49,450 research outputs found
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.Comment: 39 pages, 10 figure
Mobile Computing in Physics Analysis - An Indicator for eScience
This paper presents the design and implementation of a Grid-enabled physics
analysis environment for handheld and other resource-limited computing devices
as one example of the use of mobile devices in eScience. Handheld devices offer
great potential because they provide ubiquitous access to data and
round-the-clock connectivity over wireless links. Our solution aims to provide
users of handheld devices the capability to launch heavy computational tasks on
computational and data Grids, monitor the jobs status during execution, and
retrieve results after job completion. Users carry their jobs on their handheld
devices in the form of executables (and associated libraries). Users can
transparently view the status of their jobs and get back their outputs without
having to know where they are being executed. In this way, our system is able
to act as a high-throughput computing environment where devices ranging from
powerful desktop machines to small handhelds can employ the power of the Grid.
The results shown in this paper are readily applicable to the wider eScience
community.Comment: 8 pages, 7 figures. Presented at the 3rd Int Conf on Mobile Computing
& Ubiquitous Networking (ICMU06. London October 200
Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities
Optimization of energy consumption in future intelligent energy networks (or
Smart Grids) will be based on grid-integrated near-real-time communications
between various grid elements in generation, transmission, distribution and
loads. This paper discusses some of the challenges and opportunities of
communications research in the areas of smart grid and smart metering. In
particular, we focus on some of the key communications challenges for realizing
interoperable and future-proof smart grid/metering networks, smart grid
security and privacy, and how some of the existing networking technologies can
be applied to energy management. Finally, we also discuss the coordinated
standardization efforts in Europe to harmonize communications standards and
protocols.Comment: To be published in IEEE Communications Surveys and Tutorial
VIRTUALIZED BASEBAND UNITS CONSOLIDATION IN ADVANCED LTE NETWORKS USING MOBILITY- AND POWER-AWARE ALGORITHMS
Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental results
Resource and Application Models for Advanced Grid Schedulers
As Grid computing is becoming an inevitable future, managing, scheduling and monitoring dynamic, heterogeneous resources will present new challenges. Solutions will have to be agile and adaptive, support self-organization and autonomous management, while maintaining optimal resource utilisation. Presented in this paper are basic principles and architectural concepts for efficient resource allocation in heterogeneous Grid environment
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