Financial and environmental impacts of hypothetical Finnish dry port structure

One main theme of European Union’s in transport policy statements has been the increased role of railways in the reducing environmental impacts and costs of transport activity. One option to increase the modal share of rail transport is to utilize the dry port concept, particularly applicable to general cargo. At the Port of Gothenburg (Sweden) use of this concept in combination with rail transport has led to a reduction of CO₂ emissions, and lower transport energy costs. The main objective and motivation of this research work are to examine through analytical models, how this same dry port concept could be implemented in the Finnish transportation network, with estimates of the benefits being gained.The research method of this study is macro gravitational models of distribution. Main input data for the models are distances and population in the area. The approach aims to research, how relative transport costs behave by increasing the number of dry port distribution locations. For the actual computation work the authors apply linear integer programming. Based on the results, the authors argue that relative transport costs can decrease considerably by increasing the number of dry ports, up to the level of six locations. This is considerably less than what is the current situation in Sweden. The found solution also differs from Sweden as the fragmented Finnish seaport system enables using numerous seaports instead of one, which further decreases inland transportation distances and volumes considerably. At the same time forthcoming sulphur emission reduction regulation (for sea transports) might impact the transportation network structure by decreasing sea transport and the number of seaports used. This might lead to a further increase in land-based hinterland transport.     

Changing accessibility, dwelling price and the spatial distribution of socio-economic activities

In this paper we present an activities-location choice model with endogenous price which simulates, based on Expected Random Utility principle, the behaviour of several agents of the urban system (e.g. the workers distinguished by income, the firms by economic sector) to estimate the spatial distribution of socio-economic activities within the study area as well as the impact of differential changes in accessibility on the dwelling price. The study area for this research is the metropolitan area of Napoli (South Italy), for which we show the results of the model estimation and the results of a “backcasting” analysis.     

混合语言编程在船舶水动力计算中的应用

针对VC++具有面向对象特征和开发图形界面方面的强大功能以及FORTRAN语言在数值计算方面的优势,着重介绍了VC++和FORTRAN二种语言混合编程需要的技术要点和实现方法.对混合编程规则中的调用约定、命名约定、数据的交换与传递、多维数组的传递、DLL的调用方式以及DLL的调试进行了详细的阐述,并通过两者的混合编程实现了对船舶水动力的计算.该计算精确而便捷,对船舶操纵预报、安全评估等方面的研究有很大的应用价值.     

基于神经网络和混合编程的整流桥故障诊断研究

本文介绍了采用VC＋＋和MATLAB混合编程实现对三相整流桥进行故障诊断的方法。该方法通过VC＋＋调用MATLAB引擎来驱动神经网络,将神经网络、数据采集及人机接口结合起来,对三相整流桥的故障进行在线诊断。试验证明,经过足够的样本训练,采用三层BP神经网络能达到一定的准确度。     

Energy savings in transport

A decade of increasing Federal attention to urban transportation needs has culminated in the 1970 Urban Mass Transportation Assistance Act. This Act is intended to provide 10 thousand million dollars over the next 12 years in Federal assistance money to urban public transportation systems. This paper examines the needs of selected U.S. cities as a basis for (1) understanding the vast, various and complex transportation needs of urban areas throughout the country, and (2) assessing the sufficiency of these funds. The sample cities have been placed into three broad categories based on the state of development of their transportation systems. In Category I cities, the essential need is to ensure the survival of bus systems for the use of non‐drivers, or to provide some other viable alternative to the automobile; in Category II cities, the primary needs are to relieve auto congestion and to improve public transportation components, while in Category III cities, the primary need is massive investment to improve and to extend public transportation facilities. It is concluded that the presently intended Federal funding level for transportation will not meet the financial requirements of the Category III cities.     

High-speed train control based on multiple-model adaptive control with second-level adaptation

Speed uplift has become the leading trend for the development of current railway traffic. Ideally, under the high-speed transportation infrastructure, trains run at specified positions with designated speeds at appointed times. In view of the faster adaptation ability of multiple-model adaptive control with second-level adaptation (MMAC-SLA), we propose one type of MMAC-SLA for a class of nonlinear systems such as cascaded vehicles. By using an input decomposition technique, the corresponding stability proof is solved for the proposed MMAC-SLA, which synthesises the control signals from the weighted multiple models. The control strategy is utilised to challenge the position and speed tracking of high-speed trains with uncertain parameters. The simulation results demonstrate that the proposed MMAC-SLA can achieve small tracking errors with moderate in-train forces incurred under the control of flattening input signals with practical enforceability. This study also provides a new idea for the control of in-train forces by tracking the positions and speeds of cars while considering power constraints.     

Mathematical properties and constraints representation for bottom-up approaches to the evaluation of GHG mitigation policies

Bottom-up models, including MARKAL, MESSAGE and AIM, are widely used when analyzing the effect of greenhouse gas (GHG) abatement policies. These bottom-up models are mostly formulated as a linear programming (LP) optimization model to find both the minimal cost combination of abatement technologies and energy flows while satisfying demands. It is not unusual that the bottom-up modeling involves a great number of technical, industrial, socioeconomic and environmental constraints. Investigating representative constraints needed for analyzing GHG abatement policies, this study proposes how to implement these constraints in bottom-up modeling.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Real-time automatic rescheduling strategy for an urban rail line by integrating the information of fault handling

In the operation of urban rails, faults are inevitable, which leads to deviation between the actual timetable and the planned timetable. In nowadays, timetable rescheduling strategies rarely integrate the information of fault handling. In this paper, we develop a real-time automatic rescheduling strategy, which integrates the dynamic information of fault handling. The rescheduled timetable is obtained by a mathematical optimization model, the constraints set of which is automatically generated and adjusted as more information of fault handling is feedback. Compared with the experience-based rescheduling methods, the automatic rescheduling strategy reacts more quickly, and uses the information of fault handling more efficiently. A simulation system for testing the automatic rescheduling strategy is built, which uses the data of the Beijing Yizhuang metro line. Via testing on the simulation system, the effectiveness and efficiency of the automatic rescheduling strategy are validated.     

A mathematical programming technique for matching time-stamped records in logistics and transportation systems

Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications.