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.     

Intermodal Transit System Coordination

In urban areas where transit demand is widely spread, passengers may be served by an intermodal transit system, consisting of a rail transit line (or a bus rapid transit route) and a number of feeder routes connecting at different transfer stations. In such a system, passengers may need one or more transfers to complete their journey. Therefore, scheduling vehicles operating in the system with special attention to reduce transfer time can contribute significantly to service quality improvements. Schedule synchronization may significantly reduce transfer delays at transfer stations where various routes interconnect. Since vehicle arrivals are stochastic, slack time allowances in vehicle schedules may be desirable to reduce the probability of missed connections. An objective total cost function, including supplier and user costs, is formulated for optimizing the coordination of a general intermodal transit network. A four-stage procedure is developed for determining the optimal coordination status among routes at every transfer station. Considering stochastic feeder vehicle arrivals at transfer stations, the slack times of coordinated routes are optimized, by balancing the savings from transfer delays and additional cost from slack delays and operating costs. The model thus developed is used to optimize the coordination of an intermodal transit network, while the impact of a range of factors on coordination (e.g., demand, standard deviation of vehicle arrival times, etc) is examined.     

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.     

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.     

用模糊逻辑实现动力定位船舶的艏向寻优

由于各种环境因素随时间不断变化，船舶作为动力定位作业时，最优艏向也是不断变化的，所以艏向寻优是动力定位的关键技术之一，本文用模糊逻辑实现艏向寻优，其优点是，不依赖于环境要素的测量值和数学模型，寻优与控制相结合，反应快，准确程度高。