Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386860093> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W4386860093 endingPage "13898" @default.
- W4386860093 startingPage "13898" @default.
- W4386860093 abstract "Background: Conventional bike sharing systems are frequently adding electric bicycles. A major question now arises: Does the bike sharing system have a sufficient number of ebikes available, and are there customers who prefer to use an ebike even though none are available? Methods: Trip data from three different bike sharing systems (Indego in Philadelphia, Santander Cycles in London, and Metro in Los Angeles and Austin) have been used in this study. To determine if an ebike was available at the station when a customer departed, an algorithm was created. Using only those trips that departed while an ebike was available, a random forest classifier and deep neural network classifier were used to predict whether the trip was completed with an ebike or not. These models were used to predict the potential demand for ebikes at times when no ebikes were available. Results: For the system with the highest prediction accuracy, Santander Cycles in London, between 21% and 27% of the trips were predicted to have used an ebike if one had been available. The most important features were temperature, distance, wind speed, and altitude difference. Conclusion: The prediction methods can help bike sharing operators to estimate the current demand for ebikes." @default.
- W4386860093 created "2023-09-20" @default.
- W4386860093 creator A5013236898 @default.
- W4386860093 date "2023-09-19" @default.
- W4386860093 modified "2023-09-27" @default.
- W4386860093 title "Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests" @default.
- W4386860093 cites W1915739922 @default.
- W4386860093 cites W2011301426 @default.
- W4386860093 cites W2032865436 @default.
- W4386860093 cites W2039884997 @default.
- W4386860093 cites W2120457925 @default.
- W4386860093 cites W2148143831 @default.
- W4386860093 cites W2155261478 @default.
- W4386860093 cites W2225320996 @default.
- W4386860093 cites W2587802550 @default.
- W4386860093 cites W2756144809 @default.
- W4386860093 cites W2772724270 @default.
- W4386860093 cites W2911964244 @default.
- W4386860093 cites W2921161724 @default.
- W4386860093 cites W2946839123 @default.
- W4386860093 cites W2965109312 @default.
- W4386860093 cites W2990346675 @default.
- W4386860093 cites W3006958671 @default.
- W4386860093 cites W3010655531 @default.
- W4386860093 cites W3011750902 @default.
- W4386860093 cites W3013925715 @default.
- W4386860093 cites W3033057516 @default.
- W4386860093 cites W3099878876 @default.
- W4386860093 cites W3147192513 @default.
- W4386860093 cites W3150635270 @default.
- W4386860093 cites W3159861791 @default.
- W4386860093 cites W3190844324 @default.
- W4386860093 cites W3191722013 @default.
- W4386860093 cites W4214878430 @default.
- W4386860093 cites W4214952566 @default.
- W4386860093 cites W4221008870 @default.
- W4386860093 cites W4226059629 @default.
- W4386860093 cites W4283835157 @default.
- W4386860093 cites W4284882848 @default.
- W4386860093 cites W4286484248 @default.
- W4386860093 cites W4311903191 @default.
- W4386860093 cites W4312082836 @default.
- W4386860093 cites W4315926508 @default.
- W4386860093 cites W4328100153 @default.
- W4386860093 cites W4367853042 @default.
- W4386860093 cites W4377139135 @default.
- W4386860093 cites W4385281006 @default.
- W4386860093 cites W4385336169 @default.
- W4386860093 doi "https://doi.org/10.3390/su151813898" @default.
- W4386860093 hasPublicationYear "2023" @default.
- W4386860093 type Work @default.
- W4386860093 citedByCount "0" @default.
- W4386860093 crossrefType "journal-article" @default.
- W4386860093 hasAuthorship W4386860093A5013236898 @default.
- W4386860093 hasBestOaLocation W43868600931 @default.
- W4386860093 hasConcept C119857082 @default.
- W4386860093 hasConcept C127413603 @default.
- W4386860093 hasConcept C157085824 @default.
- W4386860093 hasConcept C169258074 @default.
- W4386860093 hasConcept C22212356 @default.
- W4386860093 hasConcept C2994001137 @default.
- W4386860093 hasConcept C41008148 @default.
- W4386860093 hasConcept C50644808 @default.
- W4386860093 hasConceptScore W4386860093C119857082 @default.
- W4386860093 hasConceptScore W4386860093C127413603 @default.
- W4386860093 hasConceptScore W4386860093C157085824 @default.
- W4386860093 hasConceptScore W4386860093C169258074 @default.
- W4386860093 hasConceptScore W4386860093C22212356 @default.
- W4386860093 hasConceptScore W4386860093C2994001137 @default.
- W4386860093 hasConceptScore W4386860093C41008148 @default.
- W4386860093 hasConceptScore W4386860093C50644808 @default.
- W4386860093 hasIssue "18" @default.
- W4386860093 hasLocation W43868600931 @default.
- W4386860093 hasOpenAccess W4386860093 @default.
- W4386860093 hasPrimaryLocation W43868600931 @default.
- W4386860093 hasRelatedWork W3080419695 @default.
- W4386860093 hasRelatedWork W3122890394 @default.
- W4386860093 hasRelatedWork W3141616519 @default.
- W4386860093 hasRelatedWork W3180236049 @default.
- W4386860093 hasRelatedWork W3209730276 @default.
- W4386860093 hasRelatedWork W4214777342 @default.
- W4386860093 hasRelatedWork W4229021576 @default.
- W4386860093 hasRelatedWork W4289520443 @default.
- W4386860093 hasRelatedWork W4312086519 @default.
- W4386860093 hasRelatedWork W4380759248 @default.
- W4386860093 hasVolume "15" @default.
- W4386860093 isParatext "false" @default.
- W4386860093 isRetracted "false" @default.
- W4386860093 workType "article" @default.