Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
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...
| Authors: | , , , , |
|---|---|
| Format: | article |
| Publication Date: | 2023 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/384883 |
| Online Access: | https://hdl.handle.net/2117/384883 https://dx.doi.org/10.3390/computers12020033 |
| Access Level: | Open access |
| Keyword: | Artificial intelligence Data structures (Computer science) Smart cities Carsharing Data analytics Machine learning Intelligent algorithms Intel·ligència artificial Estructures de dades (Informàtica) Ciutats intel·ligents Àrees temàtiques de la UPC::Economia i organització d'empreses |
| Summary: | 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 scattering 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. |
|---|