Advances in pedestrian travel monitoring: Temporal patterns and spatial characteristics using pedestrian push-button data from Utah traffic signals

Prasanna Humagain

Utah State University

Patrick Singleton

Utah State University

https://orcid.org/0000-0002-9319-2333

DOI: https://doi.org/10.5198/jtlu.2021.2112

Keywords: pedestrian travel monitoring, pedestrian push-button, empirical clustering, factor groups, land use


Abstract

In this study, we advanced pedestrian travel monitoring using a novel data source: pedestrian push-button presses obtained from archived traffic signal controller logs at more than 1,500 signalized intersections in Utah over one year. The purposes of this study were to: (1) quantify pedestrian activity patterns; (2) create factor groups and expansion/adjustment factors from these temporal patterns; and (3) explore relationships between patterns and spatial characteristics. Using empirical clustering, we classified signals into five groups, based on normalized hourly/weekly counts (each hour’s proportion of weekly totals, or the inverse of the expansion factors), and three clusters with similar monthly adjustment factors. We also used multinomial logit models to identify spatial characteristics (land use, built environment, socio-economic characteristics, and climatic regions) associated with different temporal patterns. For example, we found that signals near schools were much more likely to have bimodal daily peak hours and lower pedestrian activity during out-of-school months. Despite these good results, our hourly/weekday patterns differed less than in past research, highlighting the limits of existing infrastructure for capturing all kinds of activity patterns. Nevertheless, we demonstrated that signals with push-button data are a useful supplement to existing permanent counters within a broader pedestrian traffic monitoring program.


Author Biography

Prasanna Humagain, Utah State University

Civil & Environmental Engineering, PhD Candidate


References

ATKINS. (2016). Automated traffic signal performance measures reporting details. Atlanta, GA: Georgia Department of Transportation. Retrieved from https://udottraffic.utah.gov/ATSPM/Images/ATSPM_Reporting_Details.pdf

Blanc, B., Johnson, P., Figliozzi, M., Monsere, C., & Nordback, K. (2015). Leveraging signal infrastructure for nonmotorized counts in a statewide program: Pilot study. Transportation Research Record: Journal of the Transportation Research Board, 2527, 69–79. https://doi.org/10.3141/2527-08

Bu, F., Greene-Roesel, R., Diogenes, M. C., & Ragland, D. R. (2007). Estimating pedestrian accident exposure: Automated pedestrian counting devices report. Berkeley, CA: UC Berkeley Traffic Safety Center. Retrieved from https://escholarship.org/uc/item/0p27154n

Day, C. M., Premachandra, H., & Bullock, D. M. (2011). Rate of pedestrian signal phase actuation as a proxy measurement of pedestrian demand. Paper presented at the 90th Annual Meeting of the Transportation Research Board, Washington, DC. Retrieved from https://docs.lib.purdue.edu/civeng/24/

Day, C. M., Bullock, D. M., Li, H., Remias, S. M., Hainen, A. M., Freije, R. S., ... & Brennan, T. M. (2014). Performance measures for traffic signal systems: An outcome-oriented approach. West Lafayette, IN: Purdue University. Retrieved from https://doi.org/10.5703/1288284315333

Day, C. M., Taylor, M., Mackey, J., Clayton, R., Patel, S. K., Xie, G., ... & Bullock, D. (2016). Implementation of automated traffic signal performance measures. ITE Journal, 86(8), 26–34. https://trid.trb.org/view/1418795

Diogenes, M. C., Greene-Roesel, R., Arnold, L. S., & Ragland, D. R. (2007). Pedestrian counting methods at intersections: A comparative study. Transportation Research Record: Journal of the Transportation Research Board, 2002(1), 26–30. https://doi.org/10.3141/2002-04

Federal Highway Administration (FHWA). (2016). Traffic monitoring guide. Washington, DC: U.S. Department of Transportation. Retrieved from https://www.fhwa.dot.gov/policyinformation/tmguide/

Greene-Roesel, R., Diogenes, M. C., Ragland, D. R., & Lindau, L. A. (2008). Effectiveness of a commercially available automated pedestrian counting device in urban environments: Comparison with manual counts. Presented at the 87th Annual Meeting of the Transportation Research Board, Washington, DC. Retrieved from https://escholarship.org/uc/item/2n83w1q8

Griswold, J. B., Medury, A., Schneider, R. J. & Grembek, O. (2018). Comparison of pedestrian count expansion methods: Land-use groups versus empirical clusters. Transportation Research Record: Journal of the Transportation Research Board, 2672(43), 87–97. https://doi.org/10.1177/0361198118793006

Hankey, S., Lindsey, G., Wang, X., Borah, J., Hoff, K., Utecht, B., & Xu, Z. (2012). Estimating use of non-motorized infrastructure: Models of bicycle and pedestrian traffic in Minneapolis, MN. Landscape and Urban Planning, 107(3), 307–316. https://doi.org/10.1016/j.landurbplan.2012.06.005

Kothuri, S., Nordback, K., Schrope, A., Phillips, T., & Figliozzi, M. (2017). Bicycle and pedestrian counts at signalized intersections using existing infrastructure: Opportunities and challenges. Transportation Research Record: Journal of the Transportation Research Board, 2644, 11–18. https://doi.org/10.3141/2644-02

Medury, A., Griswold, J. B., Huang, L. & Grembek, O. (2019). Pedestrian count expansion methods: Bridging the gap between land-use groups and empirical clusters. Transportation Research Record: Journal of the Transportation Research Board, 2673(5), 720–730. https://doi.org/10.1177/0361198119838266

Miranda-Moreno, L. F., & Lahti, A. C. (2013). Temporal trends and the effect of weather on pedestrian volumes: A case study of Montreal, Canada. Transportation Research Part D: Transport and Environment, 22, 54–59. https://doi.org/10.1016/j.trd.2013.02.008

Montero, P., & Vilar, J. A. (2014). TSclust: An R package for time series clustering. Journal of Statistical Software, 62(1), 1–43. https://doi.org/10.18637/jss.v062.i01

Runa, F. (2020). The effect of weather on pedestrian activity at signalized intersections in Utah (master’s thesis). Logan, UT: Utah State University. https://doi.org/10.26076/cdbb-1171

Runa, F., & Singleton, P. A. (2021). Assessing the impacts of weather on pedestrian signal activity at 49 signalized intersections in Northern Utah. Transportation Research Record: Journal of the Transportation Research Board, 2675(6), 406–419. https://doi.org/10.1177/0361198121994111

Ryus, P., Ferguson, E., Laustsen, K. M., Proulx, F. R., Schneider, R. J., Hull, T., & Miranda-Moreno, L. (2014). Methods and technologies for pedestrian and bicycle volume data collection (NCHRP web-only document 205). Washington, DC: Transportation Research Board. Retrieved from https://doi.org/10.17226/23429

Ryus, P., Butsick, A., Proulx, F. R., Schneider, R. J., & Hull, T. (2017). Methods and technologies for pedestrian and bicycle volume data collection: Phase 2 (NCHRP Web-Only Document 205). Washington, DC: Transportation Research Board. Retrieved from https://doi.org/10.17226/24732

Schneider, R. J., Arnold, L. S., & Ragland, D. R. (2009). Methodology for counting pedestrians at intersections: Use of automated counters to extrapolate weekly volumes from short manual counts. Transportation Research Record: Journal of the Transportation Research Board, 2140(1), 1–12. https://doi.org/10.3141/2140-01

Singleton, P. A., Park, K., & Lee, D. H. (2021). Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data. Journal of Transport Geography, 93, 103067. https://doi.org/10.1016/j.jtrangeo.2021.103067

Singleton, P. A., & Runa, F. (2021). Pedestrian traffic signal data accurately estimates pedestrian crossing volumes. Transportation Research Record: Journal of the Transportation Research Board, 2675(6), 429–440. https://doi.org/10.1177/0361198121994126

Singleton, P. A., Runa, F., & Humagain, P. (2020). Utilizing archived traffic signal performance measures for pedestrian planning and analysis. Taylorsville, UT: Utah Department of Transportation. https://rosap.ntl.bts.gov/view/dot/54924

Singleton, P., Runa, F., & Humagain, P. (2021). Singletonpa/ped-signal-data [data set]. Zenodo. Retrieved from https://doi.org/10.5281/zenodo.4759088

Smaglik, E. J., Sharma, A., Bullock, D. M., Sturdevant, J. R., & Duncan, G. (2007). Event-based data collection for generating actuated controller performance measures. Transportation Research Record: Journal of the Transportation Research Board, 2035(1), 97–106. https://doi.org/10.3141/2035-11

Sturdevant, J. R., Overman, T., Raamot, E., Deer, R., Miller, D., Bullock, D. M., ... & Remias, S. M. (2012). Indiana traffic signal hi resolution data logger enumerations. West Lafayette, IN: Purdue University. http://doi.org/10.4231/K4RN35SH

Utah Department of Transportation (UDOT). 2020. Automated traffic signal performance measures, UDOT. Retrieved from https://udottraffic.utah.gov/ATSPM/