Abstract
The concept of redesigning public spaces to encourage physical distancing amid the COVID-19 pandemic is being tested around the world. In Canada, municipalities are reallocating underutilized road lanes for active modes of transportation, such as walking and cycling. We evaluated the usage and benefit of these shared spaces to ensure redesign efforts are optimally allocated. We analyzed two sets of closed-circuit television (CCTV) footage before and after the change, covering April 7–13, 2020, at two locations using automated computer vision techniques. We detected and recorded physical distancing violations, traffic safety risks such as midblock crossing, speeds, and traffic conflicts, and generated trajectory maps of all road users. It was found that the redesign was utilized effectively by road users and improved physical distancing compliance without compromising traffic safety. The proposed framework also provides an innovative tool to automatically gather, extract, share, and analyze real-world data to improve response to the COVID-19 pandemic as well as future outbreaks of contagious diseases.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party, specifically the video data. Direct request for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors would like to thank City of Edmonton for providing the data used in this study. The contents do not necessarily reflect the official views or policies of City of Edmonton. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC). Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG).
References
Alsaleh, R., M. Hussein, and T. Sayed. 2020a. “Microscopic behavioural analysis of cyclist and pedestrian interactions in shared spaces.” Can. J. Civ. Eng. 47 (1): 50–62. https://doi.org/10.1139/cjce-2018-0777.
Alsaleh, R., T. Sayed, and M. H. Zaki. 2020b. “Assessing the effect of pedestrians’ use of cell phones on their walking behavior: A study based on automated video analysis.” Transp. Res. Rec. 2672 (35): 46–57. https://doi.org/10.1177/0361198118780708.
Alsaleh, R., and S. Tarek. 2020. “Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning.” Transp. Res. Part F: Traffic Psychol. Behav. 70 (Apr): 37–57. https://doi.org/10.1016/j.trf.2020.02.007.
Alsaleh, R., and S. Tarek. 2021. “Microscopic modeling of cyclists interactions with pedestrians in shared spaces: a Gaussian process inverse reinforcement learning approach.” Transportmetrica A: Transp. Sci. 1–27. https://doi.org/10.1080/23249935.2021.1898487.
Arun, A., M. M. Haque, A. Bhaskar, S. Washington, and T. Sayed. 2021a. “A systematic mapping review of surrogate safety assessment using traffic conflict techniques.” Accid. Anal. Prev. 153 (Apr): 106016. https://doi.org/10.1016/j.aap.2021.106016.
Arun, A., M. M. Haque, A. Bhaskar, S. Washington, and T. Sayed. 2021b. “A bivariate extreme value model for estimating crash frequency by severity using traffic conflicts.” Anal. Methods Accid. Res. 32 (Dec): 100180. https://doi.org/10.1016/j.amar.2021.100180.
Arun, A., M. M. Haque, S. Washington, T. Sayed, and F. Mannering. 2021c. “A systematic review of traffic conflict-based safety measures with a focus on application context.” Anal. Methods Accid. Res. 100185. https://doi.org/10.1016/j.amar.2021.100185.
Astarita, V., C. Caliendo, V. P. Giofrè, and I. Russo. 2020. “Surrogate safety measures from traffic simulation: Validation of safety indicators with intersection traffic crash data.” Sustainability 12 (17): 6974. https://doi.org/10.3390/su12176974.
Autey, J., T. Sayed, and M. H. Zaki. 2012. “Safety evaluation of right-turn smart channels using automated traffic conflict analysis.” Accid. Anal. Prev. 45 (Mar): 120–130. https://doi.org/10.1016/j.aap.2011.11.015.
CDC (Centers for Disease Control and Prevention). 2020. “Social distancing.” Accessed December 20, 2020. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html.
Chen, A. Y., Y. L. Chiu, M. H. Hsieh, P. W. Lin, and O. Angah. 2020. “Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences.” Transp. Res. Part C: Emerging Technol. 119 (Oct): 102744. https://doi.org/10.1016/j.trc.2020.102744.
Chin, H. C., and S. T. Quek. 1997. “Measurement of traffic conflicts.” Saf. Sci. 26 (3): 169–185. https://doi.org/10.1016/S0925-7535(97)00041-6.
CoE (City of Edmonton). 2021. “Driving, cycling & walking.” Accessed March 25, 2021. https://www.edmonton.ca/transportation.aspx.
Franch-Pardo, I., B. M. Napoletano, F. Rosete-Verges, and L. Billa. 2020. “Spatial analysis and GIS in the study of COVID-19. A review.” Sci. Total Environ. 739: 140033. https://doi.org/10.1016/j.scitotenv.2020.140033.
Fu, T., W. Hu, L. Miranda-Moreno, and N. Saunier. 2019. “Investigating secondary pedestrian-vehicle interactions at non-signalized intersections using vision-based trajectory data.” Transp. Res. Part C: Emerging Technol. 105 (Aug): 222–240. https://doi.org/10.1016/j.trc.2019.06.001.
GC (Government of Canada). 2020. “Physical distancing: How to slow the spread of COVID-19.” Accessed December 20, 2020. https://www.canada.ca/en/public-health/services/publications/diseases-conditions/social-distancing.html.
Gouda, M., and K. El-Basyouny. 2017a. “Investigating distance halo effects of mobile photo enforcement on urban roads.” Transp. Res. Rec. 2660 (1): 30–38. https://doi.org/10.3141/2660-05.
Gouda, M., and K. El-Basyouny. 2017b. “Investigating time halo effects of mobile photo enforcement on urban roads.” Transp. Res. Rec. 2660 (1): 39–47. https://doi.org/10.3141/2660-06.
Gouda, M., and K. El-Basyouny. 2020. “Before-and-after empirical Bayes evaluation of achieving bare pavement using anti-icing on urban roads.” Transp. Res. Rec. 2674 (2): 92–101. https://doi.org/10.1177/0361198120902995.
Gouda, M. 2017. “Investigating time and distance halo effects due to mobile photo enforcement on urban roads.” M.Sc. thesis, Dept. of Civil and Environmental Engineering, Univ. of Alberta.
Honey-Rosés, J., et al. 2020. “The impact of COVID-19 on public space: An early review of the emerging questions—Design, perceptions and inequities.” Cities Health 1–17. https://doi.org/10.1080/23748834.2020.1780074.
Hyden, C., and L. Linderholm. 1984. “The Swedish traffic-conflicts technique.” In Proc., Int. Calibration Study of Traffic Conflict Techniques, 133–139. Berlin: Springer. https://doi.org/10.1007/978-3-642-82109-7.
Ismail, K., T. Sayed, and N. Saunier. 2010. “Automated analysis of pedestrian–vehicle conflicts: Context for before-and-after studies.” Transp. Res. Rec. 2198 (1): 52–64. https://doi.org/10.3141/2198-07.
Ismail, K., T. Sayed, N. Saunier, and C. Lim. 2009. “Automated analysis of pedestrian–vehicle conflicts using video data.” Transp. Res. Rec. 2140 (1): 44–54. https://doi.org/10.3141/2140-05.
Ismail, K. A. 2010. “Application of computer vision techniques for automated road safety analysis and traffic data collection.” Ph.D. dissertation, Dept. of Civil Engineering, Univ. of British Columbia.
Johnsson, C., A. Laureshyn, and T. De Ceunynck. 2018. “In search of surrogate safety indicators for vulnerable road users: A review of surrogate safety indicators.” Transp. Rev. 38 (6): 765–785. https://doi.org/10.1080/01441647.2018.1442888.
Johnston, J. E., K. J. Berry, and W. P. Mielke Jr. 2007. “Permutation tests: Precision in estimating probability values.” Perceptual Motor Skills 105 (3): 915–920. https://doi.org/10.2466/pms.105.3.915-920.
Laureshyn, A., and A. Várhelyi. 2018. The Swedish traffic conflict technique. Observer’s manual. Lund, Sweden: Lund Univ.
May, R. B., and M. A. Hunter. 1993. “Some advantages of permutation tests.” Can. Psychol. /Psychologie Canadienne 34 (4): 401. https://doi.org/10.1037/h0078862.
Megahed, N. A., and E. M. Ghoneim. 2020. “Antivirus-built environment: Lessons learned from COVID-19 pandemic.” Sustainable Cities Soc. 61: 102350. https://doi.org/10.1016/j.scs.2020.102350.
Miovision. 2021. “Our first acquisition will create new options for measuring, managing and optimizing traffic.” Accessed March 12, 2021. https://miovision.com/.
Opdyke, J. D. 2003. “Fast permutation tests that maximize power under conventional Monte Carlo sampling for pairwise and multiple comparisons.” J. Mod. Appl. Stat. Methods 2 (1): 27. https://doi.org/10.22237/jmasm/1051747500.
Perkins, S., and J. Harris. 1968. “Traffic conflict characteristics: Accident potential at intersections.” Highway Res. Rec. 225: 35–43.
Pfattheicher, S., L. Nockur, R. Böhm, C. Sassenrath, and M. B. Petersen. 2020. “The emotional path to action: Empathy promotes physical distancing during the COVID-19 pandemic.” Psychol. Sci. 31 (11): 1363–1373. https://doi.org/10.31234/osf.io/y2cg5.
PFP (Paths for People). 2020. “More space needed for safe physical distancing.” Accessed December 20, 2020. https://pathsforpeople.org/2020/04/more-space-for-safe-physical-distancing/.
Prati, A., I. Mikic, M. Trivedi, and R. Cucchiara. 2003. “Detecting moving shadows: Algorithms and evaluation.” IEEE Trans. Pattern Anal. Mach. Intell. 25 (7): 918–923. https://doi.org/10.1109/TPAMI.2003.1206520.
Punn, N. S., S. K. Sonbhadra, and S. Agarwal. 2020. “Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLOv3 and Deepsort techniques.” Accessed December 20, 2020. https://arxiv.org/abs/2005.01385.
Redmon, J., and A. Farhadi. 2018. “Yolov3: An incremental improvement.” Preprint, submitted April 8, 2018. http://arxiv.org/abs/1804.02767.
Sharifi, A., and A. R. Khavarian-Garmsir. 2020. “The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management.” Sci. Total Environ. 142391. https://doi.org/10.1016/j.scitotenv.2020.142391.
Tageldin, A., T. Sayed, K. Shaaban, and M. H. Zaki. 2015. “Automated analysis and validation of right-turn merging behavior.” J. Transp. Saf. Secur. 7 (2): 138–152. https://doi.org/10.1080/19439962.2014.942019.
Zhang, S., M. Abdel-Aty, Y. Wu, and O. Zheng. 2020. “Modeling pedestrians’ near-accident events at signalized intersections using gated recurrent unit (GRU).” Accid. Anal. Prev. 148 (Dec): 105844. https://doi.org/10.1016/j.aap.2020.105844.
Zivkovic, Z. 2004. “Improved adaptive Gaussian mixture model for background subtraction.” In Proc., 17th Int. Conf. on Pattern Recognition 2, 28–31. New York: IEEE.
Zivkovic, Z., and F. van der Heijden. 2006. “Efficient adaptive density estimation per image pixel for the task of background subtraction.” Pattern Recognit. Lett. 27 (7): 773–780. https://doi.org/10.1016/j.patrec.2005.11.005.
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© 2021 American Society of Civil Engineers.
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Received: Dec 28, 2020
Accepted: Jul 9, 2021
Published online: Aug 26, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 26, 2022
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