Analyses of Bicycle and Pedestrian Trail Traffic: New Tools for Modeling User Expenditures and Demand

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Analyses of Bicycle and Pedestrian Trail Traffic: New Tools for Modeling User Expenditures and Demand

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Despite the importance of multi-use trails in urban non-motorized transportation networks, transportation planners, engineers, and trail managers lack tools for describing economic activity associated with local trail use and for predicting bicycle and pedestrian demand for trails. New tools are needed to plan and prioritize investments in new facilities and to inform management and maintenance of trail infrastructure. Among other needs, they need tools to predict (1) expenditures by local users to support local economic development initiatives and assess neighborhood effects of proposals for trail development and (2) trail traffic demand for optimizing investments and managing maintenance of systems and facilities. This thesis responds to these needs and augments the burgeoning literature on trail traffic analysis by developing models of trail-related expenditures and mode-specific trail demand models. From the expenditures by local users side, using the results of intercept surveys completed by 1,282 trail users on the Central Ohio Greenway trail network in 2014, this thesis estimates the probabilities and patterns that different types of trail users will make expenditures. Approximately one-fifth of trail users reported spending between US$15 and US$20 for food, drink, and other incidental items. Across all trail users the average expenditure by individuals is about US$3 per visit. All else equal, cyclists are more than twice as likely than other users to report expenditures. Users visiting trails principally for recreation are 53% more likely to spend, while users visiting trails mainly for exercise were about 19% less likely. Both longer trips to and on the trails are associated with higher spending. From the trail traffic demand side, this thesis employs trail traffic volumes recorded at 15-minute intervals for 32 multi-use trails located in 13 urban areas across the United States from January 1, 2014 through February 16, 2016. The results of analyses indicate (1) daily trail traffic varies substantially – over three orders of magnitude – across the monitoring stations included in the study; (2) daily trail traffic is highly correlated with weather, and the parabola form of weather parameters works well for modeling variables such as temperature, where trail use is associated with warmer temperatures, but only up to a point at which higher temperatures then decrease use; (3) bicyclists and pedestrians respond differently to variations in weather, and their responses vary both within and across regions; (4) with only a few exceptions, average daily pedestrians (ADP) and average daily bicyclists (ADB) are correlated with different variables, and the magnitude of effects of variables that are the same varies significantly between the two modes; (5) the mean relative percentage error (MRPE) for bicyclist, pedestrian, and mixed-mode demand models, respectively, are 65.4%, 85.3%, and 45.9%; (6) although using multimodal monitoring networks enables us to juxtapose the bicyclist demand with pedestrian demand, there is not a significant improvement in predicting total demand using multimodal sensors; (7) a new post-validation procedure improves the demand models, reducing the MRPE of bicyclist, pedestrian, and mixed-mode models by 27.2%, 32.1%, and 14.1%. Transportation planners, engineers, and trail managers can use these results to estimate the effects of weather and climate on trail traffic and to plan and manage facilities more effectively. The developed models also can be used in practical applications such as selection of route corridors and prioritization of investments where order-of-magnitude estimates suffice.


University of Minnesota MURP thesis. August 2017. Major: Urban and Regional Planning. Advisor: Greg Lindsey. 1 computer file (PDF); x, 93 pages.

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Ermagun, Alireza. (2017). Analyses of Bicycle and Pedestrian Trail Traffic: New Tools for Modeling User Expenditures and Demand. Retrieved from the University Digital Conservancy,

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