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ZENODO
Software . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
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GuardianPath: Time-Aware Social Visibility Framework for Safe Pedestrian Route Navigation Using Machine Learning

Authors: AFZA, RUHEEN;

GuardianPath: Time-Aware Social Visibility Framework for Safe Pedestrian Route Navigation Using Machine Learning

Abstract

GuardianPath presents a machine learning–driven framework for safe pedestrian route navigation in urban environments. The system operationalizes Jane Jacobs' "eyes on the street" theory by computing time-aware Social Visibility Scores using five engineered features—Proximity, Active Hour, POI Density, Anchor Presence, and Night Penalty—derived from OpenStreetMap data. An XGBoost regressor (R² = 0.9998) predicts segment-level safety, and a modified Dijkstra's algorithm generates safer routes. TreeSHAP provides per-route explainability. Evaluated on real-world Bengaluru road networks.

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Keywords

urban pedestrian safety, Time-Aware routing, Safe route navigation, XG boost regression, social visibilty scoring

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average