The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index; 8 of the 52 sources its claims cite were checked for support.
A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data
Open accessAbstract
Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., "on-path" vs. "off-path"). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The authors introduce STRIDE, a single nonlinear model that predicts on- and off-path walking travel rates from airborne lidar-derived slope, vegetation density, and surface roughness. Across three field experiments, STRIDE explained more than 80% of variance in mean travel rates and produced more realistic least-cost paths than a slope-only model.
Key findings
- STRIDE’s best-performing lidar metrics used a 2 m surface roughness focal radius and a 0.85–1.20 m vegetation density height range, yielding a cross-validated R² of 0.806 and RMSE of 0.168 ms⁻¹ (16% of the mean measured travel rate).
- A final model using all data had a residual standard error of 0.159 ms⁻¹; the authors note ~19% of variance remained unexplained in cross-validation, possibly due to participant fitness, weather, and altitude differences.
- Experimental transects captured the 99th percentile of sampled U.S. natural-landscape slope, 92nd percentile of vegetation density, and 73rd percentile of surface roughness, suggesting broad applicability with little extrapolation.
- In least-cost path modeling, STRIDE-based travel times averaged 1.5 times greater than slope-only estimates, and STRIDE favored roads, trails, and meadows more consistently than slope-only routes.
Methods
- Three field experiments timed subjects walking ~100 m transects twice, once in each direction: up to 31 subjects on 22 juniper-sagebrush transects near Levan, Utah (September 2016); up to nine subjects on 11 varied Wasatch Mountain transects (August 2023); and eight subjects on eight paved urban Salt Lake City transects (January 2024).
- Lidar-derived predictors were slope, vegetation density via normalized relative density of lidar returns, and surface roughness from 1 m DTMs; four USGS 3D Elevation Program lidar datasets were used.
- Travel rates were modeled with nonlinear least squares regression using leave-one-transect-out cross-validation; broad applicability was assessed against 1 million random points in 100 natural U.S. areas, and least-cost paths were demonstrated in a 6 × 6 km Alta, Utah area and a 4 × 4 km Salt Lake City area.
Limitations
- The paper states that airborne lidar is not globally available; while slope can come from global DEMs, surface roughness and vegetation density require lidar, and future work should test whether these can be modeled from land cover, topography, soils, and geology.
- The paper states that 100 m transects may not induce fatigue effects seen on longer hikes, so STRIDE subjects moved slightly faster than average crowdsourced hikers; using STRIDE for longer-distance travel time estimation would require a scaling factor.
- The paper states future refinement could consider fatigue over longer distances, load carriage, additional vegetation types and surface hazards, and surface moisture; the text also makes evident that validation is transect-level and limited to the specific study areas and vegetation conditions tested.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
8 of 52 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Past work has accommodated off-path environments by lumping the vast diversity of landscape characteristics into a single multiplier or a small number of fixed multipliers specific to broad land cover categories.
“Past work has accommodated off-path environments by lumping the vast diversity of landscape characteristics into a single multiplier3,6 or a small number of fixed multipliers specific to broad land cover categories7,22.”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [3] Tobler, W. Three Presentations on Geographical Analysis and Modeling. 24 (1993).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [6] Irmischer, I. J. & Clarke, K. C. Measuring and modeling the speed of human navigation. Cartogr. Geogr. Inf. Sci. 45, 177–186 (2018). source
- cites [7] Wood, A., Mackaness, W., Simpson, T. I. & Armstrong, J. D. Improved prediction of hiking speeds using a data driven approach. PLOS ONE 18, e0295848 (2023). source
The Materials and methods sections describe a small number of terrain categories—paved road, unpaved road and off-road, plus light and heavy obstruction—with factor variables in the walking-speed model. That supports the general point about simplifying varied terrain, but the categories are not broad land-cover categories, and road type also interacts with slope rather than being represented solely by fixed multipliers. The Introduction separately describes single off-road multipliers in earlier models. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [22] Richmond, P. W., Potter, A. W. & Santee, W. R. Terrain factors for predicting walking and load carriage energy costs: Review and refinement. J. Sport Hum. Perform. 3, (2015).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
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.”
Quote in the paperSubstantial claim3 of 9 sources checked- cites [3] Tobler, W. Three Presentations on Geographical Analysis and Modeling. 24 (1993).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [4] Naismith, W. Cruach Adran, Stobinian, and Ben More. Scottish Mountain. Club J. 2, 136 (1892).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [5] Campbell, M. J., Dennison, P. E. & Thompson, M. P. Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data. Comput. Environ. Urban Syst. 97, 101866 (2022). source
- cites [6] Irmischer, I. J. & Clarke, K. C. Measuring and modeling the speed of human navigation. Cartogr. Geogr. Inf. Sci. 45, 177–186 (2018). source
The Tobler’s hiking function section (p. not available) says existing models use slope and a path-versus-non-path distinction as a starting point. The discussion (AQ8; p. not available) finds that Tobler’s and Naismith’s functions misestimate navigation times in the study setting. This supports a limitation of those functions, but the paper does not establish that they fail to account for the vast diversity of landscape conditions that impede foot travel. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [7] Wood, A., Mackaness, W., Simpson, T. I. & Armstrong, J. D. Improved prediction of hiking speeds using a data driven approach. PLOS ONE 18, e0295848 (2023). source
- cites [8] Sullivan, P. R., Campbell, M. J., Dennison, P. E., Brewer, S. C. & Butler, B. W. Modeling wildland firefighter travel rates by terrain slope: Results from GPS-tracking of type 1 crew movement. Fire 3, 52 (2020). source
The Introduction says existing pedestrian travel-rate functions focus particularly on slope and that better functions for complex terrain are needed. It identifies specific shortcomings for firefighters—load carriage, physical characteristics, and emergency movement—but does not establish the broader claim that existing functions fail to account for the vast diversity of landscape conditions that impede foot travel. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [9] Prisner, E. & Sui, P. Hiking-time formulas: A review. Cartogr. Geogr. Inf. Sci. 50, 421–432 (2023). source
- cites [10] Rees, W. G. Least-cost paths in mountainous terrain. Comput. Geosci. 30, 203–209 (2004). source
- cites [11] Pandolf, K. B., Givoni, B. & Goldman, R. F. Predicting energy expenditure with loads while standing or walking very slowly. J. Appl. Physiol. Respir. Environ. Exerc. Physiol. 43, 577–581 (1977). source
- Findingown data
A cross-validated comparison between observed and STRIDE-predicted travel rates revealed a highly linear correlation.
“A cross-validated comparison between the observed and STRIDE-predicted travel rates revealed a highly linear correlation (Fig. 3).”
Quote in the paperSubstantial claim - Findingown data
A final STRIDE model built using all the data yielded a residual standard error of 0.159 ms−1.
“A final model built using all the data (rather than withholding one transect for cross-validation) yielded a residual standard error of 0.159 ms−1.”
Quote in the paperNot a substantial claim - Findingown data
The STRIDE model best explained travel rates using a surface roughness focal radius of 2 m and a vegetation density height range of 0.85-1.20 m.
“The STRIDE model best explained travel rates using a surface roughness focal radius of 2 m and a vegetation density height range of 0.85–1.20 m (Fig. 2), producing a cross-validated R2 of 0.806 and an RMSE of 0.168 ms−1 (16% of the mean travel rate measured in our experiments).”
Quote in the paperSubstantial claim
- Findingown data
Despite the diversity of inputs, a single predictive model accounted for more than 80% of the variance in group mean travel rates.
“Despite the diversity of inputs, a single predictive model was able to account for more than 80% of the variance in group mean travel rates.”
Quote in the paperSubstantial claim - Findingcitation
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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Campbell, M. J., Dennison, P. E. & Thompson, M. P. Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data. Comput. Environ. Urban Syst. 97, 101866 (2022). source
- Findingown data
Even without the explicit addition of a transportation dataset, the STRIDE model clearly favored preexisting, easy-to-traverse paths, even when applied to a geographically complex urban environment.
“Even without the explicit addition of a transportation dataset, the STRIDE model clearly favored preexisting, easy-to-traverse paths, even when applied to a geographically complex urban environment (Supplementary Fig. S1).”
Quote in the paperSubstantial claim - Findingown data
On average, STRIDE-based travel times were 1.5 times greater than those computed from a slope-travel rate predictive model.
“On average, the STRIDE-based travel times were 1.5 times greater than those computed from a slope-travel rate predictive model, highlighting the importance of incorporating a more holistic accounting of landscape impediments into off-path travel time estimation.”
Quote in the paperSubstantial claim - Findingown data
Routes generated from least-cost path models optimizing slope-only and the STRIDE model combining slope, vegetation density, and surface roughness differed substantially.
“The routes generated from least-cost path models aimed at optimizing for slope-only—by far the most common approach for such models—and the STRIDE model combining slope, vegetation density, and surface roughness differed substantially (Fig. 5).”
Quote in the paperSubstantial claim- Rests on an assumption: Slope-only optimization is by far the most common approach for least-cost path models.
- Findingown data
The average roughness among all 20 hiking trails was 0.03 m.
“The average among all 20 trails, found to be 0.03 m, was used as the roughness value in STRIDE”
Quote in the paperSubstantial claim - Findingown data
Transect mean travel rates could be approximated to within ± 5% using just five subjects.
“A bootstrapping analysis of travel rates revealed that transect mean travel rates could be approximated to within ± 5% using just five subjects.”
Quote in the paperSubstantial claim - Interpretationnone given
The relative effects of landscape conditions on travel rates should be scalable: steeper slopes, denser vegetation, and rougher ground surfaces should impede pedestrian travel similarly on short (e.g., 100 m) and longer-distance (e.g., 1 km+) walks.
“However, we expect that the relative effects of landscape conditions on travel rates should be scalable—that is, steeper slopes, denser vegetation, and rougher ground surfaces should impede pedestrian travel similarly on short (e.g., 100 m) and longer-distance (e.g., 1 km +) walks.”
Quote in the paperNot a substantial claim - Interpretationreasoning
One limitation to broad applicability of STRIDE is the lack of globally available airborne lidar data.
“One limitation to broad applicability of STRIDE is the lack of globally available airborne lidar data.”
Quote in the paperSubstantial claim- Rests on an assumption: Ground surface roughness and vegetation density require lidar data.
- Premisereasoning
Slope is only one of multiple landscape characteristics that may impact travel rates.
“Although it plays a critical role in dictating pedestrian movement, slope is only one of multiple landscape characteristics that may impact travel rates.”
Quote in the paperSubstantial claim - Interpretationreasoning
STRIDE better approximates how pedestrians might identify their own optimal path on the ground in the absence of geospatial support, with a preference toward utilizing roads and trails to optimize travel between points on the landscape.
“STRIDE better approximates how pedestrians might identify their own optimal path on the ground in the absence of geospatial support, with a preference toward utilizing roads and trails to optimize travel between points on the landscape.”
Quote in the paperSubstantial claim - Interpretationown data
STRIDE is a singular model that can predict walking travel rates and times along a full spectrum of landscape development, from urban to wild.
“STRIDE is a singular model that can predict walking travel rates and times along a full spectrum of landscape development, from urban to wild.”
Quote in the paperSubstantial claim - Premisecitation
The presence, abundance, structure, and distribution of vegetation have major limiting effects on the ease with which one can move through a landscape.
“The presence, abundance, structure, and distribution of vegetation also have major limiting effects on the ease with which one can move through a landscape20–22.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [20] Anguelova, Z., Stow, D. A., Kaiser, J., Dennison, P. E. & Cova, T. Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the U.S.–Mexico Border Zone. Prof. Geogr. 62, 230–247 (2010). source
- cites [21] Campbell, M. J., Dennison, P. E. & Butler, B. W. A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping. Int. J. Wildland Fire 26, 884–895 (2017). source
- cites [22] Richmond, P. W., Potter, A. W. & Santee, W. R. Terrain factors for predicting walking and load carriage energy costs: Review and refinement. J. Sport Hum. Perform. 3, (2015).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Walking travel rates and times can be reliably predicted in situations in which slope is the dominant factor and variability in vegetation density and surface roughness is minimal.
“Based on past research, walking travel rates and times can be reliably predicted in situations in which slope is the dominant factor and variability in vegetation density and surface roughness is minimal.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [23] Aghabayk, K., Parishad, N. & Shiwakoti, N. Investigation on the impact of walkways slope and pedestrians physical characteristics on pedestrians normal walking and jogging speeds. Safety Sci. 133, 105012 (2021). source
- cites [24] Meeder, M., Aebi, T. & Weidmann, U. The influence of slope on walking activity and the pedestrian modal share. Transp. Res. Proc. 27, 141–147 (2017).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [25] Sun, J., Walters, M., Svensson, N. & Lloyd, D. The influence of surface slope on human gait characteristics: A study of urban pedestrians walking on an inclined surface. Ergonomics 39, 677–692 (1996). source
- Premisecitation
The roughness of the ground surface itself can act as an additional impediment to travel.
“Likewise, the roughness of the ground surface itself can act as an additional impediment to travel22.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Richmond, P. W., Potter, A. W. & Santee, W. R. Terrain factors for predicting walking and load carriage energy costs: Review and refinement. J. Sport Hum. Perform. 3, (2015).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
The STRIDE model should be broadly applicable, with little need for extrapolation.
“Thus, the STRIDE model should be broadly applicable, with little need for extrapolation.”
Quote in the paperSubstantial claim - Interpretationown data
The STRIDE model capably integrated travel rates from both off-path and on-path conditions.
“The fact that the model capably integrated travel rates from both off-path and on-path conditions is particularly noteworthy.”
Quote in the paperSubstantial claim - Premisecitation
The conditions of slope dominance and minimal vegetation-density and surface-roughness variability are met in urban and other developed settings where movement is expected on roads or other well-maintained hard surfaces.
“These conditions are met in urban and other developed settings, where movement is expected to take place on roads or other well-maintained, hard surfaces23–25”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [23] Aghabayk, K., Parishad, N. & Shiwakoti, N. Investigation on the impact of walkways slope and pedestrians physical characteristics on pedestrians normal walking and jogging speeds. Safety Sci. 133, 105012 (2021). source
- cites [24] Meeder, M., Aebi, T. & Weidmann, U. The influence of slope on walking activity and the pedestrian modal share. Transp. Res. Proc. 27, 141–147 (2017).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [25] Sun, J., Walters, M., Svensson, N. & Lloyd, D. The influence of surface slope on human gait characteristics: A study of urban pedestrians walking on an inclined surface. Ergonomics 39, 677–692 (1996). source
- Interpretationown data
Travel time estimates reveal an important degree of variability that would be left unaccounted for if fixed multiplicative terms were used to universally estimate off-path, slope-based travel times.
“Furthermore, our travel time estimates reveal an important degree of variability that would be left unaccounted for if fixed multiplicative terms were used to universally estimate off-path, slope-based travel times3,6,7.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [3] Tobler, W. Three Presentations on Geographical Analysis and Modeling. 24 (1993).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [6] Irmischer, I. J. & Clarke, K. C. Measuring and modeling the speed of human navigation. Cartogr. Geogr. Inf. Sci. 45, 177–186 (2018). source
A check is scheduled.
- cites [7] Wood, A., Mackaness, W., Simpson, T. I. & Armstrong, J. D. Improved prediction of hiking speeds using a data driven approach. PLOS ONE 18, e0295848 (2023). source
A check is scheduled.
- Premisecitation
Airborne lidar can characterize the structure of vegetation within the aboveground height ranges that are most relevant to human movement.
“and even in the presence of tall vegetation, airborne lidar can characterize the structure of vegetation within the aboveground height ranges that are most relevant to human movement26–29.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [26] Campbell, M. J., Dennison, P. E., Hudak, A. T., Parham, L. M. & Butler, B. W. Quantifying understory vegetation density using small-footprint airborne lidar. Remote Sens. Environ. 215, 330–342 (2018). source
- cites [27] Crespo-Peremarch, P., Tompalski, P., Coops, N. C. & Ruiz, L. Á. Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sens. Environ. 217, 400–413 (2018). source
Checked before — its earlier verdict is held back until a new check runs.
- cites [28] Hamraz, H., Contreras, M. A. & Zhang, J. Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds. Sci. Rep. 7, 6770 (2017). source
Checked before — its earlier verdict is held back until a new check runs.
- cites [29] Wing, B. M. et al. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sens. Environ. 124, 730–741 (2012). source
- Interpretationreasoning
In between developed and wildland extremes, such as on hiking trails, slope and ground surface roughness are the primary factors reducing travel rates.
“In between those extremes, such as on hiking trails, slope and ground surface roughness will act as the primary factors reducing travel rates.”
Quote in the paperSubstantial claim - Interpretationnone given
In developed environments, travel rates and times are primarily dictated by the slope of paved paths.
“In developed environments, travel rates and times are primarily dictated by the slope of paved paths.”
Quote in the paperSubstantial claim - Premisecitation
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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5] Campbell, M. J., Dennison, P. E. & Thompson, M. P. Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data. Comput. Environ. Urban Syst. 97, 101866 (2022). source
- cites [8] Sullivan, P. R., Campbell, M. J., Dennison, P. E., Brewer, S. C. & Butler, B. W. Modeling wildland firefighter travel rates by terrain slope: Results from GPS-tracking of type 1 crew movement. Fire 3, 52 (2020). source
A check is scheduled.
- cites [39] Campbell, M. J., Dennison, P. E., Butler, B. W. & Page, W. G. Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Appl. Geogr. 106, 93–107 (2019). source
- Premisecitation
The normalized relative density (NRD) of lidar point returns is a proxy for vegetation density.
“The normalized relative density (NRD) of lidar point returns was used as a proxy for vegetation density26.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Campbell, M. J., Dennison, P. E., Hudak, A. T., Parham, L. M. & Butler, B. W. Quantifying understory vegetation density using small-footprint airborne lidar. Remote Sens. Environ. 215, 330–342 (2018). source
- Interpretationown data
STRIDE demonstrated an impressive ability to predict travel rates across a wide range of slopes, surface roughness values, and vegetation densities.
“With more than 80% of the variance in group-level mean travel rates accounted for across three different study areas and 16% travel rate predictive error, STRIDE demonstrated an impressive ability to predict travel rates across a wide range of slopes, surface roughness values, and vegetation densities.”
Quote in the paperSubstantial claim
- Probably not a substantial claim. 11
- It may state two separate claims as one. 18
Probably not a substantial claim. The paper’s conclusions may not depend on it.
InterpretationreasoningThe residual error and approximately 19% of variance left unexplained in cross-validation may be attributable to factors including different fitness levels among study participants, different weather conditions during the experiments, and different altitudes of the study transects.
“This residual error and the ~ 19% of variance left unexplained in the cross-validation may be attributable to variety of factors, including different fitness levels in the three groups of study participants30, different weather conditions experienced during the three experiments (i.e., August vs. September vs. January)31, and different altitudes of the study transects32.”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites [30] Markov, A., Hauser, L. & Chaabene, H. Effects of concurrent strength and endurance training on measures of physical fitness in healthy middle-aged and older adults: A systematic review with meta-analysis. Sports Med. 53, 437–455 (2023). source
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [31] Obuchi, S. P., Kawai, H., Garbalosa, J. C., Nishida, K. & Murakawa, K. Walking is regulated by environmental temperature. Sci. Rep. 11, 12136 (2021). source
- cites [32] Horiuchi, M., Handa, Y., Abe, D. & Fukuoka, Y. Walking economy at simulated high altitude in human healthy young male lowlanders. Biol. Open 5, 1408–1414 (2016). source
The Results report that severe simulated hypoxia increased energy expenditure at faster walking speeds relative to normoxia and moderate hypoxia, supporting the possibility that altitude-related conditions affect walking measurements. But the study tested young men walking on a level gradient under simulated hypoxia; it did not examine study transects or whether their differing altitudes explain cross-validation error or the approximately 19% unexplained variance. The fitness and weather factors have separate citations in the citing sentence. (LLM, conf=0.82)
Experimental — accuracy not yet measured
It may state two separate claims as one.
Findingown dataAlthough the 0.85-1.20 m range performed best in the study area, a cluster of comparable height ranges from 0.45 to 1.70 m had predictive error less than 2% greater than that of the best model.
“Although the 0.85–1.20 m range was found to perform best in our study area, there was a cluster of comparable height ranges from 0.45 to 1.70 m (approximately knee to top of head for an average height person) whose predictive error was less than 2% greater than that of the best model (Supplementary Table S1).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Supplementary Table S1
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
Findingown dataThe best STRIDE model produced a cross-validated R2 of 0.806 and an RMSE of 0.168 ms−1, which was 16% of the mean travel rate measured in the experiments.
“The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%.”
- Also stated in: § Vegetation density and surface roughness geometry · plain
“The STRIDE model best explained travel rates using a surface roughness focal radius of 2 m and a vegetation density height range of 0.85–1.20 m (Fig. 2), producing a cross-validated R2 of 0.806 and an RMSE of 0.168 ms−1 (16% of the mean travel rate measured in our experiments).”
Quote in the paperSubstantial claim- Also stated in: § Vegetation density and surface roughness geometry · plain
It may state two separate claims as one.
Findingown dataComparing the range of landscape conditions tested in the experiments to a random sample of natural landscapes in the U.S. revealed that the transects captured the 99th percentile of sample slope, the 92nd percentile of sample vegetation density, and the 73rd percentile of sample surface roughness.
“Furthermore, comparing the range of landscape conditions tested in our experiments to a random sample of natural landscapes in the U.S. revealed that our transects captured the 99th percentile of the sample slope, 92nd percentile of the sample vegetation density, and 73rd percentile of the sample surface roughness (Fig. 4).”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataSlope-only least-cost paths tended to produce direct routes that ignore local landscape characteristics, whereas STRIDE generated more sinuous routes that appear to more consistently favor roadways, trails, and meadows with lower vegetation density and surface roughness.
“The former tended to produce direct routes that ignore local landscape characteristics, whereas the latter generated more sinuous routes that appear to more consistently favor roadways, trails, and meadows with lower vegetation density and surface roughness.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataSteeper slopes, denser vegetation, and rougher ground surfaces contribute to lower travel rates.
“The relationships between travel rates and the three landscape metrics of interest are clear, with steeper slopes, denser vegetation, and rougher ground surfaces contributing to lower travel rates.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataSTRIDE accounted for more than 80% of the variance in group-level mean travel rates across three study areas, with 16% travel rate predictive error.
“With more than 80% of the variance in group-level mean travel rates accounted for across three different study areas and 16% travel rate predictive error, STRIDE demonstrated an impressive ability to predict travel rates across a wide range of slopes, surface roughness values, and vegetation densities.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
PremisecitationThe 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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [3] Tobler, W. Three Presentations on Geographical Analysis and Modeling. 24 (1993).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [5] Campbell, M. J., Dennison, P. E. & Thompson, M. P. Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data. Comput. Environ. Urban Syst. 97, 101866 (2022). source
- cites [10] Rees, W. G. Least-cost paths in mountainous terrain. Comput. Geosci. 30, 203–209 (2004). source
Probably not a substantial claim. The paper may give no reason why it holds.
Premisenone givenDiscrete-return airborne lidar is unique in its ability to map three-dimensional structure over space based on point clouds representing x, y, and z coordinates of reflective surfaces.
“Discrete-return airborne lidar is unique in its ability to map three-dimensional structure over space based on point clouds representing the x, y, and z coordinates of reflective surfaces (e.g., ground and vegetation).”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Interpretationown dataThese results suggest that STRIDE can accurately capture the on-path relationships between slope and travel rate, with the caveat that subjects walking 100 m transects moved slightly faster than the average hiker in the crowdsourced dataset.
“These results suggest that STRIDE can accurately capture the on-path relationships between slope and travel rate, with the caveat that the subjects walking 100 m transects moved slightly faster than the average hiker in the crowdsourced dataset.”
Quote in the paperSubstantial claimProbably not a substantial claim.
PremisecitationTaller shrubs and trees can impact movement in ways ranging from avoidance to impedance.
“Taller shrubs and trees can impact movement in a variety of ways, ranging from avoidance (i.e., moving around obstacles deemed impassible) to impedance (i.e., moving through passible vegetation but being physically restrained by it).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [20] Anguelova, Z., Stow, D. A., Kaiser, J., Dennison, P. E. & Cova, T. Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the U.S.–Mexico Border Zone. Prof. Geogr. 62, 230–247 (2010). source
- cites [21] Campbell, M. J., Dennison, P. E. & Butler, B. W. A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping. Int. J. Wildland Fire 26, 884–895 (2017). source
- cites [22] Richmond, P. W., Potter, A. W. & Santee, W. R. Terrain factors for predicting walking and load carriage energy costs: Review and refinement. J. Sport Hum. Perform. 3, (2015).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
PremisecitationShorter vegetation at or near the ground surface can affect foot placement and alter gait.
“Shorter vegetation at or near the ground surface (i.e., grasses, forbs, shrubs) can affect foot placement and alter gait.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [20] Anguelova, Z., Stow, D. A., Kaiser, J., Dennison, P. E. & Cova, T. Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the U.S.–Mexico Border Zone. Prof. Geogr. 62, 230–247 (2010). source
- cites [21] Campbell, M. J., Dennison, P. E. & Butler, B. W. A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping. Int. J. Wildland Fire 26, 884–895 (2017). source
- cites [22] Richmond, P. W., Potter, A. W. & Santee, W. R. Terrain factors for predicting walking and load carriage energy costs: Review and refinement. J. Sport Hum. Perform. 3, (2015).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningThe faster speeds on 100 m transects are attributable to the fact that 100 m transects do not induce large fatigue effects, while longer hikes produce slightly slower travel rates due to fatigue.
“We attribute this to the fact that 100 m transects do not induce large fatigue effects, while longer hikes produce slightly slower travel rates due to fatigue.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Interpretationnone givenFor an individual with an average height of 1.70 m, the 0.85-1.20 m vegetation height range approximately represents waist-to-chest height.
“For an individual with an average height of 1.70 m, this vegetation height range approximately represents waist-to-chest height.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Interpretationown dataSTRIDE enables accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings.
“STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings.”
- Also stated in: § Broad applicability of STRIDE · boosted
“STRIDE demonstrated an impressive ability to predict on- and off-path walking travel rates, especially when considering the diversity of the data used to construct it.”
Quote in the paperSubstantial claim- Also stated in: § Broad applicability of STRIDE · boosted
It may state two separate claims as one.
PremisecitationIn the US, there is widespread, freely available lidar data thanks to the USGS 3D Elevation Program; in many other parts of the globe these data are much more limited.
“In the US, thanks to the USGS 3D Elevation Program33, there is widespread, freely available lidar data; however, in many other parts of the globe these data are much more limited.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [33] Snyder, G. I. The 3D Elevation Program: Summary of Program Direction. http://pubs.er.usgs.gov/publication/fs20123089 (2012). source
Checked before — its earlier verdict is held back until a new check runs.
It may state two separate claims as one.
InterpretationreasoningVegetation in the 0.85-1.20 m range was too high to step or climb over and too low to duck under when walking along vegetated transects.
“The vegetation in this range was too high to step or climb over and too low to duck under when walking along vegetated transects.”
Quote in the paperSubstantial claimProbably not a substantial claim. It may be too vague to check against evidence.
PremisereasoningThere remains a need to develop a singular travel rate predictive equation that can encompass both on- and off-path travel and that provides for continuous estimation of off-path travel rates.
“There remains a need to develop a singular travel rate predictive equation that can encompass both on- and off-path travel and that provides for continuous estimation of off-path travel rates.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Study design and conductnone givenThe noise classification algorithms used for the lidar data acquired from the USGS for this study are unknown.
“There are many noise classification algorithms, and we do not know which among them were used for the lidar data we acquired from the USGS for this study”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Interpretationnone givenIn wildland environments, slope, ground surface roughness, and vegetation density all contribute to travel impedance.
“In wildland environments, slope, ground surface roughness, and vegetation density all contribute to travel impedance.”
Quote in the paperSubstantial claimProbably not a substantial claim. It may state two separate claims as one.
Study design and conductown dataLeave-one-transect-out cross-validation was used to assess model performance, with R2, RMSE, and bias as metrics.
“To assess model performance, we used a leave-one-transect-out cross-validation approach and relied on the coefficient of determination (R2), root mean squared error (RMSE), and bias between observed and predicted travel rates as performance evaluation metrics.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Study design and conductnone givenThe University of Utah's Institutional Review Board approved all study protocols prior to the experiments, and informed consent was obtained from all participants.
“University of Utah’s Institutional Review Board approved all the study protocols prior to engaging in the experiments, which were performed in accordance with the Board’s guidelines and regulations, including the prior obtainment of informed consent by all study participants.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Interpretationown dataSTRIDE should have broad applicability for estimating walking travel rates used for modeling least-cost path travel across a wide range of disciplines, offering essential improvements over previous models.
“As is, STRIDE should have broad applicability for estimating walking travel rates used for modeling least-cost path travel across a wide range of disciplines, offering essential improvements over previous models.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationRemoval of noise is considered standard practice in lidar data analysis for improving the data.
“removal of noise is considered standard practice in lidar data analysis for improving the data35”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Ullrich, A. & Pfennigbauer, M. Noisy lidar point clouds: Impact on information extraction in high-precision lidar surveying. In Laser Radar Technology and Applications XXIII vol. 10636, pp. 133–138 (SPIE, 2018). source
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationLeast-cost path modeling is among the most common applications of travel rate functions.
“Least-cost path modeling is among the most common applications of travel rate functions41,42, enabling the identification of routes that minimize travel time between two (or more) points on a landscape.”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim. It may state two separate claims as one.
Study design and conductnone givenThe transects in each experiment were optimized to capture the diversity of landscape conditions within each study site while also possessing relative uniformity in slope, vegetation type, and vegetation density along each transect.
“The transects in each experiment were optimized to capture the diversity of landscape conditions within each study site while also possessing relative uniformity in slope, vegetation type, and vegetation density along each transect.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Ground surface roughness and vegetation density require lidar data.
“There are several sources of global or near-global digital elevation models that could be used for slope mapping, but ground surface roughness and vegetation density require lidar.”
The paper relies on this without support.
Slope-only optimization is by far the most common approach for least-cost path models.
“The routes generated from least-cost path models aimed at optimizing for slope-only—by far the most common approach for such models—and the STRIDE model combining slope, vegetation density, and surface roughness differed substantially (Fig. 5).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Campbell, Cutler, and Dennison (2024) found that vegetation at a height of between 0.85 and 1.20 metres impacted travel rate the most.
“Additionally, they found that vegetation at a height of between 0.85 and 1.20 metres impacted travel rate the most.”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Campbell, Cutler, and Dennison 2024
Off-path pedestrian travel rates are affected by multiple landscape factors, including slope, vegetation density, and surface roughness, which work together.
“Multiple landscape factors, including slope, vegetation density, and surface roughness, work together to affect pedestrian travel rates in off-path environments.”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Campbell, Cutler, and Dennison 2024
Campbell, Cutler, and Dennison (2024) found that LiDAR-based measurements of slope, vegetation density, and surface roughness could explain the variation in travel rate across diverse landscapes.
“Campbell, Cutler, and Dennison (2024) used LiDAR-based measurements of slope, vegetation density, and surface roughness to model net travel rate in both on-and off-path environments.They found that these three variables could explain the variation in travel rate across diverse landscapes.”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Campbell, Cutler, and Dennison 2024
Vegetation density partial dependence confirms a non-linear relationship between vegetation density and travel rate previously posited by Campbell, Cutler, and Dennison (2024).
“Vegetation density partial dependence in Figure 9B confirms a non-linear relationship between vegetation density and travel rate previously posited by Campbell, Cutler, and Dennison (2024).”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Campbell, Cutler, and Dennison (2024)
Pedestrian movement is highly sensitive to local landscape conditions, and assuming uniform effects across individuals or terrain can lead to prediction errors.
“Recent work highlights that pedestrian movement is highly sensitive to local landscape conditions, and that assuming uniform effects across individuals or terrain can lead to prediction errors (Campbell et al. 2019;Campbell, Cutler, and Dennison 2024).”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Campbell, Cutler, and Dennison 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Trail roughness is described as both median and average
MinorThe results say the on-path comparison used median trail roughness, while the methods say it used the average across 20 trails.
“A STRIDE model using no vegetation cover and median trail surface roughness”
“The average among all 20 trails, found to be 0.03 m, was used as the roughness value in STRIDE”
Citations
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Cited by
Sorted by citations
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Metadata
- Authors
- Campbell, M. J., Cutler, S. L., & Dennison, P. E.
- Affiliations
- University of Utah (US)
- Corresponding author
- Campbell, Michael J.
- Venue
- Scientific Reports
- Published
- 2024-09-13
- Volume
- 14
- Issue
- 1
- Pages
- 21393-21393
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11399398
- OpenAlex
- W4402522867
- Semantic Scholar
- abb02bddad23846248a2a29c80d8e3d4f61c7f26
- Cited by
- 7
- References
- 35
- Citations by year
- 2026: 2 · 2025: 5
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (BCS-2117865)
- Topics
- Social Sciences › Safety Research › Evacuation and Crowd Dynamics
- Life Sciences › Agricultural and Biological Sciences › Forestry › Remote Sensing and LiDAR Applications
- Social Sciences › Urban Studies › Urban Design and Spatial Analysis
- Keywords
- airborne laser scanning, STRIDE model, slope, vegetation density, surface roughness, travel time estimation, human mobility modeling, evacuation simulation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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