Article

Predicting and Generating Urban Human Mobility Flows with Random Forests and Land Cover Data

2022-06-14

Abstract excerpt

Models of human mobility are crucial to understanding transit needs, demographic trends, and disease outbreaks, among much else. Since the start of the COVID-19 pandemic, new datasets have arrived from public and private sources, and the literature has advanced with new modelling techniques using deep learning and machine learning. Inspired by these breakthroughs, this paper seeks to develop a mobility model of mo...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dd76d4c1-3151-56b4-8586-4c18b8dd1862
DOI: 10.2139/ssrn.4135090

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predicting and Generating Urban Human Mobility Flows with Random Forests and Land Cover DataDOI 10.2139/ssrn.4135090Read

Learning the complexity of urban mobility with deep generative collaboration networkSemantic proximity Read

Practical geospatial and sociodemographic predictors of human mobilitySemantic proximity Read

COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United StatesSemantic proximity Read

Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and incomeSemantic proximity Read

FairMobi-Net: A Fairness-Aware Deep Learning Model for Urban Mobility Flow GenerationSemantic proximity Read

Predicting COVID-19 outbreak using open mobility data for minimal disruption on the country’s economySemantic proximity Read

AMBIT: Augmenting Mobility Baselines with Interpretable TreesSemantic proximity Read

Review on Measuring Human Mobility DiversitySemantic proximity Read

Downscaling human mobility data based on demographic, socioeconomic, and commuting characteristics using interpretable machine learning methodsSemantic proximity Read

Quantifying Complex Urban Spillover Effects via Physics-based Deep LearningSemantic proximity Read

Limitations of gravity models in predicting fine-scale spatial-temporal urban mobility networksSemantic proximity Read

Decomposing geographical and universal aspects of human mobilitySemantic proximity Read