Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328100153> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W4328100153 endingPage "101276" @default.
- W4328100153 startingPage "101276" @default.
- W4328100153 abstract "Count-based bicycle demand models have traditionally focused on estimation rather than prediction and have been criticized for lacking a direct causal relationship between significant variables and the activity being modeled. Because they are not choice-based models, they are doubted for their ability to forecast well. The rise of machine learning techniques has given researchers tools to build better predictive models, and the tools to evaluate predictiveness. Extensive previous work in the statistics and machine learning field has shown that the best predictive model is not synonymous with the most true (or explanatory) model. The non-motorized demand modeling community could leverage these lessons learned to develop better count-based predictive models. The rise of the COVID-19 pandemic has clearly affected travel patterns, and the broad data collection has opened-up an opportunity to leverage machine learning techniques to build a predictive bicycle demand model. This study uses bicycle count data, COVID-19 data, and weather data to develop a LASSO regression model for three facilities in Austin, TX. The COVID-19 variables included both state- and local-level data between March 15, 2020, and January 31, 2021. The final model selects six variables out of 28 variables and reveals that the increase of statewide COVID-19 fatalities, statewide molecular positivity rate, and local precipitation cause a decrease in bike ridership, meanwhile maximum temperature causes an increase. The LASSO model also has a lower prediction MSE during cross validation compared to the full model. This paper aims to bring to light that our present-day demand and volume forecasting efforts would benefit tremendously from a predictive modeling approach rather than valuing the most explanatory models as the only strong forecasters of demand. In the end, modelers can use this approach to improve the forecasting ability of any count-based bicycle demand model." @default.
- W4328100153 created "2023-03-22" @default.
- W4328100153 creator A5028211994 @default.
- W4328100153 creator A5057533478 @default.
- W4328100153 creator A5081932419 @default.
- W4328100153 date "2023-09-01" @default.
- W4328100153 modified "2023-10-16" @default.
- W4328100153 title "A machine learning approach to predicting bicycle demand during the COVID-19 pandemic" @default.
- W4328100153 cites W2028966152 @default.
- W4328100153 cites W2066124661 @default.
- W4328100153 cites W2119862467 @default.
- W4328100153 cites W2154290668 @default.
- W4328100153 cites W2284729062 @default.
- W4328100153 cites W240038413 @default.
- W4328100153 cites W3011555384 @default.
- W4328100153 cites W3035425763 @default.
- W4328100153 cites W3093280446 @default.
- W4328100153 cites W3121294329 @default.
- W4328100153 cites W3121452939 @default.
- W4328100153 cites W3132426513 @default.
- W4328100153 cites W3161641388 @default.
- W4328100153 cites W4294541781 @default.
- W4328100153 doi "https://doi.org/10.1016/j.retrec.2023.101276" @default.
- W4328100153 hasPublicationYear "2023" @default.
- W4328100153 type Work @default.
- W4328100153 citedByCount "1" @default.
- W4328100153 countsByYear W43281001532023 @default.
- W4328100153 crossrefType "journal-article" @default.
- W4328100153 hasAuthorship W4328100153A5028211994 @default.
- W4328100153 hasAuthorship W4328100153A5057533478 @default.
- W4328100153 hasAuthorship W4328100153A5081932419 @default.
- W4328100153 hasBestOaLocation W43281001531 @default.
- W4328100153 hasConcept C100906024 @default.
- W4328100153 hasConcept C105795698 @default.
- W4328100153 hasConcept C119857082 @default.
- W4328100153 hasConcept C136764020 @default.
- W4328100153 hasConcept C142724271 @default.
- W4328100153 hasConcept C149782125 @default.
- W4328100153 hasConcept C152877465 @default.
- W4328100153 hasConcept C153083717 @default.
- W4328100153 hasConcept C154945302 @default.
- W4328100153 hasConcept C162324750 @default.
- W4328100153 hasConcept C27181475 @default.
- W4328100153 hasConcept C2779134260 @default.
- W4328100153 hasConcept C3008058167 @default.
- W4328100153 hasConcept C33643355 @default.
- W4328100153 hasConcept C33923547 @default.
- W4328100153 hasConcept C37616216 @default.
- W4328100153 hasConcept C41008148 @default.
- W4328100153 hasConcept C45804977 @default.
- W4328100153 hasConcept C524204448 @default.
- W4328100153 hasConcept C71924100 @default.
- W4328100153 hasConcept C83546350 @default.
- W4328100153 hasConceptScore W4328100153C100906024 @default.
- W4328100153 hasConceptScore W4328100153C105795698 @default.
- W4328100153 hasConceptScore W4328100153C119857082 @default.
- W4328100153 hasConceptScore W4328100153C136764020 @default.
- W4328100153 hasConceptScore W4328100153C142724271 @default.
- W4328100153 hasConceptScore W4328100153C149782125 @default.
- W4328100153 hasConceptScore W4328100153C152877465 @default.
- W4328100153 hasConceptScore W4328100153C153083717 @default.
- W4328100153 hasConceptScore W4328100153C154945302 @default.
- W4328100153 hasConceptScore W4328100153C162324750 @default.
- W4328100153 hasConceptScore W4328100153C27181475 @default.
- W4328100153 hasConceptScore W4328100153C2779134260 @default.
- W4328100153 hasConceptScore W4328100153C3008058167 @default.
- W4328100153 hasConceptScore W4328100153C33643355 @default.
- W4328100153 hasConceptScore W4328100153C33923547 @default.
- W4328100153 hasConceptScore W4328100153C37616216 @default.
- W4328100153 hasConceptScore W4328100153C41008148 @default.
- W4328100153 hasConceptScore W4328100153C45804977 @default.
- W4328100153 hasConceptScore W4328100153C524204448 @default.
- W4328100153 hasConceptScore W4328100153C71924100 @default.
- W4328100153 hasConceptScore W4328100153C83546350 @default.
- W4328100153 hasLocation W43281001531 @default.
- W4328100153 hasOpenAccess W4328100153 @default.
- W4328100153 hasPrimaryLocation W43281001531 @default.
- W4328100153 hasRelatedWork W2027524373 @default.
- W4328100153 hasRelatedWork W2122900100 @default.
- W4328100153 hasRelatedWork W2552172420 @default.
- W4328100153 hasRelatedWork W2759839044 @default.
- W4328100153 hasRelatedWork W3021457118 @default.
- W4328100153 hasRelatedWork W3174196512 @default.
- W4328100153 hasRelatedWork W4200341517 @default.
- W4328100153 hasRelatedWork W4285084790 @default.
- W4328100153 hasRelatedWork W4328100153 @default.
- W4328100153 hasRelatedWork W4362670612 @default.
- W4328100153 hasVolume "100" @default.
- W4328100153 isParatext "false" @default.
- W4328100153 isRetracted "false" @default.
- W4328100153 workType "article" @default.