浮码头与旁靠船舶的运动响应特性研究

本文基于三维势流理论，采用数值分析方法研究了浮码头与散货船旁靠系泊的运动特性。考虑缆绳和护舷的非线性特性，探求了波浪周期对浮体运动、系缆力的影响规律。研究结果表明：考虑间隙内水体运动影响，旁靠散货船在横浪条件下的纵摇和纵荡运动有所增大。浮码头在计算工况下，缆绳受力较大，在某些工况条件下超出了国际系缆力标准的要求，旁靠散货船的缆绳在计算工况下均未超标，满足要求。研究结果为浮码头系泊系统的设计及工程运营提供理论参考。     

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。     

基于未确知测度理论的隧道施工瓦斯灾害风险评价

选取煤层厚度、煤层瓦斯含量、回风流平均瓦斯浓度、相对瓦斯涌出量、岩石类型、连通封闭性、深度值、隧道跨度、隧道长度、通风风速和涌水量等11项瓦斯灾害指标作为隧道施工瓦斯灾害风险评价指标体系,并建立各指标的等级标准。将序关系分析法(G1)和反熵权法(AEW)运用拉格朗日乘子法确定组合权重,构建各指标的单指标测度函数,并计算出各指标的综合测度评价向量。为了优化未确知测度置信度识别准则,引进集对分析(SPA)关联系数确定各隧道的瓦斯灾害风险等级。并对24个瓦斯隧道进行瓦斯灾害评价,并和FDA法评价结果和现场实测结果进行对比,精度达到91.7%,基本符合实际情况,表明了该评价方法合理有效,可为隧道施工瓦斯灾害评价提供理论参考。     

On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models

The speed-density or flow-density relationship has been considered as the foundation of traffic flow theory. Existing single-regime models calibrated by the least square method (LSM) could not fit the empirical data consistently well both in light-traffic/free-flow conditions and congested/jam conditions. In this paper, first, we point out that the inaccuracy of single-regime models is not caused solely by their functional forms, but also by the sample selection bias. Second, we apply a weighted least square method (WLSM) that addresses the sample selection bias problem. The calibration results for six well-known single-regime models using the WLSM fit the empirical data reasonably well both in light-traffic/free-flow conditions and congested/jam conditions. Third, we conduct a theoretical investigation that reveals the deficiency associated with the LSM is because the expected value of speed (or a function of it) is nonlinear with regard to the density (or a function of it).     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

滑差对贝氏体钢轨材料磨损行为的影响

通过对磨试验研究接触应力相同时贝氏体钢轨的磨损率、表面粗糙度、硬度,并结合扫描电镜观测到的磨损表面和剖面的形貌特征,分析不同滑差条件下贝氏体钢轨的磨损行为特征和变化规律.结果表明:接触应力为500 MPa条件下,贝氏体钢轨磨损率随滑差的增大而显著增大,滑差由2%增大到10%时磨损率增大了8倍;小滑差条件下的贝氏体钢轨表面较光滑,有少量疲劳裂纹,以滚动接触疲劳磨损为主;大滑差条件下表面粗糙,有疲劳裂纹、剥落坑和表面划擦痕迹,更接近滑动磨损;增大滑差可导致磨损表面加工硬化率偏大;增大滑差对贝氏体钢轨表面的滚动接触疲劳裂纹在深度方向的扩展几乎无影响,且对塑性变形层厚度影响不明显;大滑差可引发亚表面次表层裂纹.     

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.     

Stability of transportation networks under adaptive routing policies

Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic Equilibria” (MTE), in which users update their route choice at each intersection of the road network based on traffic conditions, to the case of non-equilibrium conditions, while preserving consistency with known existence and uniqueness results on MTE. We then exhibit sufficient conditions on the network topology and the latency functions for those MTEs to be stable in the sense of Lyapunov for a single destination problem. For various more restricted classes of network topologies motivated by the observed properties of travel patterns in the Singapore network, under certain assumptions we prove local exponential stability of the MTE, and derive analytical results on the sensitivity of the characteristic time of convergence to network and traffic parameters. The results proposed in this work are illustrated and validated on synthetic toy problems as well as on the Singapore road network with real demand and traffic data.     

冰雪条件下中国驾驶员跟驰行为及模型研究

分析驾驶员在冰雪条件下的驾驶行为特性，建立考虑驾驶员行为特性的跟驰模型，有助于丰富现有交通流理论.通过招募驾驶员开展实车跟驰试验，对比分析正常条件与冰雪条件下的驾驶行为差异.进而基于任务难度均衡理论构建包含人类因素参数的任务难度模块，引入改进后的智能驾驶员模型，并采用车辆轨迹数据对模型进行标定和有效性验证.研究表明：驾驶员在跟驰行驶过程中受外界刺激及自身驾驶能力影响时会对车辆行驶状态进行动态调整，试图保持期望间距，且速度与前车一致的状态；冰雪条件下驾驶员采取风险补偿行为，其车头时距波动幅度较正常条件收窄，模型引入人类因素参数可以较好地描述其差异性. 模型有效性验证表明，新模型在6个仿真场景中的表现都优于传统智能驾驶员模型，且表现出更好的鲁棒性.研究结果可为冰雪条件下的交通管理措施制定提供理论支持.     

驾驶人“感知-决策-操控”行为模型

为准确模拟驾驶人跟车行为，提出基于隐马尔可夫模型(Hidden Markov Model，HMM)的驾驶人“感知-决策-操控”行为模型。建立描述驾驶意愿的HMM模型，模拟驾驶人感知过程，获得期望的车间距；预测模块模拟驾驶人根据交通环境和自身生理、心理状态预测车辆未来轨迹，即决策过程；优化模块描述驾驶人为使预测的车辆轨迹跟踪上期望的车辆间距而采取的操控汽车的执行动作，即操控过程。上述3个模块的滚动过程实现了对驾驶人跟车行为的模拟。利用自然驾驶数据进行算例分析，结果表明，本文模型预测车间距平均误差仅为1.47%，证明了所建模型的有效性及准确性。本文为驾驶行为建模方法的理论研究和应用拓宽了思路。