基于线性叠加的智能船舶经济性评估模型构建

经济性是智能船舶能否实现推广应用的关键,目前国内外有关智能船舶经济性分析的研究较少。本文通过对传统船舶经济性分析方法进行修正优化,提出智能船舶经济性分析计算模型,并根据理论推导结果,得出智能船舶经济性分析的线性叠加简化模型,并利用40万吨级VLOC实际运营数据进行科学性检验。本文提出的模型与方法适用于各类智能模块加装情况下的经济性核算,为相关单位开展智能船舶经济性快速分析提供了理论支持和解决方案。     

面向新型混合交通流的智能交叉口网络布局优化

考虑网联自动驾驶车辆(Connected Autonomous Vehicle, CAV)应用先进的车联网与自动驾驶技术，可以采用智能交叉口的组织形式，大幅提升交叉口的通行效率，为降低CAV与人工驾驶车辆(Human-driven Vehicle, HV)混行条件下城市交通系统的整体出行成本，提出智能交叉口在城 市交通网络中的布局优化问题，建立数学优化模型并求解。首先，基于对两类车辆行驶特性的分析，建立混合用户均衡模型，描述CAV与HV的路径选择行为；其次，从交通规划者的角度，以系统最优为目标，整合混合用户均衡模型，建立面向新型混合交通流的智能交叉口网络布局优化模型，并利用改进的遗传算法求解；最后，选取Sioux-Falls交通网络作为案例分析，验证模型与算法的有效性，并研究CAV渗透率变化对优化结果的影响。研究表明，智能交叉口在城市路网中的合理规划极大地提高了新型混行场景下城市交通系统的出行效率，同时，大幅降低了由于网联自动 驾驶单方面技术优势带来的CAV与HV的出行效率差距，增进了出行公平性。     

Dynamic Simulation of a Smart Crank and Slider Mechanism

In this paper, a smart crank and slider mechanism is analyzed mostly from a dynamic view. By means of dynamic explicit finite element method, 3D nonlinear structure is simulated. It is proved that the mechanism can effectively accomplish smart movement prescribed. And in order to ensure reciprocal movement with higher frequency, measures should be taken to avoid over heating of parts. Compared with internal energy, kinetic energy of total rigid body is dominating, and Y direction equivalent rigid velocity is much higher than X direction velocity. Equivalent rigid velocity of all parts is consistent with respective movement condition. For both energy and velocity, slider effect is dominating. Three direction equivalent inertia force oscillates. Force amplitude in Y -direction is comparitively the greatest.     

基于智慧地铁视角的广州地铁AFC系统架构设计

自动售检票(AFC)系统作为城市轨道交通中与乘客接触最为紧密的系统,应基于“以用户为中心”来提升客运服务水平。通过优化系统层级、整合多元化支付平台、引入智能客服功能、对接电子发票系统等措施,规划设计了适用于广州新一轮城市轨道交通线网建设要求的AFC系统架构,以期打造智慧的AFC系统,为乘客提供更为人性化和多元化的服务。     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

W-SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models

The development and calibration of complex traffic models demands parsimonious techniques, because such models often involve hundreds of thousands of unknown parameters. The Weighted Simultaneous Perturbation Stochastic Approximation (W-SPSA) algorithm has been proven more efficient than its predecessor SPSA (Spall, 1998), particularly in situations where the correlation structure of the variables is not homogeneous. This is crucial in traffic simulation models where effectively some variables (e.g. readings from certain sensors) are strongly correlated, both in time and space, with some other variables (e.g. certain OD flows). In situations with reasonably sized traffic networks, the difference is relevant considering computational constraints. However, W-SPSA relies on determining a proper weight matrix (W) that represents those correlations, and such a process has been so far an open problem, and only heuristic approaches to obtain it have been considered.This paper presents W-SPSA in a formally comprehensive way, where effectively SPSA becomes an instance of W-SPSA, and explores alternative approaches for determining the matrix W. We demonstrate that, relying on a few simplifications that marginally affect the final solution, we can obtain W matrices that considerably outperform SPSA. We analyse the performance of our proposed algorithm in two applications in motorway networks in Singapore and Portugal, using a dynamic traffic assignment model and a microscopic traffic simulator, respectively.     

智能天线系统中干扰抵消的算法研究

首先分析了智能天线在CDMA系统中应用,理论分析了通过空间特征向量来对其实现“零陷“,提出采用压低基站的上行干扰提升系统的性能的新算法,实验仿真显示:该算法通过空域滤波,能够显著提高系统性能.     

中国公路隧道近10年的发展趋势与思考

中国公路隧道在规模、数量、建设速度等持续快速发展的形势下,近10年来又取得了众多隧道建设技术的突破,已由隧道大国步入向隧道强国转变的轨道。首先宏观研究分析近10年来中国公路隧道建设状况,对比论述了山岭、水下、城市地下道路等类型隧道的发展特点、趋势及相关建议;从隧道建设需求导向、地质超前预报、节能环保、应急救援等方面提出了公路隧道建设理念的变化,对比总结了钻爆法、盾构法、沉管法及TBM法等4类修建公路隧道常用施工方法的应用情况及未来发展趋势,并在此基础上,对中国公路隧道未来建设中将遇到的一些问题进行了研究与思考。结果表明:应改变以支护参数设计为重点的隧道设计理念,建立以介质场为主体的隧道结构设计方法,在隧道场解重构理论与技术体系方面进一步创新;对于采用双洞布局模式的长大公路隧道,为减少长深斜竖井设置,提升建设速度,应优先采用“钻爆法+小TBM导洞扩挖法”相结合的混合方法来修建;针对越来越多的公路隧道进入后运营期,提出了隧道智能监测评估与快速修复的技术途径,以将隧道修复作业所带来的影响降到最低;中国公路隧道建设应融合大数据、智能装备、5G等先进技术,并尽快完成配套标准的制定。     

智慧高速公路设计案例分析

本文以某智慧高速的设计案例为依托,首先对国内外智慧高速发展和应用现状进行归纳总结,分析认为电动化、智能化的新型运载工具,信息化和功能专业化的基础设施,开放、共享、协同的管理与服务将是未来高速公路三大发展趋势。通过对高速公路使用者和管理者的需求针对性地精准分析,构建面向高速公路使用者和管理者的创新型应用服务。结合新技术的发展趋势,以服务需求为导向,建设"三网合一"智慧高速基础设施和智慧云控平台构成、数据流为驱动的、"开放、包容、创新"的智慧高速公路。基于案例分析的智慧高速规划设计全过程,对于国内智慧高速的整体设计和建设规划具有重要参考价值,探索开辟全新的高速公路智能化建设方向和思路。     

汽车驾驶人姿态监测系统研究综述（双语出版）

随着自动驾驶技术的发展,驾驶人将会参与更多的与驾驶无关的活动,从而呈现出新的姿态,这些新姿态是优化传统被动安全系统的重要切入点。而且在未来相当长的时间内,自动驾驶车辆的行驶依然依赖于人和系统的密切配合。对驾驶人姿态的观察,则可以为判断驾驶人是否有能力及时接管车辆提供帮助,从而确保安全、合理的人机交互过程。通过对大量相关文献的系统性梳理,综述了汽车驾驶人姿态监测技术的智能化发展趋势,从传感器种类以及相应的姿态监测算法出发,分析了目前不同监测系统的优缺点。研究发现,尽管传感器技术和姿态识别算法取得了明显进步,然而廉价稳定且能够在实际驾驶条件下对驾驶人姿态准确感知的监测系统依然缺乏。总体而言,目前的监测系统大多只是集中于对驾驶人局部身体部位的感知,缺乏实际驾驶条件下的性能分析,并且对驾驶人状态的实时感知和预测能力仍有待完善。最后,针对目前监测系统所面临的问题,对未来可能的研究方向进行展望,并提出主动式立体视觉系统和压力传感器阵列相融合的驾驶人姿态监测方式。研究成果将为驾驶人姿态监测系统的研究提供参考和借鉴,从而有助于道路交通安全水平的进一步提升,同时也可为人机交互界面的设计带来启发。