客运专线夜间行车与天窗的相互影响分析

客运专线天窗的开设影响夕发朝至列车的运行组织. 考虑高速列车可利用相邻既有线、城际铁路跨线运行,以综合利用运输通道运输能力满足旅客夜间出行需求,本文分析了客运专线开设全线垂直矩形天窗与夜间行车的相互影响,建立了求解最佳矩形天窗位置问题的数学模型,以天窗影响列车OD数最少为目标,以满足夕发朝至列车合理到发时间带为约束,并提出了求解算法. 结合京沪高速铁路实例,运用计算机编程求解,得出不同位置的天窗影响的列车OD数相差不大的结论,并给出动车组速度等级为350km/h和250km/h、速度系数0.9和0.8四种情况下长度为4小时的相对最佳矩形天窗位置. 对受天窗影响的列车提出了运行调整措施,初步分析了开行夕发朝至列车时动车组适宜采取的运行速度.     

基于旅游动机的旅游目的地选择行为分析

旅游目的地选择是游客出行决策的核心问题,本文对游客的目的地选择行为进 行了研究.本课题组选取北京市区8个景点,以在京旅游的游客作为研究对象,组织进行了 游客出行行为调查.问卷设计22 项旅游动机,通过对游客旅游动机项提取因子,并对旅游 动机的因子得分进行聚类分析把游客分为4 类.在构建游客旅游目的地选择行为模型时, 对首个目的地和非首个目的地分别建模.模型中引入了旅游动机类型变量,证明旅游动机 对游客目的地选择行为影响显著.另外出行距离在不同阶段对游客目的地选择的影响存 在显著的差异.该模型精度较高,为进一步研究旅游目的地选择行为提供了理论依据.     

大跨高低塔不对称斜拉桥抗震性能分析与评估

为了研究大跨斜拉桥的两阶段水准设防下的抗震性能,依托某大跨高低塔不对称斜拉桥,通过有限元模型建立了空间有限元模型,采用了反应谱和线性时程对比分析的方法。分析表明地震作用下,主桥的不利截面分别为双塔的塔底截面,其低塔受横向地震效应影响较大,高塔受纵向地震效应影响较大,设计配筋时应根据受力方向优化。     

黄骅港耙吸船施工周期与装舱时间研究

通过对耙吸船施工周期的研究以及施工参数的分析,确定最佳装舱时间,从而提高耙吸船生产率,降低施工成本。耙吸船最佳装舱时间的确定,使黄骅港综合港区20万吨级航道耙吸船施工生产率进一步提高,节约了施工成本。     

基于手机信令数据的游客识别与出行轨迹匹配方法

为统计旅游交通的客运量，分析公路交通运输需求，优化运输组织，合理构建“快进慢游”综合交通旅游网络，研究提出基于手机信令数据的游客识别与出行轨迹匹配方法。通过划定景区边界，设置停留时间阈值，识别手机用户的游客身份；同时，借助地图工具批量计算信令发生位置间的出行路线与行程时间，匹配实际公路网。实例分析结果表明，手机信令数据能呈现手机用户的出行轨迹，且信令数据的位置轨迹与公路线位走向结合密切。在人们越来越依赖地图导航软件规划出行路径的背景下，利用该方法能更准确地识别景区游客并估计游客往来景区的出行路径。     

基于有效绿灯时间利用率的自适应控制策略研究

为了优化单点交叉口信号控制方案,使其适应各个进口道方向交通流动态变化,提高交叉口通行效率,根据交叉口进口道排队车辆数建立有效绿灯利用率模型,提出了一种交叉口自适应控制策略.有效绿灯时间利用率模型以交叉口通行能力最大为控制参数,实时优化确定出最佳相位放行方案以及最优相位切换方案,根据进口道排队车辆最大流向的排队车辆数和车辆到达预测确定相位放行绿灯时间.利用VISSIM交通仿真软件对该自适应控制策略仿真运行,与定时控制以及感应控制对比,评价分析不同车辆到达情况下交叉口通行情况.结果表明:该自适应控制策略能有效降低车均延误,提高交叉口服务水平.     

浅析大客车气压制动装置制动失灵原因

介绍引起大客车制动失灵的一些故障,针对这些故障给出了预防措施和解决的方案。     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

Railway wheel-flat and rail surface defect modelling and analysis by time–frequency techniques

Damage to the surface of railway wheels and rails commonly occurs in most railways. If not detected, it can result in the rapid deterioration and possible failure of rolling stock and infrastructure components causing higher maintenance costs. This paper presents an investigation into the modelling and simulation of wheel-flat and rail surface defects. A simplified mathematical model was developed and a series of experiments were carried out on a roller rig. The time–frequency analysis is a useful tool for identifying the content of a signal in the frequency domain without losing information about its time domain characteristics. Because of this, it is widely used for dynamic system analysis and condition monitoring and has been used in this paper for the detection of wheel flats and rail surface defects. Three commonly used time–frequency analysis techniques: Short-Time Fourier Transform, Wigner–Ville transform and wavelet transform were investigated in this work.     

Strava Metro data for bicycle monitoring: a literature review

ABSTRACT

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.