多源数据融合驱动的居民出行特征分析方法

结合传统抽样调查数据和交通大数据，研究多源数据融合驱动的居民出行特征分析方法.根据传统入户抽样调查居民的年龄结构、职业、车辆拥有、人口，以及手机信令数据分析出行频次分布等因素进行综合分析，获取居民初步出行特征；基于手机信令、IC、AFC、GPS 等大数据，通过出行时间分布、OD分布和出行方式结构对居民的出行特征进行综合矫正分析；最后，以广州市为例进行实证分析.对比研究传统抽样调查和多元数据融合分析方法可知，传统抽样调查居民出行漏报率为30%，每天出行2次的比例相差39.5%，全方式非通勤出行比例、晚高峰公交和地铁出行比例分别相差7.4%、8.1%和12.6%.结果表明，多源数据融合驱动的居民出行特征分析方法，在总量上有效挖掘居民出行的沉默需求，在时空分布上起到了“削峰填谷”的作用，是一种研究居民出行特征的有效方法.     

Effects of the standoff distance on hull structure damage subjected to near-field underwater explosion

The near-field underwater explosion poses a great threaten to marine ships by introducing both local and overall damage on their hull structures. In this paper, a series of near-field underwater explosion experiments were carried out on the mimic steel hull structures to investigate the effects of the standoff distance on their dynamic behavior. Special attention was paid on their deformation process and failure modes. After that, a fluid-solid coupling algorithm was implemented in establishing a finite element (FE) model to simulate dynamic response of the hull structures under a near-field underwater explosion. The numerical results showed a good agreement with the experimental data. A standoff distance threshold was found to result in the maximum structural deformation. The deformation magnitude was monotonically decreased by either increasing or decreasing the standoff distance from the threshold value.     

Visual landscape classification in the coastal strip of Santander (Spain)

Abstract

The coastal landscape in Santander, Spain, is analyzed in terms of landscape quality and fragility (or vulnerability). Quality is intended to indicate those areas of major landscape value for conservation purposes. Fragility is utilized to detect areas which could easily deteriorate if certain human activities are carried out. The authors’ study has three clearly differentiated phases. In the first, landscape units are defined and mapped. In the second, units are classified by computerized techniques and grouped according to significance. In the third phase, the true significance of the groups is interpreted. A mountainous, coastal zone in northern Spain was chosen for this study. Landscape units were determined by visual criteria with strong topographical bias. The area covered by each unit is that visible to an observer situated approximately in its center. Each unit is represented by certain biophysical and visual variables. Data were subjected to clustering analysis on the basis of using three ad casum defined “distances.”; The first distance gives unit classifications grouped according to similarity or proximity of their characteristic variables. In this case, inventory data only are used, no subjective value judgments were introduced in the process. Value judgments are made at the end on the resulting groupings of units. Two other distance classifications are determined by the similarity or proximity of the units making up each group according to the landscape quality and fragility values of each unit. In these latter cases, classification is influenced by value judgments introduced at the beginning of the process. These procedures are more subjective but give more congruent results. With each mathematical distance configuration, an analysis is made of the variables shown to be most representative and which have, therefore, shown greatest discriminatory power in selecting final groups. Finally, the different possibilities of the above techniques are discussed as well as their potential generalization to other fields demanding the treatment of qualitative landscape variables.     

Estimating origin‐destination matrices from link volume counts: a simple intercity auto travel demand model for Sri Lanka

The cost of nation wide travel surveys is high. Hence in many developing countries, planners have found it difficult to develop intercity transportation plans due to the non availability of origin‐destination trip matrices. This paper will describe a method for the intercity auto travel estimation for Sri Lanka with link traffic volume data.

The paper outlines the rationale of selecting the district capitals of Sri Lanka as its “cities,” the methodology for selecting the intercity road network, determination of link travel times from express bus schedules and the location of link volume counting positions.

Initially, the total auto travel demand model is formulated with various trip purpose sub‐models. This model is finally modified to a simple demand model with district urban population and travel times between city pairs as the exogenous variables, to overcome statistical estimation difficulties. The final demand model has statistics within the acceptable regions.

The advantages of a simple model are discussed and possible extensions are proposed.     

Investigation of the use of smartphone applications for trip planning and travel outcomes

This paper explores the use of smartphone applications for trip planning and travel outcomes using data derived from a survey conducted in Halifax, Nova Scotia, in 2015. The study provides empirical evidence of relationships of smartphone use for trip planning (e.g. departure time, destination, mode choice, coordinating trips and performing tasks online) and resulting travel outcomes (e.g. vehicle kilometers traveled, social gathering, new place visits, and group trips) and associated factors. Several sets of factors such as socio-economic characteristics and travel characteristics are tested and interpreted. Results suggest that smartphone applications mostly influence younger individuals’ trip planning decisions. Transit pass owners are the frequent users of smartphone applications for trip planning. Findings suggest that transit pass owners commonly use smartphone applications for deciding departure times and mode choices. The study also identifies the limited impact of smartphone application use on reducing travel outcomes, such as vehicle kilometers traveled. The highest impact is in visiting new places (a 48.8% increase). The study essentially offers an original in-depth understanding of how smartphone applications are affecting everyday travel.     

多类信息对机场出站乘客交通方式选择的影响

在机场设置可变信息板(Variable Message Signboard, VMS)提供站台排队时间、道路拥堵状 况和车内拥挤度等信息，引导乘客在原有决策的基础上做出最优交通方式选择。根据出行信息 获取的顺序将决策过程分为两阶段，第1阶段是在自身经验或手机导航信息条件下进行初始交通 方式选择，第2阶段考虑到VMS信息对乘客的影响，建立基于巢式Logit模型的乘客交通方式转 移模型，利用全概率公式将两阶段信息数据融合，得到乘客交通方式选择结果。通过数值模拟验 证模型的可用性。结果发现：将信息质量足够高的等车时间信息、道路拥堵时间和车内拥挤度信 息结合有利于提高公共交通利用率和系统运行效率，可使公共交通利用率提高3%，系统运行时 间减少13.8%；道路拥堵越严重，路上拥堵时间信息的提供对乘客的交通方式选择影响越大；交通 需求较小的情况下提供VMS信息有利于提高机场巴士利用率。     

不同阶段的轨道交通定价模型研究

公共交通票价制定的合理与否对引导出行者出行方式选择,充分发挥各交通方式的效能具有重要作用。将出行者公交出行成本分为货币成本、时间成本和舒适性成本,确定各成本影响居民出行决策的权重。以轨道交通发展为轴线,基于城市轨道交通在不同发展阶段的差异性,研究不同阶段适用的定价模型,最后以南京市为例进行模型验证。     

基于出行特征的用地类型推断方法研究

提出一种基于卷积神经网络推测城市交通小区内用地特征的算法，同时对交通小区内多种用地类型进行预测.选用公共交通出行数据集和网约车出行数据集，融合多种出行方式的出行特征对交通小区内用地特征刻画.提取交通小区内发生强度，吸引强度和产吸差强度3个指标作为模型输入，训练得到基于区域内出行特征双通道的卷积神经网络模型，采用网格寻优方法确定最优网络结构.选取北京市六环内交通小区作为研究对象，结果表明，本文算法能够同时推断交通小区内居住、工作和休闲用地特征，并获得各用地类型在小区内占比分布.     

降雨量对OD行程时间可靠性影响分析

为详细研究降雨量对OD行程时间可靠性的影响，基于Uber 出行共享的3 年美国波士顿10 对OD行程时间数据及WeatherUnderground 网站提供的小时历史天气，构建了OD行程时间高斯混合模型(GMM). 模型参数利用EM法进行求解，K 值根据K-S 检验后的P 值 (大于0.500 0)进行确定，模型分位数利用二分法进行求解. 提出一种基于缓冲指数(BI)的新指标--缓冲指数变化率(BIVR)作为定量评估指标. 结果表明：降雨会降低总体OD行程时间可靠性，降低效果随降雨量提高而增强，但增强效果并不明显；尽管可能性较低，但当降雨处于次要影响因素时可能提高可靠性；小雨天气可视为正常天气；雨天可靠性显著低于正常天气，居民在雨天(除小雨外)出行应预留更多时间.     

基于贝叶斯网络的出行选择行为分析

以通勤出行者为研究对象,应用改进K2算法和贝叶斯参数估计方法,以模块化的建模思想,构造了分析通勤出行方式选择和出行链模式安排及其相互作用的贝叶斯网络模型.以互信息指标度量节点间相互依赖关系的强弱,完成网络的修剪.以修剪后的网络为基础,应用敏感性分析讨论了在出行者及其家庭的社会经济属性、活动和出行属性影响下的出行方式和出行链模式安排的变化,及其相互影响.本文的研究为全面分析活动-出行选择行为及其影响因素间的互动响应关系提供了新的思路.