Assessment of Methodological Alternatives for Modeling the Spatiotemporal Crossing Compliance of Pedestrians at Signalized Midblock Crosswalks
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 2
Abstract
At the midblock crosswalks with either Pedestrian Hybrid Beacons (PHBs) or Traffic Control Signals (TCSs), pedestrians’ crossing compliance can be considered in terms of the space (crossing location with respect to stripes) and time (waiting for a WALK signal). In any crossing incident, two combinations of a scenario are performed jointly; pedestrians can cross within stripes when WALK signal is either active or inactive, also they can cross outside the stripes when WALK signal is either active or inactive. This study presents the assessment of alternative methodologies for modeling the spatiotemporal crossing compliance of pedestrians. It uses data collected from five signalized crosswalks located along four major arterials in Las Vegas, Nevada. Three models, multinomial logit, ordered logit, and logistic regression (LR), are proposed and evaluated. Bayesian information criterion (BIC), Akaike information criterion (AIC), and misclassification error are the three performance measures used to compare the models. Based on these performance measures, the logistic regression outperformed the other two, as it had low AIC and BIC, as well as low misclassification error. This model was then used to evaluate the factors associated with the pedestrians’ spatiotemporal crossing compliance. The logistic regression results revealed that the active WALK sign and the crossing incidences involve female(s) only are positively associated with pedestrians’ spatiotemporal crossing compliance. On the other hand, the optional one/two cross stages, pedestrian wait time, children and teens, as well as people who cross while riding a bike are negatively associated with spatiotemporal crossing compliance.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. The items that can be made available upon request are data and codes generated for this study.
References
Akaike, H. 1974. “A new look at the statistical model identification.” IEEE Trans. Autom. Control 19 (6): 716–723. https://doi.org/10.1109/TAC.1974.1100705.
Brosseau, M., S. Zangenehpour, N. Saunier, and L. Miranda-Moreno. 2013. “The impact of waiting time and other factors on dangerous pedestrian crossings and violations at signalized intersections: A case study in Montreal.” Transp. Res. Part F: Psychol Behav. 21 (Nov): 159–172. https://doi.org/10.1016/j.trf.2013.09.010.
Chimba, D., D. Emaasit, and B. Kutela. 2012. “Likelihood parameterization of bicycle crash injury severities.” J. Transp. Technol. 02 (03): 213–219. https://doi.org/10.4236/jtts.2012.23023.
Fabozzi, F. J., S. M. Focardi, S. T. Rachev, and B. G. Arshanapalli. 2014. The basics of financial econometrics. Chichester, UK: Wiley.
FHWA (Federal Highway Administration). 2009. Manual on uniform traffic control devices (MUTCD). Washington, DC: FHWA, DOT.
Guo, H., F. Zhao, W. Wang, Y. Zhou, Y. Zhang, and G. Wets. 2014. “Modeling the perceptions and preferences of pedestrians on crossing facilities.” Discrete Dyn. Nat. Soc. 2014: 1–8. https://doi.org/10.1155/2014/949475.
Kim, K., I. M. Brunner, and E. Yamashita. 2008. “Modeling violation of Hawaii’s crosswalk law.” Accid. Anal. Prev. 40 (3): 894–904. https://doi.org/10.1016/j.aap.2007.10.004.
Koh, P. P., and Y. D. Wong. 2014. “Gap acceptance of violators at signalised pedestrian crossings.” Accid. Anal. Prev. 62 (Jan): 178–185. https://doi.org/10.1016/j.aap.2013.09.020.
Kuhn, M. 2019. “caret: Classification and Regression Training. R package version 6.0-84.” Accessed June 1, 2017. https://doi.org/10.1016/j.aap.2013.09.020.
Mergia, W. Y., D. Eustace, D. Chimba, and M. Qumsiyeh. 2013. “Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio.” Accid. Anal. Prev. 55 (Jun): 202–210. https://doi.org/10.1016/j.aap.2013.03.008.
R Core Team. 2018. “R: A language and environment for statistical computing.” Accessed November 17, 2018. https://www.r-project.org/.
Ripley, B., B. Venables, D. M. Bates, K. Hornik, A. Gebhardt, and D. Firth. 2018. “Package ‘MASS’.” Accessed June 2018. https://cran.r-project.org/web/packages/nnet/index.html.
Ripley, B., and W. Venables. 2016. “Package ‘Nnet’.” Accessed June 2018. https://cran.r-project.org/web/packages/nnet/index.html.
Rosenbloom, T. 2009. “Crossing at a red light: Behaviour of individuals and groups.” Transp. Res. Part F: Psychol. Behav. 12 (5): 389–394. https://doi.org/10.1016/j.trf.2009.05.002.
Sisiopiku, V., and D. Akin. 2003. “Pedestrian behaviors at and perceptions towards various pedestrian facilities: An examination based on observation and survey data.” Transp. Res. Part F: Traffic Psychol. Behav. 6 (4): 249–274. https://doi.org/10.1016/j.trf.2003.06.001.
Stone, M. 1979. “Comments on model selection criteria of Akaike and Schwarz.” J. Royal Stat. Society. Ser. B (Methodological) 41 (2): 276–278. https://doi.org/10.1111/j.2517-6161.1979.tb01084.x.
UCLA: Statistical Consulting Group. 2014. “Introduction to generalized linear mixed models.” Accessed May 31, 2017. https://stats.idre.ucla.edu/other/mult-pkg/introduction-to-generalized-linear-mixed-models/.
Wang, Y., and Q. Liu. 2006. “Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships.” Fish. Res. 77 (2): 220–225. https://doi.org/10.1016/j.fishres.2005.08.011.
Washington, S. P., M. G. Karlaftis, and F. L. Mannering. 2011. Statistical and econometric methods for transportation data analysis. Boca Raton, FL: CRC Press.
Woodridge, J. M. 2012. Introductory economics a modern approach. 5th ed. Mason, OH: South-Western, Cengage Learning.
Yanfeng, W., Z. Shunying, W. Hong, L. Bing, and L. Mei. 2010. “Characteristic analysis of pedestrian violation crossing behavior based on logistics model.” In Proc., Int. Conf. on Intelligent Computation Technology and Automation, 926. New York: IEEE.
Zheng, Y., T. Chase, L. Elefteriadou, B. Schroeder, and V. P. Sisiopiku. 2015. “Modeling vehicle-pedestrian interactions outside of crosswalks.” Simul. Modell. Pract. Theory 59 (Dec): 89–101. https://doi.org/10.1016/j.simpat.2015.08.005.
Zhou, Z.-P., Y.-S. Liu, W. Wang, and Y. Zhang. 2013. “Multinomial logit model of pedestrian crossing behaviors at signalized intersections.” Discrete Dyn. Nat. Soc. 2013: 1–8. https://doi.org/10.1155/2013/172726.
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©2019 American Society of Civil Engineers.
History
Received: Dec 31, 2018
Accepted: Jul 1, 2019
Published online: Nov 28, 2019
Published in print: Feb 1, 2020
Discussion open until: Apr 28, 2020
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