f85e5d3505074ded06f8219199d284e2 com_a2023m2v12n2p33.pdf 66341528fdbf2971ddf658b1120552c790e53fa6 com_a2023m2v12n2p33.pdf f3c60d7c7fe0df8d5447b2b72ad473e00735ac9e82826f5b7c950378fd434c02 com_a2023m2v12n2p33.pdf Title: Enhancing Carsharing Experiences for Barcelona Citizens with Data Analytics and Intelligent Algorithms Subject: black Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens' needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including blackscattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens' needs and behavior in relation to carsharing services. The analysis includes three main components: descriptive, predictive, and prescriptive, resulting in customer segmentation and forecast of service demand, as well as original concepts for optimizing parking station location. black Keywords: carsharing; data analytics; machine learning; intelligent algorithms; smart cities Author: Erika M. Herrera, Laura Calvet, Elnaz Ghorbani, Javier Panadero, Angel A. Juan Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Feb 17 03:12:40 2023 CET ModDate: Fri Feb 17 03:20:37 2023 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 17 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4251116 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- YHFYTP+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 GUWLJW+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 PFDEBX+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 RPRBMD+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 AAAAAA+ArialMT CID TrueType Identity-H yes yes yes 73 0 BAAAAA+Arial-BoldMT CID TrueType Identity-H yes yes yes 78 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 122 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 126 0 RNSDPU+CMSY10 Type 1 Builtin yes yes no 130 0 QIELJB+CMMI10 Type 1 Builtin yes yes no 151 0 NKBHGE+ArialMT TrueType WinAnsi yes yes yes 157 0 IOLAWT+PazoMath-Italic Type 1 Builtin yes yes no 166 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-04-23 02:36:03 CEST RepresentationInformation: com_a2023m2v12n2p33.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-04-22 15:32:09 CEST Size: 4251116 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 318 FreeObjects: 3 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Literature Review Destination: section.2 Item: Title: Methodology Destination: section.3 Item: Title: A Case Study in the City of Barcelona Destination: section.4 Item: Title: Customer Segmentation Destination: section.5 Item: Title: Predicting the Number of Reservations Destination: section.6 Item: Title: Selecting a Scattered yet Effective Location for Pick-Up Points Destination: section.7 Item: Title: Conclusions Destination: section.8 Item: Title: References Destination: section.8 Info: Title: Enhancing Carsharing Experiences for Barcelona Citizens with Data Analytics and Intelligent Algorithms Author: Erika M. Herrera, Laura Calvet, Elnaz Ghorbani, Javier Panadero, Angel A. Juan Subject: black Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens' needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including blackscattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens' needs and behavior in relation to carsharing services. 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