NDLI: Short-term FFBS demand prediction with multi-source data in a hybrid deep learning framework

Content Provider	IET Digital Library
Author	Bao, Jie Yu, Hao Wu, Jiaming
Abstract	The primary objective of this study is to predict the short-term demand of free-floating bike sharing (FFBS) using deep learning approach. The FFBS trip data in Shanghai city are collected from the Mobike Company. Other datasets such as weather data and air quality data are also collected. The spatiotemporal patterns of FFBS demand indicates that the weekday rides exhibit an obvious commuting pattern while the weekend rides are usually involved with various trip purposes. Then, a hybrid deep learning neural network (HDL-net) is developed to predict the short-term FFBS demand for different time intervals including 15, 20 and 30 min. The proposed HDL-net exhibits better performance on morning peak than the evening peak and non-peak hours for all the three time intervals. Moreover, five benchmark methods are also used to compare with the proposed HDL-net. The results suggest that the proposed hybrid deep learning framework outperform the benchmarks in the prediction performance for all three time intervals. The results of this study could provide insightful suggestions for transportation authorities to develop effective rebalancing strategies and bike lanes planning schemes to promote the service level of cycling in an urban city.
Starting Page	1340
Ending Page	1347
Page Count	8
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 9, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0008
Journal	IET Intelligent Transport Systems
Publisher Date	2019-04-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Air Quality Data Artificial Neural Network Autoregressive Moving Average Processes Bicycles Bike Lanes Bike-sharing Service Provider Combinatorial Mathematics Commuting Pattern Convolutional Neural Nets Convolutional Neural Network Deep Learning Approach Evening Peak FFBS Trip Data Free-floating Bike Sharing HDL-net Exhibits Better Performance Hourly Aggregated Weather Data Hybrid Deep Learning Framework Hybrid Deep Learning Neural Network Knowledge Engineering Technique Learning in AI Morning Peak Multisource Data Neural Computing Technique Nonpeak Hours Prediction Performance Recurrent Neural Nets Regression Analysis Road Traffic Shanghai City Short-term FFBS Demand Prediction Short-term Memory Short-term Travel Demand Spatiotemporal Pattern Statistics Traffic Engineering Computing Transportation Weekday Rides Weekend Rides
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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