Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/20840
Author(s): | Rodrigues, P. Martins, A. Kalakou, S. Moura, F. |
Editor: | Esteve Codina, Francesc Soriguera, Lídia Montero, Miquel Estrada, M. Paz Linares |
Date: | 2019 |
Title: | Spatiotemporal variation of taxi demand |
Volume: | 47 |
Pages: | 664 - 671 |
Event title: | 22nd EURO Working Group on Transportation Meeting |
ISSN: | 2352-1465 |
DOI (Digital Object Identifier): | 10.1016/j.trpro.2020.03.145 |
Keywords: | Taxi demand ARIMA Artificial Neural Network |
Abstract: | The growth of urban areas has made taxi service become increasingly more popular due to its ubiquity and flexibility when compared with, more rigid, public transportation modes. However, in big cities taxi service is still unbalanced, resulting in inefficiencies such as long waiting times and excessive vacant trips. This paper presents an exploratory taxi fleet service analysis and compares two forecast models aimed at predicting the spatiotemporal variation of short-term taxi demand. For this paper, we used a large sample with more than 1 million trips between 2014 and 2017, representing roughly 10% of Lisbon’s fleet. We analysed the spatiotemporal variation between pick-up and drop-off locations and how they are affected by weather conditions and points of interest. More, based on historic data, we built two models to predict the demand, ARIMA and Artificial Neural Network (ANN), and evaluated and compared the performance of both models. This study not only allows the direct comparison of a linear statistical model with a machine learning one, but also leads to a better comprehension of complex interactions surrounding different urban data sources using the taxi service as a probe to better understand urban mobility-on-demand and its needs. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | BRU-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
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Spatiotemporal Variation of Taxi Demand.pdf | Versão Editora | 1,09 MB | Adobe PDF | View/Open |
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