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Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data
Open accessAbstract
Accurately predicting pedestrian travel times is critically valuable in emergency response, wildland firefighting, disaster management, law enforcement, and urban planning. However, the relationship between pedestrian movement and landscape conditions is highly variable between individuals, making it difficult to estimate how long it will take broad populations to get from one location to another on foot. Although functions exist for predicting travel rates, they typically oversimplify the inherent variability of pedestrian travel by assuming the effects of landscapes on movement are universal. In this study, we present an approach for predicting the variability in pedestrian travel rates and times using a large, crowdsourced database of GPS tracks. Acquired from the outdoor recreation website AllTrails, these tracks represent nearly 2000 hikes on a diverse range of trails in Utah and California, USA. We model travel rates as a function of the slope of the terrain by generating a series of non-linear percentile models from the 2.5 th to the 97.5 th by 2.5 percentiles. The 50 th percentile model, representing the hiking speed of the typical individual, demonstrates marked improvement over existing slope-travel rate functions when compared to an independent test dataset. Our results demonstrate novel capacity to estimate travel time variability, with modeled percentiles being able to predict actual percentiles with less than 10% error. Travel rate functions can also be applied…
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Existing travel rate functions fail to sufficiently account for the vast diversity of landscape conditions that impede foot travel.
“Although a number of existing travel rate functions exist, they fail to sufficiently account for the vast diversity of landscape conditions that impede foot travel3–11.”
From A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data (2024) · cites this paper as [5]
The conditions of slope dominance and minimal vegetation-density and surface-roughness variability are met for on-path travel, where surface roughness is assumed uniform and vegetation density is negligible.
“and for “on-path” travel, where surface roughness is assumed to be uniform and vegetation density is negligible3,5,10.”
From A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data (2024) · cites this paper as [5]
A STRIDE model using no vegetation cover and median trail surface roughness was nearly identical to the 67.5th percentile travel rate function from a large, diverse, crowdsourced dataset representing on-path travel, with RMSE = 0.018 ms−2, bias = +0.005 ms−2, and R2 = 0.996.
“A STRIDE model using no vegetation cover and median trail surface roughness was nearly identical to the 67.5th percentile travel rate function from a study of a large, diverse, crowdsourced dataset representing on-path travel5 (RMSE = 0.018 ms−2; bias = + 0.005 ms−2; R2 = 0.996).”
From A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data (2024) · cites this paper as [5]
The function form of Eq. (1) is a suitable basis for modeling travel rates.
“Based on our previous work5,8,39 and preliminary testing, we identified the function form of Eq. (1) as a suitable basis for modeling.”
From A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data (2024) · cites this paper as [5]
Recent work [8,16] explored data-driven walking speed modelling, but crowdsourced data collection was not controlled, so the data could not be assumed to consist wholly of walking or hiking tracks.
“This has been explored in recent work [8, 16], however the crowdsourced nature of these studies meant that data collection was not controlled, and thus that the data could not be assumed to consist wholly of walking or hiking tracks.”
From Improved prediction of hiking speeds using a data driven approach (2023) · cites this paper as [8]
Campbell et al. attempted to overcome non-walking data in [8] by only considering data points with a speed between 0.2 m/s and 5 m/s.
“Campbell et al. attempted to overcome this in [8] by only considering data points with a speed between 0.2 m/s and 5 m/s (and the resulting model is shown in Fig 1).”
From Improved prediction of hiking speeds using a data driven approach (2023) · cites this paper as [8]
Both [8] and [16] looked solely at the effect of walking slope on speed and did not explore additional variables.
“Finally, although these studies had the benefit of using large sample sizes, they both looked solely at the effect of the walking slope on speed, and did not explore additional variables.”
From Improved prediction of hiking speeds using a data driven approach (2023) · cites this paper as [8]
The symmetrical function used in GET v2 is very similar to the original, asymmetrical function from Campbell et al. (2022), yielding an average difference (RMSE) of 0.038 m•s−1 between the two.
“The symmetrical function used in GET v2 is very similar to the original, asymmetrical function from Campbell et al. (2022) [36], yielding an average difference (RMSE) of 0.038 m•s -1 between the two (Figure 3).”
From Wildland firefighter estimated ground evacuation time modeling to support risk-informed decision-making (2024) · cites this paper as [36]
The relationship between slope and travel rates has been well studied, so a robust slope-travel rate equation was used as the basis for assigning slope-based costs.
“For example, the relationship between slope and travel rates has been well studied, so we were able to use a robust slope-travel rate equation to serve as the basis for assigning slope-based costs [36].”
From Wildland firefighter estimated ground evacuation time modeling to support risk-informed decision-making (2024) · cites this paper as [36]
The coefficients from non-linear least squares regression of Campbell et al. (2022) crowdsourced GPS-tracked hiking data were a=22.4056, b=77.6196, and c=0.0464.
“a was found to be equal to 22.4056, b was equal to 77.6196, and c was equal to 0.0464.”
From Wildland firefighter estimated ground evacuation time modeling to support risk-informed decision-making (2024) · cites this paper as [36]
GET v1 has not been updated or modified since its initial creation to account for changing landscape conditions and advancements in understanding of how landscape characteristics affect pedestrian movement.
“Despite its wide use, GET v1 has not been updated or modified since its initial creation to account for changing landscape conditions (e.g., hospital locations, road networks, and land cover) and advancements in our understanding of how landscape characteristics affect pedestrian movement [33,36,37].”
From Wildland firefighter estimated ground evacuation time modeling to support risk-informed decision-making (2024) · cites this paper as [36]
The slope-travel rate relationship was based on a scientifically robust study of pedestrian travel rates.
“Although our slope-travel rate relationship was based on a scientifically robust study of pedestrian travel rates [36]”
From Wildland firefighter estimated ground evacuation time modeling to support risk-informed decision-making (2024) · cites this paper as [36]
Slope had the highest variable importance, and the partial dependence pattern between slope and travel rates in this dataset closely resembles the asymmetrical Lorentz functions used to predict travel rate in previous studies.
“Slope had the highest variable importance and based on partial dependence the pattern between slope and travel rates in this dataset closely resembles the asymmetrical Lorentz functions used to predict travel rate in previous studies (Campbell et al., 2022;Sullivan et al. 2020) (Figure 8A).”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Campbell et al., 2022
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Metadata
- Authors
- Campbell, M. J., Dennison, P. E., & Thompson, M. P.
- Affiliations
- University of Utah (US)
- Corresponding author
- Campbell, Michael J.
- Venue
- Computers, Environment and Urban Systems
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 97
- Pages
- 101866
- ISSN
- 0198-9715 (ISSN-L)
- OpenAlex
- W4292397337
- Cited by
- 26
- References
- 58
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 6 · 2023: 5
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 323 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Computers Environment and Urban Systems (acceptedVersion)
- Funders
- USDA Forest Service (21-CS-11221636-120)
- National Science Foundation (BCS-2117433, -2117433)
- Topics
- Social Sciences › Safety Research › Evacuation and Crowd Dynamics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- walking time, terrain slope, least-cost path analysis, outdoor recreation, emergency response
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