Machine Learning for the Efficient Prediction of Barrier-Integrated Walk Scores (13645) |
| Cédric Roussel, Konstantin Geist, Klaus Böhm and Markus Schaffert (Germany) |
Mr. Cédric Roussel
Mainz University of Applied Sciences
i3mainz
Mainz
Germany
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| Corresponding author Mr. Cédric Roussel (email: cedric.roussel[at]hs-mainz.de) |
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| [ abstract ] [ paper ] [ handouts ] |
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Published on the web n/a
Received 2025-09-16 / Accepted n/a |
| This paper is one of selection of papers published for the FIG Congress 2026 in Cape Town, South Africa in Cape Town, South Africa and has undergone the FIG Peer Review Process. |
FIG Congress 2026 in Cape Town, South Africa
ISBN n/a ISSN 2308-3441
URL n/a
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Abstract |
| Walkability, understood as the extent to which an environment supports walking as a means of transportation in everyday life, is shaped by local, historically grown urban structures and therefore can vary significantly between cities and regions. To systematically record and compare these differences, walkability is typically measured using pedestrian indices and corresponding assessment tools. Composite assessments that calculate distances to multiple amenities simultaneously, such as the Walk Score, may take a relatively long time to process. Against this background, the present study examines a machine learning approach for the efficient estimation of senior-sensitive walkability using Walk Scores. The approach is being evaluated in two German county districts with the goal of reducing processing time while maintaining accurate approximations.
To evaluate the quality of the model and to avoid overfitting, we compared the overall accuracy of a training and a test dataset, performed cross-validation between our two study regions, and evaluated the performance. The results show that the proposed machine learning approach can generate predictions for Walk Scores that are largely accurate and transferable, with significantly lower computational costs.
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| Keywords: Geoinformation/GI; Spatial planning; Walkability; Walk Score; Machine Learning; Barriers; Elderly |
