沥青混合料转运车模型试验研究

在分析沥青混合料转运车的工作原理及作用的基础上,根据相似理论,设计了沥青混合料转运车相似模型,试验揭示了其结构参数对改善沥青混合料离析程度的影响,得出了材料离析率与螺旋轴转速、螺距及螺旋半径之间的关系,为沥青混合料转运车的设计开发提供科学可靠的依据。     

Introduction: Habitual travel choice

In this introduction to the special issue on habitual travel choice, we provide a brief account of the role of habit in travel behaviour, discuss more generally what habitual choice is, and briefly review the issues addressed in the solicited papers. These issues include how habitual travel behaviour should be measured, how to model the learning process that makes travel choice habitual, and how to break and replace car-use habits.     

路堤块石自然对流机理的室内模拟试验研究

为研究路堤块石在边界温度周期波动条件下的自然对流发生机理及降温效应的表征参数,采用室内模拟方法对50×50×65cm3尺寸的不同块石试样进行了试验和理论分析,结果表明:平均RAYLEIGH数能描述块石层自然对流的发生情况,不同厚度的块石层将产生不同的对流模式,厚度大的块石层所产生的自然对流降温效应也较大,上层砂砾加下层块石的结构自然对流效应最弱。块石层降温效果取决于块石粒径、层厚及铺筑在路堤中的位置,这一结论对多年冻土地区路堤设计、施工,合理确定块石铺筑层厚、铺筑层位具有现实指导意义。     

钢管活性粉末混凝土轴压短柱受力性能试验研究

进行22根钢管活性粉末混凝土(钢管RPC)轴压短柱试验,分析其荷载-变形曲线、破坏特征和影响极限承载力的主要因素。试验研究表明:钢管RPC轴压短柱的荷载-纵向应变曲线弹性阶段约为极限荷载的90％~95％;套箍系数ξ较小时,在达到极限荷载后承载力急剧下降;ξ较大时,在达到极限荷载后承载力下降平缓并呈回升趋势。ξ较小的试件多呈剪切破坏形态;ξ较大的试件所有断面上均被墩粗,试件的上、下两端明显局部鼓曲。构件承载力随RPC强度fc的提高而提高,两者基本成线性关系;套箍系数ξ越大,构件承载力也越大,但钢管对RPC的约束效果比对普通混凝土的差。提出的钢管RPC轴压短柱极限承载力的实用计算公式计算出的结果与试验结果吻合良好。     

液压马达实验方法研究

介绍了液压试验分类方式,根据其独具的特点对在用液压马达性能试验加载方法进行分析,并分类对工程机械用各种液压马达在泵工况下进行试验的可行性进行了探讨.     

岩土锚固力的确定及应用

选定粤赣高速公路有代表性的6种岩层,对其全、强、弱风化层组成的路堑高边坡锚索加固工程进行了69次破坏性拉拔试验。试验中对每种不同风化程度的岩(土)层采用锚固长度为1m、2m、3m的现场拉拔试验,计算出锚索加固边坡的锚固力值,为验证高边坡工程设计提供了准确的力学参数,也为全线高边坡工程的施工工艺提供指导性意见。同时,节省了大量的试验费用和时间,获得了显著的经济效益。     

高速公路可变收费标准模型研究

在介绍可变收费的理论基础上,分析影响高速公路可变收费标准制定的主要因素,并讨论不同时段收费费率同交通量的关系。在设定的可变收费方案下,建立了出行者选择的二元logit模型,根据调研样本,采用最小二乘法对模型参数标定并对设定的可变收费方案进行评价,结果证明可变收费能使交通流在各时段合理分布,从而提高了高速公路的服务水平。     

Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.     

Continuum theory for pedestrian traffic flow: Local route choice modelling and its implications

This contribution puts forward a novel multi-class continuum model that captures some of the key dynamic features of pedestrian flows. It considers route choice behaviour on both the strategic (pre-trip) and tactical (en-route) level. To achieve this, we put forward a class-specific equilibrium direction relation of the pedestrians, which is governed by two parts: one part describing the global route choice, which is pre-determined based on the expectations of the pedestrians, and one part describing the local route choice, which is a density-gradient dependent term that reflects local adaptations based on prevailing flow conditions.Including the local route choice term in the multi-class model causes first of all dispersion of the flow: pedestrians will move away from high density areas in order to reduce their overall walking costs. Second of all, for the crossing flow and bi-directional flow cases, local route choice causes well known self-organised patterns to emerge (i.e. diagonal stripes and bi-directional lanes). We study under which demand conditions self-organisation occurs and fails, as well as what the impact is of the choices of the different model parameters. In particular, the differences in the weights reflecting the impact of the own and the other classes appear to have a very strong impact on the self-organisation process.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.