Springer Nature OA
open access
Identifying Cycling Accident Hotspots Through Spatial-Temporal Network Kernel Density Estimation: Evidence from Madrid
This study applies the Spatiotemporal Network Kernel Density Estimation (ST-NKDE) methodology to the analysis of cycling accidents in Centro of Madrid between 2019 and 2024. Unlike traditional methods, which either disregard the temporal dimension or the spatial constraints of the road network, this
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Developing an approach to estimate the number of road traffic crashes at the national level using media-reported data.#9685
- Developing an approach to estimate the number of road traffic crashes at the national level using media-reported data#5077
- Multimodal Machine Learning to Predict Wild Boar Collision Risk on the French Railway Network#441549
- Bayesian shared-component spatiotemporal modeling of sexually transmitted infection co-occurrence: identifying geographic vulnerability across 204 countries, 1990-2023#467688
- CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety strata#1335831
Record · ID 1471762
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive