.
- Zheng, Y., Chase, T., Elefteriadou, L., Schroeder, B. and Sisiopiku, V. P., Modeling vehicle–pedestrian interactions outside of cross-walks. Simul. Model. Pract. Theory, 2015, 59, 89–101.
- Lorion, A. C. and Persaud, B., Investigation of surrogate measuresfor safety assessment of urban two-way stop controlled intersec-tions. Can. J. Civ. Eng., 2015, 42, 987–992.
- Ni, Y., Wang, M., Sun, J. and Li, K., Evaluation of pedestrian safety at intersections: a theoretical framework based on pedestrian–vehicle interaction patterns. Accid. Anal. Prev., 2016, 96, 118–129.
- Chen, P., Zeng, W., Yu, G. and Wang, Y., Surrogate safety analysis of pedestrian–vehicle conflict at intersections using unmanned aerial vehicle videos. J. Adv. Transp., 2017, 2017, 1–12.
- Paul, M. and Ghosh, I., A novel approach of safety assessment at uncontrolled intersections using proximal safety indicators. Euro-pean Transport/Transporti Europei, 2017, pp. 1–14.
- Babu, S. S. and Vedagiri, P., Traffic conflict analysis of unsignalised intersections under mixed traffic conditions. European Transport/ Transporti Europei, 2017, pp. 1–12.
- Chen, Q. and Wang, Y., Cellular automata (CA) simulation of theinteraction of vehicle flows and pedestrian crossings on urban low-grade uncontrolled roads. Phys. A: Stat. Mech. Appl., 2015, 432,43–57.
- Chandrappa, A. K., Bhattacharyya, K. and Maitra, B., Estimation ofpost-encroachment time and threshold wait time for pedestrians on a busy urban corridor in a heterogeneous traffic environment: an expe-rience in Kolkata. Asian Transp. Stud., 2016, 4, 421–429.
- Kadali, B. R. and Vedagiri, P., Proactive pedestrian safety evalua-tion at unprotected mid-block crosswalk locations under mixed traffic conditions. Saf. Sci., 2016, 89, 94–105.
- Chen, P., Wu, C. and Zhu, S., Interaction between vehicles and pe-destrians at uncontrolled mid-block crosswalks. Saf. Sci., 2016, 82,68–76.
- Rankavat, S. and Tiwari, G., Pedestrians risk perception of traffic crash and built environment features – Delhi, India. Saf. Sci., 2016, 87, 1–7.
- Pawar, D. S. and Patil, G. R., Critical gap estimation for pedestrians atuncontrolled mid- block crossings on high-speed arterials. Saf. Sci., 2016, 86, 295–303.
- Chaudhari, A., Shah, J., Arkatkar, S., Joshi, G. and Parida, M., In-vestigating effect of surrounding factors on human behaviour at un-controlled mid-block crosswalks in Indian cities. Saf. Sci., 2019, 119, 174–187.
- Chaudhari, A., Shah, J., Arkatkar, S., Joshi, G. and Parida, M.,Evaluation of pedestrian safety margin at mid-block crosswalks in India. Saf. Sci., 2019, 119, 188–198.
- Chen, Z. and Fan, W. (David), A multinomial logit model of pede-strian–vehicle crash severity in North Carolina. Int. J. Transp. Sci. Technol., 2019, 8, 43–52.
- Danaf, M., Sabri, A., Abou-Zeid, M. and Kaysi, I., Pedestrian–vehi-cular interactions in a mixed street environment. Transp. Lett., 2020, 12, 87–99.
- Golakiya, H. D., Chauhan, R. and Dhamaniya, A., Evaluating safe distance for pedestrians on urban midblock sections using trajectory plots. Eur. Transp. Trasp. Eur., 2020, 2015, 1–17.
- Golakiya, H. D. and Dhamaniya, A., Evaluation of pedestrian safety index at urban mid-block. In Urbanization Challenges in Emerging Economies, American Society of Civil Engineers, New Delhi, India,2018, pp. 676–687.
- Indo-HCM, Indian Highway Capacity Manual, CSRI-Central Road Research Institute, New Delhi, 2017.
- Golakiya, H. D., Patkar, M. and Dhamaniya, A., Impact of midblockpedestrian crossing on speed characteristics and capacity of urban arterials. Arab. J. Sci. Eng., 2019, 44, 8675–8689.
- Golakiya, H. D. and Dhamaniya, A., Development of pedestrian crossing facility warrants for urban midblock crosswalks based on vehicular delay. Transp. Dev. Econ., 2021, 18, 1–13.
- Golakiya, H. D. and Dhamaniya, A., Reexamining pedestrian cross-ing warrants based on vehicular delay at urban arterial midblock sections under mixed traffic conditions. J. Transp. Eng. Part A, 2021, 147, 1–18.
- Golakiya, H. and Dhamaniya, A., Evaluating LOS at urban midblock section under the influence of crossing pedestrians in mixed traffic conditions. Transp. Res. Procedia, 2020, 48, 777–792.
- Silgu, M. A. and Çelikoğlu, H. B., K-Means clustering method to classify freeway traffic flow patterns. Pamukkale Univ. J. Eng. Sci., 2014, 20, 232–239.
- Celikoglu, H. B., An approach to dynamic classification of traffic flow patterns. Comput. Civ. Infrastruct. Eng., 2013, 28, 273–288.
- Chauhan, R., Dhamaniya, A. and Arkatkar, S., Spatiotemporal vari-ation of rear-end conflicts at signalized intersections under disor-dered traffic conditions. J. Transp. Eng. Part A, 2021, 147, 14.
- Wei, H., Feng, C., Meyer, E. and Lee, J., Video-capture-based ap-proach to extract multiple vehicular trajectory data for traffic mod-eling. J. Transp. Eng., 2005, 131, 496–505.
- Suzuki, K. and Nakamura, H., Traffic analyzer: the integrated video image processing system for traffic flow analysis. J. East. Asia Soc. Transp. Stud., 2011, 9, 1839–1854.
- Dandona, R., Kumar, G. A., Ameer, M. A., Reddy, G. B. and Dan-dona, L., Under-reporting of road traffic injuries to the police: re-sults from two data sources in urban India. Inj. Prev., 2008, 14, 360–365.
- Singh, P., Lakshmi, P. V. M., Prinja, S. and Khanduja, P., Under-reporting of road traffic accidents in traffic police records – a cross sectional study from North India. Int. J. Community Med. Public Health, 2018, 5, 579.