Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization

Travel surveys based on global positioning system (GPS) data have exponentially increased over the past decades. Trip characteristics, including trip ends, travel modes, and trip purposes need to be detected from GPS data. Compared with other trip characteristics, studies on trip purpose detection are limited. These studies struggle with three types of limitations, namely, data validation, classification approach-related issues, and result comparison under multiple scenarios. Therefore, we attempt to obtain full understanding and improve these three aspects when detecting trip purposes in the current study. First, a smartphone-based travel survey is employed to collect GPS data, and a surveyor-intervened prompted recall survey is used to validate trip characteristics automatically detected from the GPS data. Second, artificial neural networks combined with particle swarm optimization are used to detect trip purposes from the GPS data. Third, four scenarios are constructed by employing two methods for land-use type coding, i.e., polygon-based information and point of interest, and two methods for selecting training dataset, i.e., equal proportion selection and equal number selection. The accuracy of trip purpose detection is then compared under these scenarios. The highest accuracies of 97.22% for the training dataset and 96.53% for the test dataset are achieved under the scenario of polygon-based information and equal proportion selection by comparing the detected and validated trip purposes. Promising results indicate that a smartphone-based travel survey can complement conventional travel surveys.     

基于最小浮动车样本数量的道路覆盖率与交通信息更新周期研究

基于浮动车样本数量的道路覆盖率与交通信息更新周期的关系是浮动车信息采集技术应用中亟待解决的关键问题。浮动车的道路覆盖率与交通信息更新周期之间成正比关系,通过对基于经典概率统计理论的传统计算最小浮动车样本数量方法中所存在不足的分析,提出从路段内车辆运行时间角度出发考虑样本量,引入表征运行时间变化的变量,确定满足道路覆盖率的最小浮动车样本数量,进而建立基于浮动车样本数量的道路覆盖率与交通信息更新周期的关系模型,并给出满足道路覆盖率的浮动车最小样本数量计算的改进算法。通过成渝高速公路成都—龙泉段的实际浮动车数据,验证了关系模型的可靠性及改进算法的优越性。     

散道床基础的驼峰电动减速器换装施工方案优化

介绍了散道床基础的驼峰电动减速器换装施工的方案优化;分析了驼峰电动减速器的组成和散道床基础的成因;从吊装前的准备、吊装、安装调试、轧道及溜放试验4个过程,阐述了散道床基础的驼峰电动减速器换装的施工方案;指出了电务、工务、车务、施工车辆在换装施工中应注意的事项;并从吊装、试溜、减速器安装、基本轨除锈、人身安全防护5个方面对施工方案进行了优化。     

怠速开空调车内共振问题的优化

汽车怠速工况车内噪声振动情况是影响整车NVH（噪声、振动和平顺性）水平的重要因素且影响乘车舒适性。以某款车型为例,对车内噪声源及传递路径进行分析,通过对悬置和冷却风扇等系统进行试验分析,确定了问题产生的主要原因,并提出了相应的优化方案,提出为保证悬置隔振和制冷效果,需对悬置系统和风扇转速合理匹配,同时提高转向柱的固有频率。验证表明车内轰鸣声消除,噪声及振动明显减小,效果良好,为解决同类问题提供了方法和思路。     

基于捕食搜索策略的粒子群算法求解高铁闭塞分区划分问题

高铁闭塞分区的合理划分可以保证列车的运行安全、提高运输效率和减少投资成本。为了更好地解决这个问题,利用基于捕食搜索策略的粒子群算法求解优化准移动闭塞条件下的闭塞分区划分模型。捕食搜索策略可以平衡粒子的局域搜索和全局搜索,从而避免陷入局部最优,提高算法精度。通过算例仿真,比较基于捕食搜索策略的粒子群算法和标准粒子群算法对模型优化的结果,验证基于捕食搜索策略的粒子算法对模型的求解是有效的,而且得到的解更精确,运算速度更快。     

基于单分类算法OSVM船用燃气轮机状态评估

针对船舶综合监控系统中存储有海量设备正常运行时的数据没有得到充分利用，此外设备退化时的故障数据难以获取，无法训练传统的多分类退化检测模型，提出利用单分类算法OSVM来建立模型,从而实现退化检测，在该过程中只需用正常样本数据来训练模型，并在一个经过实船数据验证过的模拟器产生的数据集上进行了实验。结果显示，只需要400个正常样本就可训练出准确的退化检测模型，该模型在精确度，召回率，特异性，正确率，AUC五个指标都有很好表现，此外，该退化检测模型有很好的扩展性，也可被用于其他机械设备的状态评估中。     

Optimization of mooring systems in the context of an integrated design methodology

This work describes an enhanced mooring optimization procedure, oriented towards recent floating production systems (FPS) for oil & gas exploitation in ultra-deep-water scenarios, which may present a large number of risers in an asymmetric layout. Acknowledging that the risers are the key component of an FPS, the optimization procedure is associated to an integrated mooring-riser design methodology; thus, instead of simply minimizing the platform offsets and/or the costs of the mooring system itself, one of the main objectives is to obtain a mooring configuration that ensures the integrity of the risers. Other highlights of the optimization procedure include the following aspects: Enhancements in the modeling of the optimization problem (including the definition of design variables, objective function and constraints that are relevant for such actual applications); The use of the PSO optimization algorithm associated to the ε-constrained method to efficiently handle the constraints; Enhancements in the evaluation of candidate solutions, by full nonlinear time-domain dynamic Finite Element simulations with coupled models; and the implementation in a parallel computing environment to deal with the high associated computational costs. A case study considering an FPS representative of actual applications in deepwater scenarios is presented to illustrate the practical application of the optimization tool.     

Locating inspection facilities in traffic networks: an artificial intelligence approach

Abstract

In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? and (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.     

Prospects for computer aided railway scheduling: perspectives from users and parallels from mass transit

Railway scheduling faces new challenges as competition and, in many parts of the world, privatisation require ever better results, both in terms of minimising the resources used and maximising the performance achieved by those resources. It is therefore surprising that in practice railway scheduling tasks appear to be performed largely without the use of optimizing models. This paper takes two different perspectives, a case study of user needs from the UK railway industry and a comparative analysis with mass transit scheduling systems, to consider why this might be and assesses what the future prospects are for computer aided railway scheduling.

A number of conclusions are drawn: that optimization models need to be integrated with software packages that meet schedulers' data management needs; that researchers need to work with commercial developers to achieve this integration; that there needs to be an emphasis on extending models to cover more real‐life circumstances; and that solutions with international applicability should be sought.     

Single track line capacity model

The objective of the research described in this paper was to develop a model for computation of an ultimate capacity of a single track line and to provide a sensitivity analysis of this capacity to the parameters which influence it. The model is based in a concept of mathematical expectation of capacity and can be applied under saturation conditions i.e. a constant demand for service. It can serve for planning purposes, computation of single track line capacity on the base of which estimations are possible concerning a single track line performance under given conditions, as well as commercial time‐tables planning, decisions about a partial or complete construction of the second parallel track along the line in service, intermediate stations locations planning and the necessary facilities along the line under construction.

In the sensitivity analysis, the model allows a change of parameters upon which the capacity depends. These are: the length of the line segment which is considered to be bottleneck for calculation of capacity, traffic distributions per directions, train mix, train velocities and train spacing rules applied by the dispatching service when regulating the traffic on a line.