Transactions on Transport Sciences 2021, 12(3):13-21 | DOI: 10.5507/tots.2022.001

Three-Step Performance Assessment of a Pedestrian Crossing Time Prediction Model

Chiara Grudena, Irena Ištoka Otkovićb, Matjaž Šramla
a. Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
b. Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia

Pedestrian behavior and safety are emerging issues in current transportation. One way to safely study pedestrian dynamics, especially at potential conflict points such as crosswalks, is through micro-simulation. This tool provides the opportunity to repeatedly study pedestrian behavior and safety under different scenarios of interest. However, to obtain reliable results, micro-simulation models need to be calibrated and their parameters fine-tuned. One way to methodically calibrate these models is to identify the outcomes of interest, develop a predictive model for those specific outcomes, and use it as a tool to fine-tune the input parameters of the micro-simulation model. To be reliable, the results of the predictive model should be comparable to those of the micro-simulation model, and these should be validated. The aim of this research is to present a predictive model of pedestrian behavior and to evaluate this model and a conventional micro-simulation model developed using Vissim/Viswalk, given that the chosen common output is pedestrian crossing time. To achieve this goal, a multi-step procedure is followed, which is part of a more general methodological framework for calibrating the Vissim/Viswalk micro-simulation model. This evaluation consisted in a three-step validation procedure, i.e. visual, conceptual and operational validation. Operational (statistical) validation was performed by comparing the variances of the results to understand whether the predicted sample is representative of the simulated sample. A correlation of 97% have been found between the predicted and micro-simulated crossing time values, with mean values of 6.41s and 6.32s for the simulated and predicted crossing times, respectively. Furthermore, both the predicted and simulated crossing time values fall within the ranges found in the literature for field measurements of this variable, indicating good agreement with real observed pedestrian behavior.

Keywords: Pedestrian; micro-simulation; neural network; crossing time; validation

Received: October 14, 2021; Revised: December 29, 2021; Accepted: January 6, 2022; Prepublished online: January 6, 2022; Published: March 9, 2022  Show citation

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Gruden, C., Ištoka Otković, I., & Šraml, M. (2021). Three-Step Performance Assessment of a Pedestrian Crossing Time Prediction Model. Transactions on Transport Sciences, 12(3), 13-21. doi: 10.5507/tots.2022.001
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    References

    1. Advanced Neural Network and Genetic Algorithm Software. (n.d.). Retrieved February 13, 2020 from http://www.wardsystems.com/neuroshell2.asp.
    2. Ambros, J., Novak, J., Borsos, A., Hoz, E., Kiec, M., Machcinik, Š., & Ondrejka, R. (2016). Central European Comparative Study on traffic Safety on Roundabouts. Transportation Research Procedia, 14, 4200-4208. Go to original source...
    3. Bak, R., & Kiec, M. (2012). Influence of Midblock Pedestrian Crossings on Urban Street Capacity. Transportation Research Record: Journal of the Transportation Research Board, 2316, 76-83. Go to original source...
    4. Blue, V.J., & Adler, J.L. (2001). Cellular automata micro-simulation for modeling bi-directional pedestrian walkways. Transportation Research Bart B: Methodological, 35(3), 293-312. doi:10.1016/S0191-2615(99)00052-1. Go to original source...
    5. Bonnett, D.G. (2006). Robust confidence interval for a ration of standard deviations. Applied Psychological Measurement, 30(5), 432-439. doi:10.1177/0146621605279551. Go to original source...
    6. Brilon, W., & Miltner, T. (2005). Capacity at Intersections Without Traffic Signals. Transportation Research Record: Journal of the Transportation Research Board, 1920, 32-40. Go to original source...
    7. Chen, X., Shao, C., & Hao, Y., Influence of Pedestrian Traffic on Capacity of Right Turning Movements at Signalized Intersections. Transportation Research Record: Journal of the Transportation Research Board, 2073, 114-124. Go to original source...
    8. Chen, Y., Persaud, B., Sacchi, E., & Bassani, M. (2013). Investigation of models for relating roundabout safety to predicted speed. Accident Analysis and Prevention, 50, 196-203. Go to original source...
    9. Derrick, B., Ruck, A., Toher, D.; & White, P. (2018). Tests for equality of variances between two samples which contain both paired observations and independent observations. Journal of Applied Quantitative Methods, 13(2), 36-47. Retrieved from http://jaqm.ro/issues/volume-13,issue-2/pdfs/3_BE_AN_DE_PA_.pdf.
    10. Gipps, P.G., & Marksjö, B. (1985). A micro-simulation model for pedestrian flows. Mathematics and Computers in Simulation, 27(2-3), 95-105. doi:10.1016/0378-4754(85)90027-8. Go to original source...
    11. Gorrini, A., Vizzari, G., & Bandini, S. (2016). Age and Group-driven Pedestrian Behavior: from Observations to Simulations. Collective Dynamics, 1 (A3), 1-16. Go to original source...
    12. Gruden, C., Otković, I.I., & Šraml, M. (2020). Neural Networks Applied to Micro-simulation: A Prediction Model for Pedestrian Crossing Time. Sustainability, 12(13), 5355. doi: 10.3390/su12135355. Go to original source...
    13. HCM 2016. Highway Capacity Manual. (2016). Washington, D.C.: Transportation Research Board.
    14. Helbing, D. (1998a). A mathematical Model for the behavior of pedestrians. Retrieved from http://www.theo2.physik.uni-stuttgart.de/helbing.html.
    15. Helbing, D. (1998b). Models for Pedestrian Behavior. Retrieved from http://www.theo2.physik.uni-stuttgart.de/helbing.html.
    16. Hongfei,J., Lili, Y., & Tang, M. (2009). Pedestrian Flow Characteristics Analysis and Model Parameter Calibration in Comprehensive Transport Terminal. Journal of Transportation Systems Engineering and Information Technology, 9(5), 117-123. doi:10.1016/ S1570-6672(08)60082-3. Go to original source...
    17. Jain, A., Gupta, A., & Rastogi, R. (2014). Pedestrian crossing behavior analysis at intersections. International Journal for Traffic and Transport Engineering, 4(1), 103-116. doi:10.7708/ijtte.2014.4(1).08. Go to original source...
    18. Johansson, A., Helbing, D., & Shukla, P.K. (2008). Specification of a Microscopic Pedestrian Model by Evolutionary Adjustment to Video Tracking Data. Advances in Complex Systems. Retrieved from https://arxiv.org/pdf/0810.4587.pdf.
    19. Kitchen, C.M.R. (200). Nonparametric vs Parametric Tests of Location in Biomedical Research. American Journal of Ophtalmology, 147, 571-572. doi:10.1016/j.ajo.2008.06.031. Go to original source...
    20. Kouskoulis, G:, Spyropoulou, I., & Antoniou, C. (2018). Pedestrian simulation: Theoretical models vs. Data driven techniques. International Journal of Transportation Science and Technology, 7, 241-253. doi: 10.1016/j.ijtst.2018.09.001. Go to original source...
    21. Kretz, T., Hnegst, S., &Vortisch, P. (n.d.). Pedestrian flow at bottlenecks validation and calibration of Vissim Social Force Model of Pedestrian Traffic and its empirical foundations. Retrieved from https://arxiv.org/ftp/arxiv/papers/0805/0805.1788.pdf.
    22. Kretz, T., Lohmiller, J. & Sukennik, P. (2018). Some Indications on How to Calibrate the Social Force Model of Pedestrian Dynamics. Transportation Research Record: Journal of the Transportation Research Board, 2672(20), 228-238. doi:10.1177/0361198118786641 Go to original source...
    23. Li, X., & Yeh, A.G.-O. (2001). Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems. Environment and Planning A: Economy and Space, 33(8), 1445-1462. Doi:10.1068/a33210. Go to original source...
    24. Liao, W., Chraibi, M., Seyfried, A., Zhang, J., Zheng, X., & Zhao, Y. (2014). Validation of FDS+Evac for pedestrian simulations in wide bottlenecks. 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, 554-559. doi:10.1109/ITSC.2014.6957748. Go to original source...
    25. Liao, W., Zhang, J., Zheng, X., & Zhao, Y. (2017). A generalized validation procedure for pedestrian models. Simulation Modelling Practice and Theory, 77, 20-31. doi:10.1016/j.simpat.2017.05.002. Go to original source...
    26. Malinovskiy, Y., Wu, Y.J., & Wang, Y. (2008). Video-Based Monitoring of Pedestrian Movements at Signalized Intersections. Transportation Research Record: Journal of the Transportation Research Board, 2073(1), 11-17. doi:10.3141/2073-02. Go to original source...
    27. Marusteri, M., &Bacarea, V. (2010). Comparing groups for statistical differences: how to choose the right statistical test? Biochemia Medica, 20(1), 15-32. Retrieved from https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=73802. Go to original source...
    28. McDonald, J.H. (2014). Handbook of biological statistics third edition. Retrieved from http://udel.edu/~mcdonald/statpermissions.html. Go to original source...
    29. National Joint Committee on Uniform Traffic Control Devices (U.S.). (1971). Manual on uniform traffic control devices for streets and highways. Federal Highway Administration.
    30. Obsu, L.L., Meurer, A., Kassa, S.M., & Klar, A. (2016). Modelling pedestrians' impact on the performance of a roundabout. Traffic flow modelling View project. Neural, Parallel and Scientific Computations, 24, 317-334.
    31. Otković, I. I., Tollazzi, T., & Šraml, M. (2013). Calibration of micro-simulation traffic model using neural network approach. Expert Systems with Applications, 40(15), 5965-5974. doi: 10.1016/j.eswa.2013.05.003. Go to original source...
    32. Otković, I.I., Varevac, D., & Šraml, M. (2015). Analysis of neural network responses in calibration of micro-simulation traffic model. The Electronic Journal of the Faculty of Civil Engineering Osijek E-GFOS, 10, 67-76. Doi:10.13167/2015.10.8. Go to original source...
    33. PTV. (2018). PTV VISSIM 11 User manual. Karlsruhe, Germany.
    34. Rudloff, C., Matyus, T., Seer,S., &Bauer, D. (2011). Can Walking Behavior be Predicted? Analysis of Calibration and Fot of Pedestrian Models. Transportation Research Record: Journal of the Transportation Research Board, 2264(1), 101-109. doi:10.3141/2264-12. Go to original source...
    35. Shiwakoti, N., Sarvi, M., & Rose, G. (2008). Modelling pedestrian behavior under emergency conditions - State-of-the-art and future directions. 31st Australasian Transport Research Forum, 457-473. Retrieved from https://www.australasiantransportresearchforum.org.au/sites/default/files/2008_Shiwakoti_Sarvi_Rose.pdf.
    36. Thakur, S., & Biswas, S. (2019). Assessment of pedestrian-vehicle interaction on urban roads: a critical review. Archives of Transport, 51 (3), 50-63. Go to original source...
    37. Teknomo, K., Takeyama, Y., & Inamura, H. (2000). Review on Microscopic Pedestrian Simulation Model. Proceedings Japan Society of Civil Engineering Conference. Retrieved from https://arxiv.org/abs/1609.01808.
    38. Thompson, L.L., Rivara, F.P., Ayyagari, R.C., & Ebel, B.E. (2013). Impact of social and technological distraction on pedestrian crossing behavior: an observational study. Injury Prevention, 19(4), 232-237. doi: 10.1136/injuryprev-2012-040601. Go to original source...
    39. Toledo, T., Koutsopoulos, H. N., Davol, A., Ben-Akiva, M. E., Burghout, W., Andréasson, I., & Lundin, C. (2003). Calibration and Validation of Microscopic Traffic Simulation Tools: Stockholm Case Study. Transportation Research Record: Journal of the Transportation Research Board, 1831(1), 65-75. doi: 10.3141/1831-08. Go to original source...
    40. Virkler, M. R., & Guell, D. L. (1984). Pedestrian Crossing-Time Requirements at Intersections. Pedestrian and Bicycle facilities, 47-51. Retrieved from http://onlinepubs.trb.org/Onlinepubs/trr/1984/959/959-007.pdf.
    41. Zeng, W., Chen, P., Yu, G., & Wang, Y. (2017). Specification and calibration of a microscopic model for pedestrian dynamic simulation at signalized intersections: a hybrid approach. Transport Research Part C: Emerging Technologies, 80, 37-70. doi: 10.1016/j.trc.2017.04.009. Go to original source...
    42. Zhong, J., Hu, N., Cai, W., Lees, M., & Luo, L. (2015). Density-based evolutionary framework for crowd model calibration. Journal of Computational Science, 6, 11-22. doi: 10.1016/j.jocs.2014.09.002. Go to original source...

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