Fast Ferry Smoothing Motion via Intelligent PD Controller

A novel type of control law was adopted to reduce the vertical acceleration of a fast ferry as well as the motion sickness incidence suffered by the passengers onboard by means of a submerged T-foil. Considering the system changing characteristics under high disturbances, a model-free approach was adopted. In addition, an upgraded proportional-derivative (PD) controller with correction terms resulting from a fast-online estimation of the system dynamics was designed. The overall controller, known as intelligent PD (i-PD) controller, was tested, and the obtained results were compared with those of a classic PD controller. The controllers were also tested in a changing environment and at different operating velocities. The results confirmed the effectiveness of the i-PD controller to smooth the motions with low computational cost control schemes. Furthermore, thanks to ability of the i-PD controller to continually update the estimated dynamics of the system, it showed a better reduction in both vertical motions and the seasickness level of the passengers with the needed robustness under external disturbances and system changing parameters.     

Full Duplex Communications for Next Generation Cellular Networks: The Capacity Gain

Full duplex communication highly improves spectrum efficiency of a wireless communication link. However, when it is applied to a cellular network, the capacity gain from this technology remains unknown. The reason is that full duplex communication changes the aggregate interference experienced by each communication link in cellular networks. In this paper, the capacity gain from full duplex communication is studied for cellular networks of 4G and beyond, where the same frequency channel is adopted in each cell. A two-layer Poisson point process (PPP) is adopted to model the network topology, and stochastic geometry is employed to derive the coverage probability and the average capacity of typical link in a cellular network. On the basis of these derived parameters, the capacity gain from full duplex communication is determined. Numerical results reveal that without mutual interference cancellation (MIC), the capacity gain is small under various power levels; with perfect MIC at base stations, the capacity gain can exceed 60%; with imperfect MIC at base stations, the capacity gain decreases quickly even with a slight drop of MIC performance.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

Isolating high-priority metro and feeder bus transfers using smart card data

Fixed-rail metro (or ‘subway’) infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service’s operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers’ transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.     

铁路信号安全关键软件的组合测试序列集约简

针对现有铁路信号系统安全关键软件t-路组合测试序列生成方法在处理大规模输入时产生冗余测试序列，导致测试执行成本较高的问题，提出了一种基于贪婪-粒子群混合优化算法的t-路组合测试序列集约简方法，用于降低序列集的执行成本. 首先，以执行成本最低为约简目标，建立针对t-路组合测试序列集约简的优化模型；然后，在保证测试序列集逻辑覆盖特性的基础上，采用贪婪-粒子群混合优化算法求解模型，计算约简后的测试序列集；最后，以ZPW-2000轨道电路接收器软件作为研究对象，以其生成的2-路组合测试序列集为例开展约简验证. 结果表明，所提方法在保证2-路组合覆盖和逻辑覆盖的前提下，对单个序列集的执行成本约简幅度最高达到98.33%，对序列集总的执行成本约简幅度达到36.10%，验证了所提方法的可行性和有效性.      

Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam,Tanzania

The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational power—for tracking movements, automatically detecting modes and activities, facilitating data collection, etc.. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a 1-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every 3 days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.     

Optimal Threshold Policies for Robust Data Center Control

With the simultaneous rise of energy costs and demand for cloud computing, efficient control of data centers becomes crucial. In the data center control problem, one needs to plan at every time step how many servers to switch on or off in order to meet stochastic job arrivals while trying to minimize electricity consumption. This problem becomes particularly challenging when servers can be of various types and jobs from different classes can only be served by certain types of server, as it is often the case in real data centers. We model this problem as a robust Markov decision process (i.e., the transition function is not assumed to be known precisely). We give sufficient conditions (which seem to be reasonable and satisfied in practice) guaranteeing that an optimal threshold policy exists. This property can then be exploited in the design of an efficient solving method, which we provide. Finally, we present some experimental results demonstrating the practicability of our approach and compare with a previous related approach based on model predictive control.     

基于云平台的铁路视频系统及安全技术研究

云技术的广泛应用是信息技术发展的新趋势,铁路视频监控系统无论从存储的可扩展性、安全性还是从集中管理、大数据收集的角度,都需要云技术的有力支撑,云技术的应用也会带来新的安全问题,如云数据安全风险、虚拟化安全风险、非法访问风险等。简要分析铁路视频监控系统面临的安全问题和相应的安全技术方案。