Abstract:With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.

Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.

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Information and communication technologies have improved the quality of Intelligent Transportation Systems (ITS). The real-time traffic information has traditionally been collected via stationary vehicle detectors or GPS-based probe cars. Compared to the traditional ways, estimating traffic information from Cellular Floating Vehicle Data (CFVD) is more cost-effective, and easier to acquire. In this paper, this study proposes a novel approach to evaluate the relation of call arrival, handover, traffic flow, and traffic density. Moreover, the traffic speed is estimated by the proposed approach according to CFVD. Through the analytical analysis, this study analyzes the effects of traffic information (e.g. traffic flow and vehicle speed) and communication behaviors (e.g. call arrival rate and call holding time) on handovers and call arrivals. In the simulation, this study compares the estimated traffic information with the real traffic information. The experiment results show that the accuracy of traffic speed estimation is 89.75%. Therefore, the proposed approach can be used to estimate traffic speed from CFVD for ITS.

The growing demand of real-time services, such as video streaming and VoIP with high constraints on delays and bandwidth brings new challenges in the design of wireless communication system. So how to use radio resource efficiently becomes an important point for the LTE system. In our paper, we propose a new LTE downlink packet scheduler, which adopts the packet delay to prioritize packet transmissions and uses packet prediction mechanism to solve the burst transmission situation. We also use the channel quality indicator that users report to the eNodeB to classify users' mobility, and then we utilize different resource allocation schemes for uses with different mobility in every transmission time interval. The simulations are conducted by LTE-Sim R5 to analyze the performance of proposed method. And the simulation results show that the proposed method outperforms other packet scheduling schemes in terms of goodput, packet delays, and packet loss rates. Our proposed method can improve the spectrum efficiency and satisfy the QoS requirements of real-time traffic.