Identifying critical road sections using the weighted periodicity mined from trajectory data for efficient urban transportation DOI Creative Commons
Hong Gao, Liang Zhou, Yong Dong

и другие.

GIScience & Remote Sensing, Год журнала: 2024, Номер 62(1)

Опубликована: Дек. 30, 2024

Critical road sections (CRS) are part of links the network, which have an obvious influence on urban transportation systems. Identifying CRS would contribute to improving efficient traffic management. However, existing studies pay less attention temporal dynamism and spatial disparity in different scenarios. We propose a method identify network. The new takes sparse tensor decomposition reconstruction for imputation driving speed that is calculated from trajectory data. Then, empirical mode applied calculate weighted periodicity each time series speed. Finally, determined according local autocorrelation periodicity. Taking area Xi'an City, China, as case study, result show could effectively achieve information (R2 >0.67). characterize with considering aliasing effect modes. reflect multi-center characteristics systems, holiday workday. identified by proposed be management maintaining transportation.

Язык: Английский

Environmental determinants of dynamic jogging patterns: Insights from trajectory big data analysis and interpretable machine learning DOI
Wei Yang, Jun Fei, Jingjing Li

и другие.

Applied Geography, Год журнала: 2025, Номер 178, С. 103596 - 103596

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

2
Identifying critical road sections using the weighted periodicity mined from trajectory data for efficient urban transportation DOI Creative Commons
Hong Gao, Liang Zhou, Yong Dong

и другие.

GIScience & Remote Sensing, Год журнала: 2024, Номер 62(1)

Опубликована: Дек. 30, 2024

Critical road sections (CRS) are part of links the network, which have an obvious influence on urban transportation systems. Identifying CRS would contribute to improving efficient traffic management. However, existing studies pay less attention temporal dynamism and spatial disparity in different scenarios. We propose a method identify network. The new takes sparse tensor decomposition reconstruction for imputation driving speed that is calculated from trajectory data. Then, empirical mode applied calculate weighted periodicity each time series speed. Finally, determined according local autocorrelation periodicity. Taking area Xi'an City, China, as case study, result show could effectively achieve information (R2 >0.67). characterize with considering aliasing effect modes. reflect multi-center characteristics systems, holiday workday. identified by proposed be management maintaining transportation.

Язык: Английский

Процитировано

2