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In recent years, it has been witnessed a boom in the development of mobile networks and a great increase in the computing ability of mobile devices. The rapid booming in client requests lead to some new challenges for real-time on-demand data broadcasting: (1) the dynamic diversity of the data characteristics; (2) the dynamic diversity of real-time clients' demand greatly increase the volume of hot-spot data (the most access data); and (3) the clients' demands for high service quality. To date, the current research has focused on the fixed-channel models (i.e. the bandwidth and number of channels are unchangeable) and algorithms. To adapt to the characteristics of the real-time requests, an optimized channel split method (OCSM) is proposed for automatic channel split and data allocation in this paper. The experiments undertaken in this study included two aspects: (1) determining the different strategies under different data sizes and deadlines; and (2) verifying the validity of the automatic channel split and data allocation through a series of experiments with the general performance matrics. The results show that the proposed method outperforms some of the state-of-the-art scheduling algorithms. [ABSTRACT FROM AUTHOR]

The urban traffic signal control system is complex, non-linear and non-equilibrium in real conditions. The existing methods could not satisfy the requirement of real-time and dynamic control. In order to solve these difficulties and challenges, this paper proposes a novel Multi-Intersection Model (MIM) based on Cellular Automata (CA) and a Multi-Intersection Signal Timing Plan Algorithm (MISTPA), which can reduce the delay time at each intersection and effectively alleviate the traffic pressure on each intersection in the urban traffic network. Our work is divided into several parts: (1) a multi-intersection model based on CA is defined to build the dynamic urban traffic network; (2) MISTPA is proposed, which truly reflects the real-time demand degree to green time of the traffic flow at each intersection. The MISTPA is composed Single Intersection Volume Algorithm (SIVA), Single-Lane Volume Algorithm (SLVA) and single intersection signal timing plan algorithm (SISTPA). Extensive experiments show that when the saturation is greater than 0.3, the MIM and the MISTPA achieve good performance, and can significantly reduce the vehicle delay time at each intersection. The average delay time of the traffic flow at each intersection can obviously be reduced. Finally, a practical case study demonstrates that the proposed model and the corresponding algorithm are correct and effective. [ABSTRACT FROM AUTHOR]

Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results. [ABSTRACT FROM AUTHOR]

Compared with conventional data broadcasting, on-demand data broadcasting is adaptive and real-time, which can better reflect the actual needs of mobile users. Current researches do not consider the attribute of data item size, and the constantly changing characteristics of data item size in on-demand data broadcasting is non-ignorable. This paper introduces the split strategies and backpacks theories into on-demand data broadcasting scheduling to deal with the inconsistencies of data item size, and proposes two scheduling models under different split strategies: (1) equal split scheduling model ES-LxRxW, which proposes the equal splitting strategy (ES) and a deadline adjust strategy. (2) Unequal split scheduling model US-LxRxW, which proposes the unequal split strategy (US) and two effective scheduling algorithms priority first (PF) and propriety and bandwidth first (PxBF). Extensive experiments shows that ES-LxRxW and US-LxRxW can both improve bandwidth utilization and dynamically adjust to the real-time situation of data item size, which takes into account data item size, bandwidth, cycle and scheduling priority of data item. The two proposed scheduling models could reach or outperform the other state-of-the-art scheduling algorithms without considering data item size in the performance of request drop ratio. US-LxRxW can also better reflect the real-time changes of data items than ES-LxRxW, and the proposed PF and PxBF algorithms can effectively improve the bandwidth utilization and reduce the split times. [ABSTRACT FROM AUTHOR]

Block allocation problem (BAP) is the key part of container terminal management. To make full use of the limited yard resources and to reduce the turnaround and transportation time, this paper focuses on the objectives of BAP offering multiple work ways, avoiding loading or collecting containers at the same time, and minimising the total distance between the berths and the blocks. The solution of BAP is similar to the model that individuals choose proper islands to live in the unified neutral theory of biodiversity and biogeography (UNTBB). The difficulty lies on that the UNTBB model is a complicated stochastic process. A novel algorithm based on the modified UNTBB model is proposed. Based on this modified UNTBB model, this paper proposed a grouping strategy to reduce the solution set. To guarantee the consecutive container groups not to put in the same block and to assure a consecutive and parallel loading process, a strategy that kills any one of the species existing prey relationship is put forward. Moreover, a greedy strategy is constructed to select an island to accelerate the ecological balance among islands. All these strategies are included into UNTBB to make ecological selection more instructive and faster. The optimal solution in the modified UNTBB model is the optimisation result of the BAP. The experiments show that the novel algorithm proposed in this paper achieves a better performance than the other conventional methods. [ABSTRACT FROM PUBLISHER]