In recent years, free-floating bike-sharing systems (FFBSSs) have been considerably developed in China. As there is no requirement to construct bike stations, this system can substantially reduce the cost when compared to the traditional bike-sharing systems. However, FFBSSs have also become a critical cause of parking disorder, especially during the morning and evening rush hours. To address this issue, the local governments stipulated that FFBSSs are required to deploy virtual stations near public transit stations and major establishments. Therefore, the location assignment of virtual stations is sufficiently considered in the FFBSSs, which is required to solve the parking disorder and satisfy the user demand, simultaneously. The purpose of this study is to optimize the location assignment of virtual stations that can meet the growing demand of users by analyzing the usage data of their shared bikes. This optimization problem is generally formulated as a mixed-integer linear programming (MILP) model to maximize the user demand. As an alternative solution, this article proposes a clustering algorithm, which can solve this problem in real time. The experimental results demonstrate that the MILP model and the proposed method are superior to the K-means method. Our method not only provides a solution for maximizing the user demand but also gives an optimized design scheme of the FFBSSs that represents the characteristics of virtual stations.
In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid development of sensor technologies enables multisource data collection to improve the modeling of the SFC problem. To address the features of such heterogeneous data, this paper proposes an integrated model for the estimation of SFC that includes three modules: a multisource data collection module, a heterogeneous data feature fusion module and a fuel consumption estimation module. First, in the data collection module, data related to SFC are collected by multiple sensors installed aboard the ship. Second, the feature fusion module employs a series of moving overlapped frames to merge different frequency data into small frames so that fusion features can be extracted from the heterogeneous data of multiple sources. Finally, in the fuel estimation module, the fusion features provide a novel way to consider the modeling and estimation of SFC as a classical time-series analysis using various machine learning techniques. Experimentally, linear regression (LR), support vector regression (SVR), and artificial neural network (ANN) were employed as the machine learning methods to train SFC models. Compared with the traditional feature extraction method, the accuracy of LR, SVR, and ANN were improved by 8.5, 0.35 and 51.5%, respectively, using the proposed method. The main contribution of this work is to consider the multisource and heterogeneous problem of sensor-based SFC data and propose an integrated model to extract the information of SFC data. Moreover, the experimental results showed that the estimation accuracy can be greatly improved.
In a heterogeneous wireless environment, mobility particularly vertical handover between WiFi and cellular data service has to be supported. The support for multihoming feature makes Stream Control Transmission Protocol (SCTP) become a promising transport layer-based handover scheme for end users. Although the benefits of applying SCTP for multimedia delivery and handover have been proved to be extremely useful, there are still many challenges that remain: (i) current sender-centric SCTP handover management solutions fail to balance the computational overhead between a sender and receiver and (ii) they also suffer from performance degradation due to the rigid three-duplicated-SACK-based loss detection and recovery strategy. This paper proposes SCTP-Rev + , a new receiver-assisted cellular/WiFi handover management mechanism for SCTP-based multimedia transport aiming to (i) optimize SCTP handover management and achieve overhead balancing between the sender and receiver, by moving path estimation and handover decision operations from the sender onto receiver and (ii) improve SCTP loss recovery ability and multimedia delivery performance, by providing SCTP with a simple but effective retransmission-aware loss detection/recovery mechanism. We show that the proposed solution outperforms the current SCTP schemes in terms of goodput performance and multimedia delivery quality.
Abstract:The sustainable development of an economic-energy-environment (3E) system has received increasing attention by the government because it both determines national development and individuals' health at the macro and micro level. In this paper, we synthetically consider various important factors based on analysis of the existing literature and use system dynamics (SD) to establish models of sustainable development of a 3E system. The model not only clearly shows the complex logical relationship between the factors but also reveals the process of the 3E system. In addition, the paper provides a case study of the Beijing-Tianjin-Hebei region in China by using a scenario analysis method. The models proposed in this paper can facilitate an understanding of the sustainable development pattern of a 3E coordination system and help to provide references for policy-making institutions. The results show that the long-term development of the Beijing-Tianjin-Hebei region's 3E system is not sustainable, but it can be changed through the adjustment of the energy structure and an increase in investment in environmental protection, which can improve the environmental quality and ensure continuous growth rather than excessive growth of energy consumption and the gross domestic product (GDP).
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