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.     

港口腹地划分的断-电模型研究

如何准确地划分港口的腹地范围,以明确港口的定位和规划布局很重要。现有的港口腹地划分方法大多只以某一个单一因素作为港口腹地划分的依据,只能实现对港口腹地的笼统划分,不能满足指导港口制定未来发展目标与规划的任务。在总结了以往的腹地划分方法的基础上,提出了一种新的港口腹地划分方法——断-电模型。该模型是基于城市腹地划分的断裂点模型和电子云模型综合而成的,是在考虑到港口及腹地地区综合影响因素的基础上建立的,能够较为准确地划分港口的腹地范围。最后以营口港的腹地划分为例对断-电模型进行了实际应用,并取得满意效果。     

城市轨道交通融合云平台探讨

针对现有条件下城市轨道交通系统存在的信息孤岛、设备分散、网络资源浪费、安全管控弱、运维体系失调等问题,结合云计算在城轨行业应用的政治环境、政府响应、技术环境、现象后的本质、内部推动力等方面,提出城市轨道交通融合云平台的方案。该方案基于线网化IT资源建设,为ISCS、ACC、CCTV、PIS等业务提供基于IaaS层服务,并伴有车站降级运行和异地热迁移等策略,提高了系统的可靠性和可用性,解决了IT基础设施统一化、线网化的相关问题。论述主要业务系统的部署要点和建议,如综合监控系统的主备切换、AFC的扁平化层级、CCTV的带宽计算、PIS系统软件的可移植性等方面。     

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.     

四边固定支承矩形薄板振动分析的有限积分变换法

利用双重有限余弦积分变换的方法推导出了四边固定支承条件下,矩形薄板的固有频率和振型的解析解表达式.由于在求解过程中不需要事先人为地选取挠度函数,而是从弹性薄板的基本振动方程出发,直接利用数学的方法求出可以完全满足四边固定支承的边界条件,弹性矩形薄板的固有频率和振型解析解,使得问题的求解更加合理化.最后,还给出了计算实例来验证文中所采用的方法以及所推导出的公式的正确性.