Work

Explaining and Predicting Station Demand Patterns Using Google Popular Times Data

Year: 2023

Type: article

Source: Data Science for Transportation

DOI: 10.1007/s42421-023-00072-z

Authors: Teethat Vongvanich, Wenzhe Sun, Jan-Dirk Schmöcker

Institution: Kyoto University

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

Funders: Japan Science and Technology Agency, Strategic International Collaborative Research Program

Awards: ディープラーニングを用いた都市モビリティのピーク予測（ＤＡＲＵＭＡ）, G6856500701

OpenAlex ID: W4382022345

FWCI: 2.259

Cites: 38

Cited by: 11

Related to: 10