NDLI: Optimization Approach to Depot Location in Car Sharing Systems with Big Data

Content Provider	IEEE Xplore Digital Library
Author	Xiaolu Zhu Jinglin Li Zhihan Liu Fangchun Yang
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Networking & Switching Technol., Beijing Univ. of Posts & Telecommun., Beijing, China (Xiaolu Zhu; Jinglin Li; Zhihan Liu; Fangchun Yang)
Abstract	Determining the location of depots of car sharing systems is a fundamental problem in car sharing systems. Existing methods to determine the location of depots mainly use qualitative method and do not take real demand into account. This paper proposes a novel optimization approach to determine the depot location in car sharing systems scientifically. To predict the car sharing demand accurately, we propose a deep learning approach which has been implemented as a stacked auto-encoder (SAE) model at the bottom with a logistic regression layer at the top. The SAE model is employed for unsupervised feature learning, which has been proved to be effective. Meanwhile the spatial and temporal correlations is considered inherently in the prediction model. The results allow us to determine the location of depots scientifically. Experiments on the datasets illustrate that the proposed model for car sharing demand prediction has superior performance.
Starting Page	335
Ending Page	342
File Size	430470
Page Count	8
File Format	PDF
e-ISBN	9781467372787
DOI	10.1109/BigDataCongress.2015.57
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deep learning Correlation Semantics Predictive models Depots location Stacked auto-encoders Trajectory Car sharing demand prediction Optimization Car sharing Vehicles Global Positioning System
Content Type	Text
Resource Type	Article

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