Analysis and visual exploration of prediction algorithms for public bicycle sharing systems
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| Titel: | Analysis and visual exploration of prediction algorithms for public bicycle sharing systems |
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| Autoren: | Cortez Ordóñez, Alexandra Piedad, Vázquez Alcocer, Pere Pau |
| Quelle: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Verlagsinformationen: | Curran associates, 2021. |
| Publikationsjahr: | 2021 |
| Schlagwörter: | Anàlisi numèrica, Programming (Mathematics), Matemàtiques i estadística::Investigació operativa::Programació matemàtica [Àrees temàtiques de la UPC], Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica, Classificació AMS::65 Numerical analysis::65K Mathematical programming, optimization and variational techniques, Visualization systems and tools, Visual analytics, 90 Operations research, mathematical programming::90C Mathematical programming [Classificació AMS], Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming, Bike Sharing Systems, Matemàtiques i estadística::Anàlisi numèrica [Àrees temàtiques de la UPC], Forecasting algorithms, 11. Sustainability, Programació (Matemàtica), Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica, 65 Numerical analysis::65K Mathematical programming, optimization and variational techniques [Classificació AMS], Numerical analysis |
| Beschreibung: | Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system’s current status and is of little use for both citizens and responsible planning entities. The vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station’s behavior, hourly intervals, days, or types of days (weekdays and weekends). This case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios. Partially supported by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía y Competitividad, by 839 FEDER (EU) funds. |
| Publikationsart: | Conference object |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Zugangs-URL: | http://hdl.handle.net/2117/363301 https://hdl.handle.net/2117/363301 |
| Dokumentencode: | edsair.dedup.wf.002..c6666e1e8cfeab22562d7928e4dc8b3b |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system’s current status and is of little use for both citizens and responsible planning entities. The vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station’s behavior, hourly intervals, days, or types of days (weekdays and weekends). This case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios.<br />Partially supported by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía y Competitividad, by 839 FEDER (EU) funds. |
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