Travel demand and distance analysis for free-floating car sharing based on deep learning method


Autoři: Chen Zhang aff001;  Jie He aff001;  Ziyang Liu aff001;  Lu Xing aff001;  Yinhai Wang aff002
Působiště autorů: School of Transportation, Southeast University, Dongnandaxuelu, Nanjing, P.R. China aff001;  Smart Transportation Applications and Research Laboratory, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223973

Souhrn

In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample.

Klíčová slova:

Deep learning – Forecasting – Fuels – Human mobility – Recurrent neural networks – Statistical data – Statistical methods – Statistical models


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Článek vyšel v časopise

PLOS One


2019 Číslo 10