Volume 7, Issue 1, February 2019, Page: 46-53
Signs and Pedestrian Safety in Automated Transportation Systems
Haiyan Xie, Department of Technology, Illinois State University, Normal IL, USA
Luke Verplaetse, Department of Technology, Illinois State University, Normal IL, USA
Received: Apr. 18, 2019;       Published: Jun. 15, 2019
DOI: 10.11648/j.acis.20190701.16      View  52      Downloads  7
Motor-vehicle accidents have caused many safety concerns ever since cars have been on the road. With the implementation of cooperative and automated vehicles (CAVs) merging into the current crosswalks, signals, and concrete rules, the vehicle-pedestrian interactions create noteworthy safety issues. Through observational findings, intersections with higher signage and pedestrian signals had less likely of a chance for pedestrians to run into an altercation when compared to intersections with just crosswalks and no pedestrian signals. This research presents an optimization framework and an analytical solution with field observations to study whether the implementation of more pedestrian signals could have a great effect on vehicle/pedestrian incidents. The research implements the integrated methods of case studies, modeling and simulation using mathematical and statistical software on correlations and probabilities. This study adds minimal interference to the observations as they naturally occur. The study setting is non-contrived and maintained as natural environment. The collected data is continuous time series and measured using Chi-Square for analysis. After the identification of possible interactions between CAVs and pedestrians based on the data surveyed around the Illinois State University (ISU), this study finds that the safety of pedestrian relies on the intersection design of signs and signals more than the intelligence of CAVs (significance level = 95%). This paper also discusses law enforcement and autonomous driving as a means of lowering pedestrian incidents at intersections. The developed mathematical analysis model and simulations help to verify the influences of transportation signs and intersection designs. The investigation innovatively demonstrates the feasibilities of different methods to protect the pedestrian safety while they enter intersections. The findings from this research can provide decision support for future transportation design and implementation rules of CAVs.
Cooperative and Automated Vehicles (CAVs), Pedestrian, Safety, Signal Design, Crosswalk Analysis
To cite this article
Haiyan Xie, Luke Verplaetse, Signs and Pedestrian Safety in Automated Transportation Systems, Automation, Control and Intelligent Systems. Vol. 7, No. 1, 2019, pp. 46-53. doi: 10.11648/j.acis.20190701.16
You, F., Zhang, R., Lie, G., Wang, H., Wen, H., & Xu, J. (2015). Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Systems with Applications, 42 (14), 5932-5946.
Lee, J., & Park, B. (2012). Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment. IEEE Transactions on Intelligent Transportation Systems, 13 (1), 81-90.
Getting around Illinois. (n.d.). Retrieved from Illinois Department of Transportation: https://www.gettingaroundillinois.com/gai.htm?mt=aadt.
Jome, E. (2018, September 5). Freshman and graduate student numbers up at Illinois State. Retrieved from Illinois State University news: https://news.illinoisstate.edu/2018/09/freshman-and-graduate-student-numbers-up-at-illinois-state/
Monsere, C. M., Kothuri, S., Rampa, A., & Figliozzi, M. A. (2018). An Analysis of the Safety Effectiveness of pedestrian Crossing Enhancements in Oregon. Transportation Board 97th Anual Meeting, 17. Retrieved from https://trid.trb.org/view/1494553.
Kwigizile, V., Oh, J.-S., Van Houten, R., Prieto, D., Boateng, R., Rodriguez, L., Andridge, P. (2015). Evaluation of Michigan's Engineering Improvement for Older Drivers. Western Michigan University, Kalamazoo; Michigan Department of Transportation, 148. Retrieved from https://trid.trb.org/view/1371139.
Hu, W. (2017, November 24). Giving Pedestrians a Head Start Crossing Streets. Retrieved from New York Times: https://www.nytimes.com/2017/11/24/nyregion/pedestrians-new-york-walk-signals.html.
Retting, R. A., Ferguson, S. A., & McCartt, A. T. (2003). A Review of Evidence-Based Traffic Engineering Measures Designed to Reduce Pedestrian–Motor Vehicle Crashes. American Journal of Public Health, 1456-1463. Retrieved from https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.93.9.1456.
Chen, P., & Zhou, J. (2016). Effects of the built environment on automobile-involved pedestrian crash frequency and risk. Journal of Transport & Health, 448-456.
Yanagisawa, M., Swanson, E., & Najm, W. G. (2014). Target crashes and safety benefits estimation methodology for pedestrian crash avoidance/mitigation systems. United States. national Highway Traffic Safety Administration, 1-88. Retrieved from https://rosap.ntl.bts.gov/view/dot/12068J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68–73.
Gilmore, J. (2000). How Radical Measures Can End A century of Slaughter on the Roads. Traffic Engineering & Control, 361-362. Retrieved from https://trid.trb.org/view/670841.
Automated Vehicle for safety. (n.d.). Retrieved from National Highway Traffic Safety Administration: https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety#issue-road-self-driving.
Nyholm, S., & Smids, J. (2016). The Ethics of Accident-Algorithms for Self-Driving Cars: Ethical Theory and Moral Practice, 1275-1289. Retrieved from https://link.springer.com/article/10.1007/s10677-016-9745-2.
Rivara, F. P., Reay, D. T., & Bergman, A. B. (1989). Analysis of Fatal Pedestrian Injuries in King County, WA and Prospects for Prevention. Public Health Reports, 293-297. Retrieved from https://trid.trb.org/view/354285.
Ibrahim, J. M., Day, H., Hirshon, J. M., & El-Setouhy, M. (2012). Road Risk-Perception and Pedestrian Injuries among Students at Ain Shams University, Cairo, Egypt. Journal of Injury and Violence Research, 4(2), 65-72. Retrieved from https://trid.trb.org/view/1163272.
Pollack, K. M., Gielen, A. C., Mohad Ismail, M. N., Mitzner, M., Wu, M., & Links, J. M. (2014). Investigating and improving pedestrian safety in an urban environment. Injury Epidemiology, 1-9. Retrieved from https://www.ncbi.nlm.nih. gov/pmc/articles/PMC5005641/.
Illinois Department of Transportation. (2016, April 4). Retrieved from City Summary Crash Report: Normal Illinois: http://apps.dot.illinois.gov/eplan/desenv/crash/City%20Summaries/Year%202016/Normal.pdf.
Ukkusuri, S., Maranda-Mareno, L. F., Ramadurai, G., & Isa-Tavarez, J. (2012). The role of built environment on pedestrian crash frequency. Safety Science, 1141-1151. Retrieved from https://www.sciencedirect.com/science/article/pii/S0925753511002578#!
Tidwell, J. E., & Doyle, D. P. (1995). Driver and Pedestrian Comprehension of Pedestrian Law and Traffic Control Devices. Transportation Research Record, 119-128.
Oxley, J., Fildes, B., Ihsen, E., Charlton, J., & Day, R. (1997). Differences in traffic judgements between young and old adult pedestrians. Accident Analysis & Prevention, 29 (6), 839-847. Retrieved from https://doi.org/10.1016/S0001-4575 (97) 00053-5.
Koepsell, T., McCloskey, L., & Wolf, M. (2002). Crosswalk Markings and the Risk of Pedestrian–Motor Vehicle Collisions in Older Pedestrians. JAMA, 2136-2143.
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