Article

Investigating Mobility Patterns through Crowd-sourced Activity Data

2023-08-18

Abstract excerpt

<h4>Objectives: </h4> Investigating trip purposes represents an important phase of travel demand modeling which allows to correctly infer mobility patterns and to better understand travel behavior. Until now, researchers collected information on the motivation for performing a trip mainly through travel surveys. However, traditional methods of acquiring this type of information are challenging and expensive to imp...

Topics

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

Identifiers and source

Literature Corpus work: b7b9b5ef-46d5-5c2b-899b-d61dfcdd7849
DOI: 10.21203/rs.3.rs-3210573/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Investigating Mobility Patterns through Crowd-sourced Activity DataDOI 10.21203/rs.3.rs-3210573/v1Read

Activity Space Maps: a novel human mobility data set for quantifying time spent at riskSemantic proximity Read

Place Generator & Place Interpreter: A new methodology to understand regular mobility patternsSemantic proximity Read

Review on Measuring Human Mobility DiversitySemantic proximity Read

Detecting behavioural bias in GPS location data collected by mobile applicationsSemantic proximity Read

ActivityNET: Neural Networks to Predict Trip Purposes in Public Transport from Individual Smart Card Data and POIs.Semantic proximity Read

Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic DataSemantic proximity Read

Sporadic long-range contacts in out-of-venue settings dominate mass gathering events in GermanySemantic proximity Read

Practical geospatial and sociodemographic predictors of human mobilitySemantic 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

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: an analysis of large-scale mobility dataSemantic proximity Read

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemicSemantic proximity Read

Quantifying Travel Time, Mapping and Validating Health Facility Catchment Areas in Blantyre, MalawiSemantic proximity Read