By cryptoadmin 
February 25, 2025
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HikingTTE: A deep studying method for mountaineering journey time estimation based mostly on private strolling means
On the College of Electro-Communications, a analysis crew led by Mizuho Asako, Yasuyuki Tahara, Akihiko Ohsuga, and Yuichi Sei has developed a brand new deep studying mannequin referred to as "HikingTTE" that considerably improves mountaineering journey time estimation. Mountaineering is standard worldwide, however accidents nonetheless happen when hikers underestimate the time wanted to achieve their vacation spot.
The work is printed within the journal Cybernetics and Info Applied sciences.
This mannequin may assist scale back mountain accidents and enhance hiker security by offering extra correct journey time predictions. Earlier mountaineering journey time estimation strategies typically use the connection between slope (uphill or downhill) and strolling velocity. Nevertheless, these strategies don’t absolutely bear in mind particular person strolling means or how fatigue builds up over lengthy distances.
HikingTTE addresses these points by combining a modified Lorentz function-based slope-speed perform with a deep studying framework that features LSTM (Lengthy Brief-Time period Reminiscence) and a spotlight modules. LSTM is nicely suited to dealing with time-series knowledge, whereas the eye mechanism highlights necessary components of the GPS knowledge for extra correct predictions.
A key power of HikingTTE is its means to be taught a hiker's strolling means from solely a part of the GPS knowledge recorded in the course of the journey. By analyzing the efficiency on the primary a part of the route, the mannequin creates a slope-speed perform for that individual after which applies it to estimate the remaining journey time.
Moreover, by utilizing LSTM and an attention-based mechanism, HikingTTE accounts for adjustments in terrain and the results of fatigue, resulting in extra dependable estimates than current fashions.
In experiments, HikingTTE outperformed typical mountaineering journey time estimation methods, lowering the Imply Absolute Proportion Error (MAPE) by 12.95 share factors. It additionally outperformed different deep studying fashions initially designed for transportation duties by 0.97 share factors. The analysis crew believes that these outcomes may set a brand new normal for mountaineering journey time estimation.
Sooner or later, the crew plans to incorporate every hiker's previous logs to additional personalize the predictions. By serving to hikers plan and regulate their tempo extra successfully, this innovation is anticipated to forestall delays, decrease dangers, and in the end save lives on the path. The mannequin may be built-in into mountaineering apps or navigation instruments, offering sensible and dependable steering.
Extra data: Mizuho Asako et al, Deep Studying-Primarily based Journey Time Estimation in Mountaineering with Consideration of Particular person Strolling Means, Cybernetics and Info Applied sciences (2024). DOI: 10.2478/cait-2024-0033
Supplied by The College of Electro-Communications Quotation: HikingTTE: A deep studying method for mountaineering journey time estimation based mostly on private strolling means (2025, February 25) retrieved 25 February 2025 from https://techxplore.com/information/2025-02-hikingtte-deep-approach-hiking-based.html This doc is topic to copyright. Aside from any honest dealing for the aim of personal examine or analysis, no half could also be reproduced with out the written permission. The content material is supplied for data functions solely.
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