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

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 d...

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Bibliographic Details
Main Authors: Xiaolu Zhu, Jinglin Li, Zhihan Liu, Fangchun Yang
Format: Conference Proceeding
Language:English
Subjects:
car sharing
car sharing demand prediction
Correlation
deep learning
depots location
Global Positioning System
optimization
Predictive models
Semantics
stacked auto-encoders
Trajectory
Vehicles
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Summary: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.
ISSN:2379-7703
DOI:10.1109/BigDataCongress.2015.57