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.     

大型LNG船舶系泊条件试验研究

以某开敞式LNG接收站码头工程为例,通过物理模型,采用国内先进的仪器设备和试验手段,包括国内第一台可吸收式L型造波机等,模拟了风、浪、流对系泊船舶的共同作用,分析探讨了大型LNG船舶系缆力和撞击力对风浪流三种环境荷载不同方向的响应。通过优化系缆墩的布置和系缆方式,达到了LNG船舶安全系泊要求,为工程设计和运营提供了依据。     

基于800/900MHz频段的组合式电子收费车道设计

根据目前国内高速公路联网收费实际和技术条件,结合重庆市电子车牌项目作为省级区域国家级信息化试点的要求,提出了将基于800/900 MHz频段的组合式电子收费车道作为ETC(Electronic Toll Collection,电子不停车收费)快速通行手段的设计方案。该方案与目前MTC(Manual Toll Collection,人工半自动收费)的技术条件和实际环境兼容,并且投资成本低,具有较高的实用性和可操作性。     

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.     

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.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

Women’s mode and trip structure choices in daily activity-travel: a developing country perspective

ABSTRACT

In this paper, we analyze the travel patterns of Iranian women, where typical patriarchal views and specific social and cultural norms may differ from the patterns of those in western societies. In addition to inherent psycho-physical gender differences, women in Iran can face special constraints forcing them not to be involved in all activity-travel patterns that people in developed countries usually undertake. We pay special attention to the role of marital and employment status on women’s activity-travel patterns. To this end, we develop a joint mode and daily activity pattern (DAP) discrete choice model, which is a two-level mixed nested Logit. The upper nest of the proposed model embodies women’s DAP choices, and the lower nest belongs to the mode choices. In this paper, we try to show how different factors in a patriarchal Muslim society like Iran affect or restrict women’s type and structure of activity-travel patterns.     

Influence of some vehicle and track parameters on the environmental vibrations induced by railway traffic

A study is performed on the influence of some typical railway vehicle and track parameters on the level of ground vibrations induced in the neighbourhood. The results are obtained from a previously validated simulation framework considering in a first step the vehicle/track subsystem and, in a second step, the response of the soil to the forces resulting from the first analysis. The vehicle is reduced to a simple vertical 3-dof model, corresponding to the superposition of the wheelset, the bogie and the car body. The rail is modelled as a succession of beam elements elastically supported by the sleepers, lying themselves on a flexible foundation representing the ballast and the subgrade. The connection between the wheels and the rails is realised through a non-linear Hertzian contact. The soil motion is obtained from a finite/infinite element model. The investigated vehicle parameters are its type (urban, high speed, freight, etc.) and its speed. For the track, the rail flexural stiffness, the railpad stiffness, the spacing between sleepers and the rail and sleeper masses are considered. In all cases, the parameter value range is defined from a bibliographic browsing. At the end, the paper proposes a table summarising the influence of each studied parameter on three indicators: the vehicle acceleration, the rail velocity and the soil velocity. It namely turns out that the vehicle has a serious influence on the vibration level and should be considered in prediction models.     

Use of a probabilistic particle tracking model to simulate the Prestige oil spill

A probabilistic particle tracking model is used to simulate the oil dispersion after the Prestige wreckage. This oil spill constitutes a suitable benchmark to analyze the capabilities of a probabilistic model, since the time elapsed from wreckage to oil landing (12 days) is much longer than the reliability time associated with forecast winds, usually on the order of 3–4 days. The particle model can be run in two different modes: real time mode (when existing reliable wind fields for the event under scope) and in probabilistic mode (in absence of reliable wind fields but with historical fields corresponding to a similar period). The validity of the particle model is first evaluated in a hindcast way, running the Prestige case with the wind fields corresponding to the period November 19 to November 30, 2002, which were not available at the moment of the wreckage. Calculations show the accuracy of the model to provide the right impact point and timing. The probabilistic model is then used to simulate the same event by means of historical data. The region where the oil landed is shown to be the area with the highest probability to be impacted.