dor_id: 4150049

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/2074/1046

100.1.#.a: Correa, Diego; Moyano, Christian

524.#.#.a: Correa, Diego, et al. (2023). Analysis & Prediction of New York City Taxi and Uber Demands. Journal of Applied Research and Technology; Vol. 21 Núm. 5, 2023; 886-898. Recuperado de https://repositorio.unam.mx/contenidos/4150049

245.1.0.a: Analysis & Prediction of New York City Taxi and Uber Demands

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-10-30

653.#.#.a: Large Scale Data Analysis; GPS-enabled Taxi Data; Machine Learning Algorithms; Taxi & Uber demand Prediction; Visual Analytics; New York City

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/2074

001.#.#.#: 074.oai:ojs2.localhost:article/2074

041.#.7.h: eng

520.3.#.a: Taxi and Uber are an imperative transportation mode in New York City (NYC). This paper investigates the spatiotemporal distribution of pickups of medallion taxi (Yellow), Street Hail Livery Service taxi (Green), and Uber services in NYC, within the five boroughs Brooklyn, the Bronx, Manhattan, Queens, and Staten Island. Regression Models and Machine Learning algorithms such as XGboost and Random Forest are used to predict the ridership of taxis and Uber dataset combined in NYC, given a time window of one-hour and locations within zip-code areas. The dataset consisting of over 90 million trips within the period April-September 2014, being Yellow with 86% the most used in the city, followed by Green with 9% and Uber with 5%. In outer boroughs, the number of pickups is 12.9 million (14%), while 77.9 million (86%) were made in Manhattan only. Yellow is the predominant option in Manhattan and Queens, while Green is preferred in Brooklyn and Bronx. In Staten Island, the market is shared between the three services. However, Uber presents a highly rising trend of 81% in Manhattan and 145% in outer boroughs during the analysis period. The regression model XGboost performed best because of its exceptional capacity to catch complex feature dependencies. The XGboost model accomplished an estimation of 38.51 for RMSE and 0.97 for R^2. This modelcould present valuable insights to taxi companies, decision-makers, and city planners in responding to questions, e.g., how to situate taxis where they are generally required, understand how ridership shifts over time, and the total number of taxis needed to dispatch in order to meet de the demand.

773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 5 (2023); 886-898

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 886-898

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2023.21.5.2074

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

file_creation_date: 2023-10-30 01:15:05.0

file_modification_date: 2023-10-30 01:15:13.0

file_creator: Yolanda G.G.

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