Table of Contents Author Guidelines Submit a Manuscript
Journal of Advanced Transportation
Volume 2019, Article ID 9072358, 15 pages
https://doi.org/10.1155/2019/9072358
Research Article

Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

1Civil, Construction, and Environmental Engineering, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USA
2Texas A&M Transportation Institute, 505 E. Huntland Dr., Austin, TX 78752, USA
3Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, USA
4School of Public Affairs, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USA
5Texas A&M Transportation Institute, 3135 TAMU, College Station, TX 77843, USA

Correspondence should be addressed to Arash Jahangiri; ude.usds@irignahaja

Received 1 January 2019; Revised 30 March 2019; Accepted 16 May 2019; Published 16 June 2019

Guest Editor: Milad Haghani

Copyright © 2019 Mahdie Hasani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Fatality Analysis Reporting System (FARS), https://www-fars.nhtsa.dot.gov/Main/index.aspx, 2018.
  2. Beyond Traffic 2045, US Department of Transportation, 2015.
  3. D. Johnstone, K. Nordback, and S. Kothuri, “Annual average nonmotorized traffic estimates from manual counts: quantifying error,” Transportation Research Record: Journal of the Transportation Research Board, 2017. View at Google Scholar · View at Scopus
  4. S. Hankey, G. Lindsey, and J. Marshall, “Day-of-year scaling factors and design considerations for nonmotorized traffic monitoring programs,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2468, no. 1, pp. 64–73, 2014. View at Publisher · View at Google Scholar
  5. A. R. Budowski, Developing Expansion Factors to Estimate Cyclist Seasonal Average Daily Traffic in Winnipeg, MB, 2015.
  6. K. Nordback, W. Marshall, B. Janson, and E. Stolz, “Estimating annual average daily bicyclists,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2339, pp. 90–97, 2013. View at Google Scholar · View at Scopus
  7. T. Nosal, Improving the Accuracy of Bicycle AADT Estimation: Temporal Patterns, Weather and Bicycle AADT Estimation Methods, McGill University Libraries, 2014.
  8. T. Nosal, L. F. Miranda-Moreno, and Z. Krstulic, “Incorporating weather: comparative analysis of annual average daily bicyclist traffic estimation methods,” Transportation Research Record, vol. 2468, no. 1, pp. 100–110, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Beitel and L. F. Miranda-Moreno, “Methods for improving and automating the estimation of average annual daily bicyclists,” in Proceedings of the 95th Annual MeetingTransportation Research Board, 2016.
  10. D. J. Fagnant and K. Kockelman, “A direct-demand model for bicycle counts: the impacts of level of service and other factors,” Environment and Planning B: Planning and Design, vol. 43, no. 1, pp. 93–107, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Strauss, L. F. Miranda-Moreno, and P. Morency, “Cyclist activity and injury risk analysis at signalized intersections: a bayesian modelling approach,” Accident Analysis & Prevention, vol. 59, pp. 9–17, 2013. View at Google Scholar
  12. M. Tabeshian and L. Kattan, “Modeling nonmotorized travel demand at intersections in calgary, Canada: use of traffic counts and geographic information system data,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2430, no. 1, pp. 38–46, 2014. View at Publisher · View at Google Scholar
  13. R. Schneider, T. Henry, M. Mitman, L. Stonehill, and J. Koehler, “Development and application of a pedestrian volume model in San Francisco, California,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2299, pp. 65–78, 2012. View at Google Scholar · View at Scopus
  14. M. H. Cameron, “A method of measuring exposure to pedestrian accident risk,” Accident Analysis Prevention, vol. 14, no. 5, pp. 397–405, 1982. View at Publisher · View at Google Scholar
  15. M.-R. Lin and J. F. Kraus, “A review of risk factors and patterns of motorcycle injuries,” Accident Analysis & Prevention, vol. 41, no. 4, pp. 710–722, 2009. View at Publisher · View at Google Scholar
  16. Y. Peng, Y. Chen, J. Yang, D. Otte, and R. Willinger, “A study of pedestrian and bicyclist exposure to head injury in passenger car collisions based on accident data and simulations,” Safety Science, vol. 50, no. 9, pp. 1749–1759, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Xie, K. Ozbay, A. Kurkcu, and H. Yang, “Analysis of traffic crashes involving pedestrians using big data: investigation of contributing factors and identification of hotspots,” Risk Analysis, vol. 37, no. 8, pp. 1459–1476, 2017. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Boufous, L. De Rome, T. Senserrick, and R. Ivers, “Risk factors for severe injury in cyclists involved in traffic crashes in Victoria, Australia,” Accident Analysis & Prevention, vol. 49, pp. 404–409, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. D. K. Tinsworth, S. P. Cassidy, and C. Polen, “Bicycle-related injuries: injury, hazard, and risk patterns,” International Journal for Consumer and Product Safety, vol. 1, no. 4, pp. 207–220, 1994. View at Publisher · View at Google Scholar
  20. Federal Highway Administration, “PBCAT - pedestrian and bicycle crash analysis tool version 2.0. publication FHWA-HRT-06-090,” U.S. Department of Transportation.
  21. S. Ryan, D. E. Sidelinger, S. Saitowitz, D. Browner, S. Vance, and L. McDermid, “Designing and implementing a regional active transportation monitoring program through a county-MPO-university collaboration,” American Journal of Health Promotion, vol. 28, no. 3, pp. S104–S111, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Greene-Roesel, M. C. Diogenes, and D. R. Ragland, Estimating Pedestrian Accident Exposure. Publication UCB-ITS-PRR-2010-3, Calofornia Path Program, 2010.
  23. W. Schwartz and C. Porter, Bicycle and Pedestrian Data: Sources, Needs, and Gaps, 2000.
  24. T. Schweizer, Methods for Counting Pedestrians, 2005.
  25. R. J. Schneider, L. S. Arnold, and D. R. Ragland, “Methodology for counting pedestrians at intersections,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2140, no. 1, pp. 1–12, 2009. View at Publisher · View at Google Scholar
  26. R. J. Schneider, L. S. Arnold, and D. R. Ragland, “Pilot model for estimating pedestrian intersection crossing volumes,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2140, no. 1, pp. 13–26, 2009. View at Publisher · View at Google Scholar
  27. M. Abdel-Aty and X. Wang, “Crash estimation at signalized intersections along corridors: analyzing spatial effect and identifying significant factors,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1953, pp. 98–111, 2006. View at Publisher · View at Google Scholar
  28. J. B. Griswold, A. Medury, and R. J. Schneider, “Pilot models for estimating bicycle intersection volumes,” Transportation Research Record, vol. 2247, no. 1, pp. 1–7, 2011. View at Publisher · View at Google Scholar
  29. I. N. Sener, K. Lee, J. G. Hudson, M. Martin, and B. Dai, The Challenge of Safe and Active Transportation: Examination of Pedestrian and Bicycle Crashes in the Austin District, Texas A & M Transportation Institute, 2018.
  30. M. Hasani, A. Jahangiri, and S. G. Machiani, “Developing models for matching of short-term and long-term data collection sites to improve the estimation of average annual daily bicyclists,” in Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2931–2936, IEEE, Maui, HI, USA, 2018. View at Publisher · View at Google Scholar
  31. Y. P. Aggarwal, Better Sampling: Concepts, Techniques, and Evaluation, Stosius Inc/Advent Books Division, 1988.
  32. J. A. Molino, J. F. Kennedy, P. L. Johnson, P. A. Beuse, A. K. Emo, and A. Do, “Pedestrian and bicyclist exposure to risk: methodology for estimation in an urban environment,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2140, no. 1, pp. 145–156, 2009. View at Publisher · View at Google Scholar
  33. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017. View at Publisher · View at Google Scholar
  34. H. Hediyeh, T. Sayed, M. H. Zaki, and K. Ismail, “Automated analysis of pedestrian crossing speed behavior at scramble-phase signalized intersections using computer vision techniques,” International Journal of Sustainable Transportation, vol. 8, no. 5, pp. 382–397, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. M. S. Shirazi and B. T. Morris, “Vision-based turning movement monitoring:count, speed & waiting time estimation,” IEEE Intelligent Transportation Systems Magazine, vol. 8, no. 1, pp. 23–34, 2016. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Ryus, E. Ferguson, K. M. Laustsen et al., Methods and Technologies for Pedestrian and Bicycle Volume Data Collection, CiteSeerX, 2014. View at Publisher · View at Google Scholar
  37. Y. Guo, T. Sayed, and M. H. Zaki, “Automated analysis of pedestrian walking behaviour at a signalised intersection in China,” IET Intelligent Transport Systems, vol. 11, no. 1, pp. 28–36, 2017. View at Publisher · View at Google Scholar · View at Scopus
  38. G. Ponte, Z. L. Szpak, J. E. Woolley, and D. J. Searson, “Using specialised cyclist detection software to count cyclists and determine cyclist travel speed from video,” in Proceedings of the Australasian Road Safety Research Policing Education Conference, Melbourne, Victoria, Australia, 2014.
  39. H. Tang, “Development of a multiple-camera tracking system for accurate traffic performance measurements at intersections,” http://www.its.umn.edu/Publications/ResearchReports/reportdetail.html?id=2254.
  40. M. S. Shirazi and B. Morris, “Vision-based pedestrian monitoring at intersections including behavior & crossing count,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), IEEE, 2016. View at Scopus
  41. D. Kasper, G. Weidl, T. Dang et al., “Object-oriented bayesian networks for detection of lane change maneuvers,” IEEE Intelligent Transportation Systems Magazine, vol. 4, no. 3, pp. 19–31, 2012. View at Publisher · View at Google Scholar
  42. M. Zaki, T. Sayed, K. Ismail, and F. Alrukaibi, “Use of computer vision to identify pedestrians’ nonconforming behavior at urban intersections,” Transportation Research Record, vol. 2279, pp. 54–64, 2012. View at Google Scholar · View at Scopus
  43. F. Garcia, P. Cerri, A. Broggi, A. De La Escalera, and J. M. Armingol, “Data fusion for overtaking vehicle detection based on radar and optical flow,” in Proceedings of the IEEE Intelligent Vehicles Symposium, IEEE, 2012. View at Scopus
  44. J. Roll, BicycLe Traffic Count Factoring: An Examination of National, State and Locally Derived Daily Extrapolation Factors, Dissertations and Theses, 2013.
  45. S. Hankey, G. Lindsey, X. Wang et al., “Estimating use of non-motorized infrastructure: models of bicycle and pedestrian traffic in minneapolis, MN,” Landscape and Urban Planning, vol. 107, no. 3, pp. 307–316, 2012. View at Publisher · View at Google Scholar · View at Scopus
  46. G. Lindsey, S. X. Hankey, Wang., and J. Chen, The Minnesota Bicycle and Pedestrian Counting Initiative: Methodologies for Non-Motorized Traffic Monitoring, 2013.
  47. D. Beitel, S. McNee, and L. F. Miranda-Moreno, “Quality measure of short-duration bicycle counts,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2644, no. 1, pp. 64–71, 2017. View at Publisher · View at Google Scholar
  48. S. Gadda, A. Magoon, and K. M. Kockelman, Estimates of Aadt: Quantifying the Uncertainty, Transportation Research Board, 2007.
  49. L. Miranda-Moreno, T. Nosal, R. Schneider, and F. Proulx, “Classification of bicycle traffic patterns in five North American cities,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2339, pp. 68–79, 2013. View at Google Scholar · View at Scopus
  50. D. Johnstone, K. Nordback, and M. Lowry, Collecting Network-Wide Bicycle and Pedestrian Data: A Guidebook for When and Where to Count, Washington State Department of Transportation, 2017.
  51. S. Turner, I. N. Sener, M. E. Martin et al., “Synthesis of methods for estimating pedestrian and bicyclist exposure to risk at areawide levels and on specific transportation facilities,” Tech. Rep. FHWA-SA-17-041, The Federal Highway Administration (FHWA), 2017. View at Google Scholar
  52. E. Radwan, H. Abou-Senna, A. Mohamed et al., Assessment of Sidewalk/Bicycle-Lane Gaps with Safety and Developing Statewide Pedestrian Crash Rates, 2016.
  53. L. F. Beck, A. M. Dellinger, and M. E. O'Neil, “Motor vehicle crash injury rates by mode of travel, United States: using exposure-based methods to quantify differences,” American Journal of Epidemiology, vol. 166, no. 2, pp. 212–218, 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. S. Blaizot, F. Papon, M. M. Haddak, and E. Amoros, “Injury incidence rates of cyclists compared to pedestrians, car occupants and powered two-wheeler riders, using a medical registry and mobility data, Rhône County, France,” Accident Analysis & Prevention, vol. 58, pp. 35–45, 2013. View at Publisher · View at Google Scholar · View at Scopus
  55. B. Zerhari, A. Ait-Lahcen, and S. Mouline, Big Data Clustering: Algorithms and Challenges, Tetuan, Morocco, 2015.
  56. K. Li, H. Jiang, and A. Y. Zomaya, Big Data Management and Processing, Chapman and Hall/CRC, 2017. View at Publisher · View at Google Scholar
  57. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, NY, USA, 2009. View at MathSciNet
  58. R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 2, pp. 411–423, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  59. M. G. Jones, S. Ryan, J. Donlon, L. Ledbetter, D. R. Ragland, and L. S. Arnold, “Seamless travel: measuring bicycle and pedestrian activity in san diego county and its relationship to land use, transportation, safety, and facility type,” PATH Research Report, 2010. View at Google Scholar
  60. C. V. Zegeer, J. R. Stewart, H. H. Huang, P. A. Lagerwey, J. Feaganes, and B. J. Campbell, Safety Effects of Marked versus Unmarked Crosswalks at Uncontrolled Locations: Final Report and Recommended Guidelines, 2005.
  61. Estimating Pedestrian Accident Exposure, Safe Transportation Education and Research Center (SafeTREC), California Path Program Institute Of Transportation Studies University Of California, Berkeley, Calif, USA.
  62. J. C. Gower, “A general coefficient of similarity and some of its properties,” Biometrics, vol. 27, no. 4, pp. 857–872, 1971. View at Publisher · View at Google Scholar
  63. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297, University of California Press, 1967. View at MathSciNet
  64. L. Kaufman and P. Rousseeuw, “Clustering by means of medoids,” in Statistical Data Analysis Based on the L_1–Norm and Related Methods, North-Holland, Amsterdam.
  65. E. Bochinski, V. Eiselein, and T. Sikora, High-Speed Tracking-by-Detection Without Using Image Information [Challange Winner IWOT4S], 2017. View at Publisher · View at Google Scholar
  66. M. J. Chan-Lau, Lasso Regressions and Forecasting Models in Applied Stress Testing, International Monetary Fund, 2017. View at Publisher · View at Google Scholar
  67. T. R. Miller, “Costs and Functional Consequences of U.S. Roadway Crashes,” Accident Analysis & Prevention, vol. 25, no. 5, pp. 593–607, 1993. View at Publisher · View at Google Scholar
  68. E. Zaloshnja and T. Miller, Unit Costs of Medium and Heavy Truck Crashes, 2007.
  69. T. R. Miller, E. Zaloshnja, B. A. Lawrence, J. Crandall, J. Ivarsson, and A. E. Finkelstein, “Pedestrian and pedalcyclist injury costs in the united states by age and injury severity,” in Proceedings of the 48th Annual Proceedings - Association for the Advancement of Automotive Medicine, vol. 48, pp. 265–284, 2004. View at Scopus
  70. Maximum Police-Reported Injury Severity Within Selected Crash Geometries, Chapter 4, FHWA-HRT-05-051, 2005.
  71. E. Zaloshnja, T. Miller, F. Council, and B. Persaud, “Comprehensive and human capital crash costs by maximum police-reported injury severity within selected crash types,” in Proceedings of the 48th Annual Proceedings - Association for the Advancement of Automotive Medicine, vol. 48, pp. 251–263, 2004. View at Scopus
  72. Abbreviated Injury Scale (AIS), Association for the Advancement of Automotive Medicine, 2018, https://www.aaam.org/abbreviated-injury-scale-ais/. View at Publisher · View at Google Scholar
  73. G. Lindsey, K. Nordback, and M. A. Figliozzi, “Institutionalizing bicycle and pedestrian monitoring programs in three states,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2443, no. 1, pp. 134–142, 2014. View at Publisher · View at Google Scholar
  74. X. Liu and J. Griswold, “Pedestrian volume modeling: a case study of San Francisco,” Yearbook of the Association of Pacific Coast Geographers, vol. 71, no. 1, pp. 164–181, 2009. View at Publisher · View at Google Scholar
  75. L. F. Miranda-Moreno and D. Fernandes, “Modeling of pedestrian activity at signalized intersections,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2264, no. 1, pp. 74–82, 2011. View at Publisher · View at Google Scholar
  76. S. S. Pulugurtha and S. R. Repaka, “Assessment of models to measure pedestrian activity at signalized intersections,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2073, pp. 39–48, 2008. View at Google Scholar · View at Scopus
  77. San Diego Metropolitan Transit System, “How to Ride: Bikes,” 2018, https://www.sdmts.com/rider-info-how-ride/bikes.
  78. City of San Diego, “Vision zero saves lives,” City of San Diego Comprehensive Pedestrian Collision Analysis, Transportation and Stormwater Division, 2014.
  79. City of San Diego, Zero Traffic Related Fatalities and Severe Injuries by 2025, 2018.
  80. A. P. McGinn, K. R. Evenson, A. H. Herring, S. L. Huston, and D. A. Rodriguez, “The association of perceived and objectively measured crime with physical activity: A cross-sectional analysis,” Journal of Physical Activity & Health, vol. 5, no. 1, pp. 117–131, 2008. View at Publisher · View at Google Scholar · View at Scopus
  81. G. G. Bennett, L. H. McNeill, K. Y. Wolin, D. T. Duncan, E. Puleo, and K. M. Emmons, “Safe to walk? Neighborhood safety and physical activity among public housing residents,” PLoS Medicine, vol. 4, no. 10, pp. 1599–1607, 2007. View at Google Scholar · View at Scopus
  82. A. Bowen, “New bike sharing program launching in San Diego,” KPBS Public Media, 2018, http://www.kpbs.org/news/2018/feb/15/new-bike-sharing-launches-san-diego-limebike/. View at Google Scholar
  83. San. Diego, “Association of Governments,” Bayshore Bikeway Fact Sheet, 2018. View at Google Scholar
  84. J. Strauss and L. F. Miranda-Moreno, “Spatial modeling of bicycle activity at signalized intersections,” Journal of Transport and Land Use, vol. 6, no. 2, pp. 47–58, 2013. View at Publisher · View at Google Scholar · View at Scopus
  85. TE. McMillan, “Walking and Biking to School, Physical Activity, and Health Outcomes,” in Active Living Research, a National Program of the Robert Wood Johnson Foundation, Princeton, NJ, 2009. View at Google Scholar
  86. The Fatal Fifteen Intersections - Vision Zero, “Circulate San Diego,” http://www.circulatesd.org/fatal15sd, 2018.