Comparing travel mode and trip chain choices between holidays and weekdays

Choices of travel mode and trip chain as well as their interplays have long drawn the interests of researchers. However, few studies have examined the differences in the travel behaviors between holidays and weekdays. This paper compares the choice of travel mode and trip chain between holidays and weekdays tours using travel survey data from Beijing, China. Nested Logit (NL) models with alternative nesting structures are estimated to analyze the decision process of travelers. Results show that there are at least three differences between commuting-based tours on weekdays and non-commuting tours on holidays. First, the decision structures in weekday and holiday tours are opposite. In weekday tours people prefer to decide on trip chain pattern prior to choosing travel mode, whereas in holiday tours travel mode is chosen first. Second, holiday tours show stronger dependency on cars than weekday tours. Third, travelers on holidays are more sensitive to changes in tour time than to the changes in tour cost, while commuters on weekdays are more sensitive to tour cost. Findings are helpful for improving travel activity modeling and designing differential transportation system management strategies for weekdays and holidays.     

板-桁模型计算钢筋混凝土构件抗扭强度综述

首先简单介绍了现有的钢筋混凝土构件抗扭强度计算的简化力学模型,并指出了板-桁模型提出的必要性,最后对板-桁模型进行了详细介绍,有关经验可供相关专业人员参考。     

经直肠豚鼠志贺菌痢疾模型的改良

目的改良经直肠途径建立豚鼠志贺菌痢疾动物模型的方法。方法 20只受试豚鼠根据感染前24 h禁食或不禁食处理分为2组,8只非禁食组参照文献经直肠灌注致病性福氏志贺菌sf301菌株,12只禁食组经直肠分别灌注PBS(5只)和sf301菌株(7只),观察受试豚鼠的感染症状,通过组织病理学方法证实其发病与否。结果非禁食组豚鼠仅有1只(1/8)在感染后24 h内表现出明显的体重减轻、发热等典型的菌痢症状。禁食组经直肠灌注sf301菌株的豚鼠(6/7)在感染后24 h内有显著的发热症状,体重减轻则发生在感染后的24~48 h内;灌注PBS的豚鼠(0/5)无异样症状。组织病理学结果显示,发病豚鼠结肠末端的黏膜以及黏膜下层遭到明显的破坏,肠壁增厚,出现水肿、糜烂、大量中性粒细胞浸润等。结论成功建立了改良的经直肠豚鼠志贺菌痢疾动物模型。     

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

Emergence of cooperation in congested road networks using ICT and future and emerging technologies: A game-based review

Information and communications technologies (ICT) and future and emerging technologies (FET) are expected to revolutionize transportation in the next generation. Travelers’ behavioral adaptation is a key to their success. We discuss the notion of managing traffic congestion by enhancing cooperation in road networks enabled with ICT and FET. Cooperation is an emergent social state related to the dynamics and complexity of road traffic and reinforced learning. Game theory and research in behavioral economics show that cooperation can be leveraged to efficiently solve social dilemmas similar to traffic congestion. We review the applicability of behavioral economics and game theory concepts to route, mode and departure time choice problems. Beyond advancing theory, research on cooperation in the context of transportation is still in its infancy. We discuss state-of-the-art methodologies and their weaknesses and review the unexplored opportunities inherent in game-based methodologies. A behavioral-technological research agenda for FET is also discussed.     

基于相对变形的大变形隧道地应力反算

针对复杂地质条件下高地应力大变形隧道地应力复杂多变的特点，为满足该类隧道动态设计对地应力资料的要求，提出基于上台阶开挖后拱腰水平收敛与拱顶下沉的相对比值并结合开挖揭示的围岩情况，建立二维数值模型反算地应力的方法。以丽江至香格里拉铁路中义隧道为例，对该方法进行详细说明。研究表明所述方法具有无需地应力测试、计算方便的优点，施工期间可用于地应力分析、预测。中义隧道典型区段地应力反算结果表明： 1）隧道出口及2号横洞工区的地应力分布特点并不相同； 2）围岩及埋深差别不大的区段，隧道水平收敛变形出现较大差异主要是由于地应力侧压力系数不同引起的。     

水工模型试验的流速测量方法与数据处理程序

在水工模型试验中,基础数据的准确度与精确度直接关系到科研成果的质量。流速是模型试验中观测的主要数据之一。模型流速的测量方法有许多种,如示踪物法、光电法、声学多普勒法等。光电法是目前水工模型试验中应用最多的流速测量方法。文章所述的模型测杆流速处理程序实现的功能就是计算流速测杆采集流速的平均值并直观地以流速过程线图显示。通过应用于上海国际航运中心洋山港区整体物理模型试验的实践表明,程序采用微调方法,解决了判断涨落潮时存在的问题,提高了水工模型试验工作的精度和效率,为科学决策提供了依据。     

舰船水下爆炸鞭状运动中的阻尼效应

舰船在水下爆炸气泡脉动压力作用下将产生鞭状运动冲击响应。为了掌握冲击响应的衰减规律,建立了舰船结构、设备、水舱内流体和外部流体等在振动和晃荡过程中的阻尼效应动力学模型,并编写程序对是否考虑上述阻尼效应的舰船鞭状运动响应进行了数值计算。研究结果表明,对于具有相对较大质量的内部设备和水舱的舰船来说,上述阻尼效应对舰船水下爆炸鞭状运动的衰减起到重要的作用,如不予以考虑有可能造成对舰船鞭状运动响应幅值的高估。     

船舶机损事故人误原因因素的层次分析法

基于认知系统工程分类方法，应用层次分析法构建了船舶机损事故人误原因因素结构模型，将成对比较矩阵的特征向量作为因素的权重，确定相关因素的影响力排序。给出了一种识别船舶机损事故中人误原因主要影响因素的实用方法，并针对性地提出相应预防措施，减少船舶机损事故提供可靠的理论依据。