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Erschienen in:

24.07.2021

Predictions of Taxi Demand Based on Neural Network Algorithms

verfasst von: Chung-Yi Lin, Shen-Lung Tung, Po-Wen Lu, Tzu-Cheng Liu

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 3/2021

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Abstract

To increase the profit both of taxi drivers and operators, this paper proposes an approach that efficiently collects the features of a customized-shape dispatch area to build the multivariate time-series prediction models for forecasting taxi demands. We also considered population distribution obtained from IMSI (International Mobile Subscriber Identity) data as the spatial correlations feature. The predictive models are built on some neural network algorithms and analyzed statistically. The experiments show that the predictions of the taxi demand in the next 30 minutes are successfully achieved. It is noteworthy that our approach outperforms the forecasting accuracy proved by a real-world error metric.

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Metadaten
Titel
Predictions of Taxi Demand Based on Neural Network Algorithms
verfasst von
Chung-Yi Lin
Shen-Lung Tung
Po-Wen Lu
Tzu-Cheng Liu
Publikationsdatum
24.07.2021
Verlag
Springer US
Erschienen in
International Journal of Intelligent Transportation Systems Research / Ausgabe 3/2021
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-020-00248-9

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