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Rodrigues, P., Martins, A., Kalakou, S. & Moura, F. (2019). Spatiotemporal variation of taxi demand. In Esteve Codina, Francesc Soriguera, Lídia Montero, Miquel Estrada, M. Paz Linares (Ed.), 22nd EURO Working Group on Transportation Meeting, EWGT 2019. (pp. 664-671). Barcelona

P. Rodrigues et al.,  "Spatiotemporal variation of taxi demand", in 22nd EURO Working Group on Transportation Meeting, EWGT 2019, Esteve Codina, Francesc Soriguera, Lídia Montero, Miquel Estrada, M. Paz Linares, Ed., Barcelona, 2019, vol. 47, pp. 664-671

@inproceedings{rodrigues2019_1732196774834,
	author = "Rodrigues, P. and Martins, A. and Kalakou, S. and Moura, F.",
	title = "Spatiotemporal variation of taxi demand",
	booktitle = "22nd EURO Working Group on Transportation Meeting, EWGT 2019",
	year = "2019",
	editor = "Esteve Codina, Francesc Soriguera, Lídia Montero, Miquel Estrada, M. Paz Linares",
	volume = "47",
	number = "",
	series = "",
	doi = "10.1016/j.trpro.2020.03.145",
	pages = "664-671",
	publisher = "",
	address = "Barcelona",
	organization = "EWGT"
}

TY  - CPAPER
TI  - Spatiotemporal variation of taxi demand
T2  - 22nd EURO Working Group on Transportation Meeting, EWGT 2019
VL  - 47
AU  - Rodrigues, P.
AU  - Martins, A.
AU  - Kalakou, S.
AU  - Moura, F.
PY  - 2019
SP  - 664-671
DO  - 10.1016/j.trpro.2020.03.145
CY  - Barcelona
AB  - 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.
ER  -