/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.

Support Vector Regression

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2016.0208

LSTM network: a deep learning approach for short-term traffic forecast

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN .

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.

/pdf/travel-time-prediction-with-support-vector-regression-3nxajn3hpe.pdf

Travel-time prediction with support vector regression

Travel time prediction is essential for the development of advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time predictions and compare its results to the other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector regression performs well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can reduce significantly both relative mean errors and root mean squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and perform well for traffic data analysis.

/pdf/travel-time-prediction-with-support-vector-regression-udrfuxlx98.pdf

Travel time prediction with support vector regression

A peer-to-peer (P2P) system will starve from resources if every user is a freeloader who only takes resources from the system but never contributes any. It is useful to develop a scheme that can incentivize users to contribute resources such as content, CPU, storage and network bandwidth. This paper describes an admission control system capable of providing differentiated services to users based on their past contributions or reputations. A user desiring a certain level of service will need to make a comparable level of contributions to the P2P system. The admission system uses a distributed eigenvector-based method to compute user reputations, a sampling heuristic to reduce computation and communication costs, and a distributed trust-enhancement scheme to provide system security.

/pdf/differentiated-admission-for-peer-to-peer-systems-5djj52ilyl.pdf

Differentiated admission for peer-to-peer systems: incentivizing peers to contribute their resources

We propose to support symmetric bidirectional route optimization in mobile IPv4 considering ingress-filtering routers. We developed subnet-based direct tunneling techniques to improve the routing efficiency for mobile IP and a binding optimization technique to reduce the handoff latency for mobile nodes. An enhanced correspondent agent was introduced to collaborate with the home agent and the foreign agent to support these techniques. Utilizing a tunneling cache, datagrams sent from a mobile node can be directly tunneled to a correspondent agent, including binding update messages. The proposed, approach can maximize the use of a bi-directional tunnel without abandoning the transparency to existing hosts. Learning from our deployment experience, the enhanced mobile IP protocol is suitable for wireless LANs that provide broadband multimedia and location-based services.

https://www.csie.nuk.edu.tw/~wuch/publications/2002-vtc-biro-revised.pdf

Bi-directional route optimization in mobile IP over wireless LAN

Advanced Traveler Information Systems (ATIS) plays an important role in intelligent transportation systems; it assists travelers with pre-trip and en route travel information to improve the convenience, safety and efficiency of travel In this paper we present a highway ATIS system which provides more decisive and valuable travel information than conventional ATIS systems by integrating it with newly emerging Web service and wireless communication technologies Following the Web service standards, our system can aggregate multiple travel-related data from different sources and provide value-added services to application developers or users Through multimedia messaging services or wireless LAN communication, we can support access of the system to mobile travelers with visualized real-time traffic information for pre-trip planning or en route referencing In order to make our system more reliable and useful, the methods of lost data reconstruction and travel time prediction are also proposed and examined

/pdf/an-advanced-traveler-information-system-with-emerging-sis3n8zsgg.pdf

Chun-Hsin Wu

Papers

Travel-time prediction with support vector regression

Travel time prediction with support vector regression

Differentiated admission for peer-to-peer systems: incentivizing peers to contribute their resources

Bi-directional route optimization in mobile IP over wireless LAN

An Advanced Traveler Information System with Emerging Network Technologies