7 research outputs found
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
MLET: A Power Efficient Approach for TCAM Based, IP Lookup Engines in Internet Routers
Routers are one of the important entities in computer networks specially the
Internet. Forwarding IP packets is a valuable and vital function in Internet
routers. Routers extract destination IP address from packets and lookup those
addresses in their own routing table. This task is called IP lookup. Internet
address lookup is a challenging problem due to the increasing routing table
sizes. Ternary Content-Addressable Memories (TCAMs) are becoming very popular
for designing high-throughput address lookup-engines on routers: they are fast,
cost-effective and simple to manage. Despite the TCAMs speed, their high power
consumption is their major drawback. In this paper, Multilevel Enabling
Technique (MLET), a power efficient TCAM based hardware architecture has been
proposed. This scheme is employed after an Espresso-II minimization algorithm
to achieve lower power consumption. The performance evaluation of the proposed
approach shows that it can save considerable amount of routing table's power
consumption.Comment: 14 Pages, IJCNC 201
Seismic performance of ordinary RC frames retrofitted at joints by FRP sheets
This paper reports on the results of an investigation into the effectiveness of FRP retrofitting the joints in enhancing the seismic performance level and the seismic behaviour factor (R) of ordinary RC frames. The flexural stiffness of FRP retrofitted joints of the frame is first determined using nonlinear analyses of detailed FE models of RC-joint–FRP composite. The retrofitted joint stiffness is then implemented into the FE model of the frame in order to carry out nonlinear static (pushover) analyses on the FRP retrofitted frame. The seismic performance level and R-factor components of the retrofitted frame are then compared with those of the original frame and the same frame retrofitted with steel bracings, reported previously. The results show that the performance level and the seismic behaviour factor of the FRP retrofitted RC frame are significantly enhanced in comparison with the original frame and are comparable with those of the steel-braced frame. It is also found that using FRP at joints may upgrade an ordinary RC frame to an intermediate and even a high ductility frame
Modeling Relationships between Surface Water Quality and Landscape Metrics Using the Adaptive Neuro-Fuzzy Inference System, A Case Study in Mazandaran Province
Landscape indices can be used as an approach for predicting water quality changes to monitor non-point source pollution. In the present study, the data collected over the period from 2012 to 2013 from 81 water quality stations along the rivers flowing in Mazandaran Province were analyzed. Upstream boundries were drawn and landscape metrics were extracted for each of the sub-watersheds at class and landscape levels. Principal component analysis was used to single out the relevant water quality parameters and forward linear regression was employed to determine the optimal metrics for the description of each parameter. The first five components were able to describe 96.61% of the variation in water quality in Mazandaran Province. Adaptive Neuro-fuzzy Inference System (ANFIS) and multiple linear regression were used to model the relationship between landscape metrics and water quality parameters. The results indicate that multiple regression was able to predict SAR, TDS, pH, NO3‒, and PO43‒ in the test step, with R2 values equal to 0.81, 0.56, 0.73, 0.44. and 0.63, respectively. The corresponding R2 value of ANFIS in the test step were 0.82, 0.79, 0.82, 0.31, and 0.36, respectively. Clearly, ANFIS exhibited a better performance in each case than did the linear regression model. This indicates a nonlinear relationship between the water quality parameters and landscape metrics. Since different land cover/uses have considerable impacts on both the outflow water quality and the available and dissolved pollutants in rivers, the method can be reasonably used for regional planning and environmental impact assessment in development projects in the region