The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep Learning-Based Travel Time Estimation in Hiking with Consideration of Individual Walking Ability
Abstract
Hiking is popular, but mountain accidents are serious problems. Accurately predicting hiking travel time is an essential factor in preventing mountain accidents. However, it is challenging to accurately reflect individual hiking ability and the effects of fatigue in travel time estimation. Therefore, this study proposes a deep learning model, “HikingTTE”, for estimating arrival times when hiking. HikingTTE estimates hiking travel time by considering complex factors such as individual hiking ability, changes in walking pace, terrain, and elevation. The proposed model achieved significantly higher accuracy than existing hiking travel time estimation methods based on the relation between slope and speed. Furthermore, HikingTTE demonstrated higher accuracy in predicting hiking arrival times than a deep learning model originally developed to estimate taxi arrival times. The source code of HikingTTE is available on github for future development of the travel time estimation task.
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Studies that have used machine learning to model travel rate have either not included a vegetation measure or used a very generalised vegetation measure.
“Studies that have used machine learning to model travel rate have either not included a vegetation measure or used a very generalised vegetation measure (Wood et al. 2023;Asako et al. 2024).”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Asako et al. 2024
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data
Campbell, M. J., Dennison, P. E., & Thompson, M. P. (2022) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101866 · Cited by 26
Improved prediction of hiking speeds using a data driven approach
Wood, A., Mackaness, W., Simpson, T. I., & Armstrong, J. D. (2023) · PLOS One · doi:10.1371/journal.pone.0295848 · Cited by 15
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Research works printed but not linked: 21
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Cited by
Sorted by citations
Cutler, S. L., Campbell, M. J., Brewer, S. C., & Dennison, P. E. (2026) · GIScience & Remote Sensing · doi:10.1080/15481603.2026.2626632
Metadata
- Authors
- Asako, M., Tahara, Y., Ohsuga, A., & Sei, Y.
- Affiliations
- The University of Electro-Communications, Graduate School of Informatics and Engineering Departments, Department of Informatics 1-5-1 Chofugaoka , Chofu , Japan
- Venue
- Cybernetics and Information Technologies
- Publisher
- Walter de Gruyter GmbH
- Published
- 2024-12-01
- Volume
- 24
- Issue
- 4
- Pages
- 3-21
- ISSN
- 1314-4081
- Cited by
- 6
- References
- 3
- Language
- en
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