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Explaining and Predicting Station Demand Patterns Using Google Popular Times Data · Study

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ArticleExplaining and Predicting Station Demand Patterns Using Google Popular Times Data

Teethat Vongvanich2023

OA

Articlegreen2023

Explaining and Predicting Station Demand Patterns Using Google Popular Times Data

Teethat Vongvanich · Wenzhe Sun · Jan‐Dirk Schmöcker

11 citations·0/0 files stored·Data Science for Transportation

No readable copy stored yet — this record is metadata only.

Human Mobility and Location-Based Analysis Transportation Planning and Optimization Urban Transport and Accessibility

Abstract

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Citations in this library

Stored only when both papers are in the corpus — 11 citations exist upstream.

References

2011Modelling Transport 2016The Multi-Agent Transport Simulation MATSim 2011Smart card data use in public transit: A literature review 1992Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems 2014Digital Footprints: Opportunities and Challenges for Online Social Research 2007Evaluating passive mobile positioning data for tourism surveys: An Estonian case study 2009MATSim-T 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model

Cited by

2023On the Relationship Between Crowdsourced Sentiments and Mobility Trends During COVID-19: A Case Study of Kyoto

Details

Year2023

Typearticle

Published2023-06-26

VenueData Science for Transportation

ISSN-L2948-135X

Languageen

Citations11

Files0 public / 0

ShelfHuman Mobility and Location-Based Analysis

Identifiers

doi10.1007/s42421-023-00072-z

openalex_workW4382022345

Links

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