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Open AccessArticle

Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines

1
Department of Engineering, University of Sannio, piazza Roma 21, 82100 Benevento, Italy
2
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, via Claudio 21, 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(15), 3424; https://doi.org/10.3390/s19153424
Received: 1 July 2019 / Revised: 24 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision. View Full-Text
Keywords: artificial neural networks; metro; transportation; user flow forecast artificial neural networks; metro; transportation; user flow forecast
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MDPI and ACS Style

Gallo, M.; De Luca, G.; D’Acierno, L.; Botte, M. Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines. Sensors 2019, 19, 3424.

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