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

被引：0

作者：

Jin, Jieling ^{[1
]}

Liu, Pan ^{[2
]}

Huang, Helai ^{[1
]}

Dong, Yuxuan ^{[1
]}

机构：

[1] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410000, Hunan, Peoples R China

[2] Natl Univ Singapore, Dept Civil & Environm Engn, Singapore, Singapore

来源：

APPLIED GEOGRAPHY | 2024年 / 172卷

关键词：

Urban traffic safety; Traffic crash frequency; Spatio-temporal pattern; City-level SNMFS; Sparse data; Traffic management; MORANS I; PREDICTION; FREQUENCY; COLLISIONS; SEVERITY; NETWORK;

D O I：

10.1016/j.apgeog.2024.103402

中图分类号：

P9 [自然地理学]; K9 [地理];

学科分类号：

0705 ; 070501 ;

摘要：

Urban traffic crashes represent a significant challenge affecting public safety and urban mobility worldwide. This study introduces a novel application of Sparse Non-negative Matrix Factorization with spatial constraints to analyze spatio-temporal patterns of traffic crashes at a city level. Using comprehensive crash data from Denver and Manhattan during 2020, we developed and validated a model capable of capturing distinct temporal dynamics and spatial distributions of traffic crashes. Unlike traditional methods, our approach integrates sparsity and spatial constraints, enhancing the model's ability to handle the inherent sparsity and geographical dependencies found in urban traffic data. The results demonstrate the model's effectiveness in identifying high-risk areas and times, providing actionable insights that can inform urban planning and targeted safety interventions. The study underscores the potential of advanced data-driven techniques in urban traffic analysis and contributes to the broader efforts of improving traffic safety through informed decision-making and policy development.

引用

页数：13