超限严格管制下公路货运运力结构预测研究

针对传统的超限运输运力结构预测模型中,将车辆超限部分轴载转化为同一车型满轴载通过假设的不足,本文基于运力均衡的思想建立了超限严格管制下公路货运运力结构预测模型。在轴载调查的基础上,模型以单车装载量的累积概率分布需同单位货物重量的累积概率分布相一致为求解目标,推测了各轴型车辆总重谱的偏移情况,进而对超限管制变动下的公路货运车型结构及运量结构进行预测。模型在公路安徽世行项目II———超载运输研究中的使用结果表明,在超限运输严格管制条件下,2-S2将取代SU2成为公路货运的主力车型。     

不同阻尼计算模式对斜拉桥地震响应分析的影响

大跨度斜拉桥阻尼组成复杂,各阶振型的阻尼差别较大。为了研究阻尼计算模式对斜拉桥地震响应分析结果的影响,以主跨448 m的杭州湾跨海大桥北航道桥为对象,对应用考虑结构阻尼分布影响的振型应变能比例阻尼理论、常数阻尼、Rayleigh阻尼理论的适用性进行分析。结果表明,地震响应结果与所用的阻尼理论有关,常数阻尼不能很好反映结构的阻尼特性,而Rayleigh阻尼模式的计算结果取决于选取的参考振型,在较大的频率范围内存在过大或过小估计结构阻尼的问题,具有很大的随意性。相比之下,振型应变能比例阻尼理论能够考虑斜拉桥阻尼的分布特性,比较合理。     

初始条件对驾驶员逐日路径选择行为和网络交通流演化的影响

从查阅到的文献可以看到,在驾驶员逐日路径选择行为及网络交通流演化的研究中,均假定驾驶员第1天对路径的理解行程时间相同,也即初始条件中没有考虑驾驶员的个体差异性。首先,对初始条件和驾驶员逐日路径选择过程建模,在2条平行路径的简单路网中,运用Agent仿真方法模拟了不同初始条件下驾驶员逐日路径选择过程。结果表明：路网达到平衡所需的时间与驾驶员对历史信息的依赖程度显著相关,而与第1天驾驶员对路径行程时间理解的相关差异性不显著;路网平衡和用户平衡的差别与两者均显著相关。虽然在不同情况下路网均能够达到近似的用户平衡状态,但是平衡时驾驶员对2条路径的理解行程时间存在较大差异。     

沉管隧道管节预制工艺比选

沉管管节预制是沉管隧道工程的重要环节。文章介绍4种不同混凝土管节预制方式及工程应用实例,结合港珠澳大桥沉管隧道工程的实际情况,重点对干坞法和工厂法进行了深入比选研究,最后对港珠澳大桥管节预制实施方案提出建议。     

基于ANSYS的半挂车车架动态分析

车架可靠性对整车的安全性有着重要的作用。为了获得车架的动态特性,运用SOLIDWORKS建立某半挂车车架有限元模型,并在ANSYS中对车架做了动载荷、模态、谐响应等动态分析。在分析的基础上为车架的结构改进提供了理论依据。     

诱导信息下城市轨道交通乘客路径选择行为研究

为研究城市轨道交通诱导信息对乘客的影响，考虑信息提供时间、方式、更新频率及其与 路径属性之间的交互关系，提出融合诱导信息的城市轨道交通乘客路径选择行为决策框架。考 虑乘客在决策时表现出遵循混合决策规则的倾向差异和不同属性感知差异，建立改进的混合效用-后悔模型。将路径属性与诱导信息同时纳入调研情景设计，基于调研结果标定模型中关键参数，结果表明，基于混合决策规则的模型拟合效果最高(调整优度比达0.396)。进一步分析乘客对信息的偏好：相较于下载手机APP，乘客更喜欢社交媒体推送的形式接收诱导信息；引入信息推送频率时间价值，并对比得出，女性、中老年及非通勤乘客更倾向于接受更高频率的诱导信息推送服务；分析各属性弹性值，表明诱导信息提供方式和更新频率应作为管理者的辅助手段，在特定场景下(例如高峰拥堵和突发事故)诱导信息将会发挥更大的作用。研究揭示了诱导信息提供方式如何影响轨道交通乘客的路径决策机理，有助于设计更加精准和高效的诱导信息策略辅助客流组织管理。     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

A nested recursive logit model for route choice analysis

We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems.We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible parameter estimates and the fit is significantly better than the RL model. Moreover, the NRL model outperforms the RL model in terms of prediction.     

Modelling users’ behaviour in inter-urban carsharing program: A stated preference approach

In this paper, the effects of a inter-urban carsharing program on users’ mode choice behaviour were investigated and modelled through specification, calibration and validation of different modelling approaches founded on the behavioural paradigm of the random utility theory. To this end, switching models conditional on the usually chosen transport mode, unconditional switching models and holding models were investigated and compared. The aim was threefold: (i) to analyse the feasibility of a inter-urban carsharing program; (ii) to investigate the main determinants of the choice behaviour; (iii) to compare different approaches (switching vs. holding; conditional vs. unconditional); (iv) to investigate different modelling solutions within the random utility framework (homoscedastic, heteroscedastic and cross-correlated closed-form solutions). The set of models was calibrated on a stated preferences survey carried out on users commuting within the metropolitan area of Salerno, in particular with regard to the home-to-work trips from/to Salerno (the capital city of the Salerno province) to/from the three main municipalities belonging to the metropolitan area of Salerno. All of the involved municipalities significantly interact each other, the average trip length is about 30 km a day and all are served by public transport. The proposed carsharing program was a one-way service, working alongside public transport, with the possibility of sharing the same car among different users, with free parking slots and free access to the existent restricted traffic areas. Results indicated that the inter-urban carsharing service may be a substitute of the car transport mode, but also it could be a complementary alternative to the transit system in those time periods in which the service is not guaranteed or efficient. Estimation results highlighted that the conditional switching approach is the most effective one, whereas travel monetary cost, access time to carsharing parking slots, gender, age, trip frequency, car availability and the type of trip (home-based) were the most significant attributes. Elasticity results showed that access time to the parking slots predominantly influences choice probability for bus and carpool users; change in carsharing travel costs mainly affects carpool users; change in travel costs of the usually chosen transport mode mainly affects car and carpool users.