Analyzing urban traffic crash patterns through spatio-temporal data: A city-level study using a sparse non-negative matrix factorization model with spatial constraints approach-Reference-Cited by-同舟云学术

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Analyzing urban traffic crash patterns through spatio-temporal data: A city-level study using a sparse non-negative matrix factorization model with spatial constraints approach

Published:2024-11 Issue: Volume:172 Page:103402
ISSN:0143-6228
Container-title:Applied Geography
language:en
Short-container-title:Applied Geography

Author:

Jin Jieling^ORCID,Liu Pan,Huang Helai,Dong Yuxuan

Funder

Hunan Provincial Natural Science Foundation

Fundamental Research Funds for Central Universities of the Central South University

National Key Research and Development Program of China

National Key Scientific Instrument and Equipment Development Projects of China

Publisher

Elsevier BV

Reference62 articles.

1. Modeling traffic accident occurrence and involvement;Abdel-Aty;Accident Analysis & Prevention,2000

2. Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review;Ali;Accident Analysis & Prevention,2024

3. An effective short-text topic modelling with neighbourhood assistance-driven nmf in twitter;Athukorala;Social Network Analysis and Mining,2022

4. Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data;Bao;Journal of Transport Geography,2021

5. Perccs: Person-count from carbon dioxide using sparse non-negative matrix factorization;Basu,2015

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