Trail use by pedestrians has become more popular in the United States
over the last decade although fewstudies explore the use of technology to monitor
high use trails. Monitoringtrail
users is an important part of trail management and an optimal monitoring system
usually depends on site-specific characteristics. The objective of this study
was to demonstrate how using a multi-methods system to monitor backcountry
trail usage in complex spatial settings can be a useful approach for collecting
the information that trail managers need. Given the national growth in
recreational hiking, we were particularly interested in exploring these issues
for highly visited trails close to urban areas and selected a portion of the
Larch Trail leading to the top of Multnomah Falls for the study. The
multi-methods approach that we used included a combination of automated
infrared sensor counts, manual counts, parking lot data from an inductive loop,
and travel time estimates collected with low-energy Bluetooth sensors. We found
that using multiple methods allowed for a cost-effective and rich data set that
considered the site characteristics and the specific need of the trail
managers. We expect that many backcountry trail settings have complex landscape
and physical design without robust pre-existing baseline data and hope that our
insights will aid trail managers as they strive for a sustainable balance
between human use and landscape impact.
References
[1]
American Hiking Society (2015). Hiking Trails in America: Pathways to Prosperity. http://www.AmericanHiking.org
[2]
D’Antonio, A., Monz, C., Lawson, S., Newman, P., Pettebone, D., & Courtemanch, A. (2010). GPS Based Measurements of Backcountry Visitors in Parks and Protected Areas: Examples of Methods and Applications from Three Case Studies. Journal of Park and Recreation Administration, 28, 42-60.
[3]
English, D., & Bowker, J. M. (2018). Introduction to the Special Issue on Visitor Monitoring. Journal of Park and Recreation Administration, 36, IX-X.
[4]
Federal Highway Administration (FHWA) (2016). Traffic Monitoring Guide. FHWA-PL-17-003. https://www.fhwa.dot.gov/policyinformation/tmguide/
[5]
Fisher, D., Wood, S., White, E., Blahna, D., Lange, S., Weinberg, A., Tomco, M., & Lia, E. (2018). Recreational Use in Dispersed Public Lands Measured Using Social Media Data and On-Site Counts. Journal of Environmental Management, 222, 465-474. https://doi.org/10.1016/j.jenvman.2018.05.045
[6]
Groesbeck, R. (2019). Personal Interview. 28 February 2019.
[7]
Lorber, C., Dittrich, R., Jones, S., & Junge, A. (2021). Is Hiking Worth It? A Contingent Valuation Case Study of Multnomah Falls, Oregon. Forest Policy and Economics, 128, Article ID: 102471. https://doi.org/10.1016/j.forpol.2021.102471
[8]
Pettebone, D., Newman, P., & Lawson, S. (2010). Estimating Visitor Use at Attraction Sites and Trailheads in Yosemite National Park Using Automated Visitor Counters. Landscape and Urban Planning, 97, 229-238. https://doi.org/10.1016/j.landurbplan.2010.06.006
[9]
Quality Counts (2018). Bluemac Applications & Use Cases. Unpublished.
[10]
Shoji, Y., Yamaguchi, K., & Yamaki, K. (2008). Estimating Annual Visitors Flow in Daisetsuzan National Park, Japan: Combining Self-Registration Books and Infrared Trail Traffic Counters. Journal of Forest Research, 13, 286-295. https://doi.org/10.1007/s10310-008-0085-5
[11]
Statista Research Department (2022, September 23). Number of Hiking Participants in the United States from 2010 to 2021. https://www.statista.com/statistics/191240/participants-in-hiking-in-the-us-since-2006/
[12]
TRAFx (2018). TRAFx Infrared Trail Counter Instructions Part I and Part II. https://www.trafx.net/downloads/TRAFx_Infrared_Trail_Counter_Instructions.pdf?v=190129
[13]
USDA Forest Service (2014). Columbia River Gorge National Scenic Area Interagency Recreation Strategy Team. Recreation Report and Recommended Interim Strategies. https://www.fs.usda.gov/recarea/crgnsa/recreation/natureviewing/recarea/?recid=30026
[14]
Wang, Y., Malinovskiy, Y., Wu, Y., & Lee, U. (2011). Error Modeling and Analysis for Travel Time Data Obtained from Bluetooth MAC Address Matching. Research Report T4118 Task 46.
[15]
White, E. (2018). Visitor Use Report: Columbia River Gorge National Scenic Area. USDA Forest Service.
[16]
White, E., & Hinatsu, S. (2019, April 19). Personal Interview.
[17]
Yang, H., Ozbay, K., & Bartin, B. (2010). Investigating the Performance of Automatic Counting Sensors for Pedestrian Traffic Data Collection. In 12th World Conference on Transport Research. https://www.semanticscholar.org/paper/Investigating-the-Performance-of-Automatic-Counting-Yang-Ozbay/1025a2191f70f1a6da950c027fb122ffcfd34517