FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3126355907> ?p ?o ?g. }

• W3126355907 abstract "With the increasing use of big data in varied applications to improve decision making and provide new insights, the research explores the potential of the Uber Movement data set released by Uber comprising of travel times from one zone to the other. A better understanding of the potential of the dataset could lead to the addition of the existing tool kit of Transport planners and city officials at the municipality of Amsterdam. Moreover, it would be the first of a kind data set enabling an understanding of taxi movement in the city. The Uber Movement Travel Time comprises of the average travel time between two wijken, where the ‘sourceid’ and ‘dstid’ do not correspond to the origin and destination of a trip but simply represent the directionality of the travel time measured. The data is aggregated across different levels of temporal detail and the number of data points directly corresponds to the level of temporal aggregation. For instance, if the quarterly aggregated data for the different days of the week is downloaded, the number of data points between a ‘sourceid’ and ‘dstid’ cannot exceed seven. Three aspects of the data set were explored: 1) ability to capture the demand for Ubers 2) ability to capture recurrent congestion and 3) ability to capture non-recurrent congestion. While the data according to the Uber Movement and previously used instances, the data is suited for performance (recurrent congestion and non-recurrent congestion) and impact-related studies of the network. The absence of route related information limits the applications of the data. The potential of the data is also limited by the data sparsity. The potential of the data was best revealed through demand studies which indicated a skewed user group of tourists, airport users (to and fro), work-related trips and users using Ubers late at night. In addition, with respect to the goals of the municipality in managing traffic activity across different zones and time periods, by implementing and extending an existing model in the form of adding ‘occupancy related measures’ and ‘shortest path’. Thus, based on the data penetration levels and travel time data, the model developed offers insights at a strategic level to the city in the form of Spatio-temporal concentration of Uber vehicles, occupancy levels through the day. The potential of the data lies in its ability to offer strategic insights to the city of Amsterdam and the greater Amsterdam region in the form of the unique Spatio-temporal spread of Uber vehicles across different hours of the day." @default.
• W3126355907 created "2021-02-15" @default.
• W3126355907 creator A5065127090 @default.
• W3126355907 date "2019-01-01" @default.
• W3126355907 modified "2023-09-27" @default.
• W3126355907 title "Exploring the Potential of Uber Movement Data: An Amsterdam case study" @default.
• W3126355907 hasPublicationYear "2019" @default.
• W3126355907 type Work @default.
• W3126355907 sameAs 3126355907 @default.
• W3126355907 citedByCount "0" @default.
• W3126355907 crossrefType "journal-article" @default.
• W3126355907 hasAuthorship W3126355907A5065127090 @default.
• W3126355907 hasConcept C107038049 @default.
• W3126355907 hasConcept C124101348 @default.
• W3126355907 hasConcept C138885662 @default.
• W3126355907 hasConcept C154945302 @default.
• W3126355907 hasConcept C177264268 @default.
• W3126355907 hasConcept C199360897 @default.
• W3126355907 hasConcept C205649164 @default.
• W3126355907 hasConcept C2522767166 @default.
• W3126355907 hasConcept C2780226923 @default.
• W3126355907 hasConcept C41008148 @default.
• W3126355907 hasConcept C58489278 @default.
• W3126355907 hasConcept C75684735 @default.
• W3126355907 hasConceptScore W3126355907C107038049 @default.
• W3126355907 hasConceptScore W3126355907C124101348 @default.
• W3126355907 hasConceptScore W3126355907C138885662 @default.
• W3126355907 hasConceptScore W3126355907C154945302 @default.
• W3126355907 hasConceptScore W3126355907C177264268 @default.
• W3126355907 hasConceptScore W3126355907C199360897 @default.
• W3126355907 hasConceptScore W3126355907C205649164 @default.
• W3126355907 hasConceptScore W3126355907C2522767166 @default.
• W3126355907 hasConceptScore W3126355907C2780226923 @default.
• W3126355907 hasConceptScore W3126355907C41008148 @default.
• W3126355907 hasConceptScore W3126355907C58489278 @default.
• W3126355907 hasConceptScore W3126355907C75684735 @default.
• W3126355907 hasLocation W31263559071 @default.
• W3126355907 hasOpenAccess W3126355907 @default.
• W3126355907 hasPrimaryLocation W31263559071 @default.
• W3126355907 hasRelatedWork W1905262251 @default.
• W3126355907 hasRelatedWork W2054007841 @default.
• W3126355907 hasRelatedWork W2144506299 @default.
• W3126355907 hasRelatedWork W2232898627 @default.
• W3126355907 hasRelatedWork W2565658586 @default.
• W3126355907 hasRelatedWork W2617058181 @default.
• W3126355907 hasRelatedWork W2741683156 @default.
• W3126355907 hasRelatedWork W2756414492 @default.
• W3126355907 hasRelatedWork W2933971328 @default.
• W3126355907 hasRelatedWork W2949335274 @default.
• W3126355907 hasRelatedWork W2982076623 @default.
• W3126355907 hasRelatedWork W3039609258 @default.
• W3126355907 hasRelatedWork W3158353711 @default.
• W3126355907 hasRelatedWork W3193962989 @default.
• W3126355907 hasRelatedWork W3211949856 @default.
• W3126355907 hasRelatedWork W609740712 @default.
• W3126355907 hasRelatedWork W63010435 @default.
• W3126355907 hasRelatedWork W646576969 @default.
• W3126355907 hasRelatedWork W6638422 @default.
• W3126355907 hasRelatedWork W1872333779 @default.
• W3126355907 isParatext "false" @default.
• W3126355907 isRetracted "false" @default.
• W3126355907 magId "3126355907" @default.
• W3126355907 workType "article" @default.