智慧交通与智慧物流

智慧交通和智慧物流是智慧城市建设的重要内容.智慧交通技术的创新和发展，促进了智能交通升级.解决大规模数据计算问题是物流智能化的基础.本次论坛以“智慧交通和智慧物流”为主题，介绍了安全驾驶统一集理论、实践及成果，探讨了新一代信息技术对智能交通发展与创新的推动作用，介绍了无人驾驶技术的总体框架、发展与应用，探讨了智慧物流所面临的大规模数据计算问题及解决方案.     

基于宏观基本图的路网多子区状态一致协同控制

针对基于宏观基本图(MFD)的路网多子区协同控制未考虑各子区拥堵状态差异性及均衡性的问题，本文提出以多子区状态可达一致为目标的子区边界状态反馈控制设计方法. 首先，基于路网 MFD模型建立路网多子区协同模型；进一步，基于部分变量稳定性理论，设计多子区状态可达一致的边界状态反馈控制律.在此基础上，考虑子区拥堵状态的差异性，设计了子区间的协同控制策略，快速缓解子区拥堵状态；同时，提出子区边界输入流的分配优化策略.最后，以潍坊市实际路网为背景建立仿真模型.实验结果表明，本文方法可实现子区交通流分布的均衡性，快速缓解子区拥堵状态，较大幅度地提升路网运行效率.     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

用于智能驾驶系统评价的乘员损伤模型

只有较少的交通事故数据资源被用于建立基于碰撞速度信息的乘员损伤模型，致使所得到的模型精度差。为此，提出了基于车辆变形深度的乘员损伤模型。对美国不同制造年代和车辆级别的事故数据进行聚类分析，论证出车辆变形深度与乘员损伤风险具有相关性。以车辆变形深度为自变量，通过回归分析得到乘员损伤模型。不同种类车辆的乘员损伤模型拟合精度R²约为0.9，证明了该模型的正确性。为进一步验证，以此模型为基础，评价智能驾驶系统的有效性。以自动紧急制动系统为例，对比基于变形深度和速度变化量信息2种方法的有效性计算结果。结果表明：2组结果的平均误差不超过1%，验证了基于变形深度的乘员损伤模型的准确性。该模型仅需要事故数据库中准确的变形深度信息，能够获得更多的事故数据支持，从而可以更好地适应于不同类别智能驾驶系统的评价需求。     

宁波舟山港主通道一体式钢管桩制造技术

宁波舟山港主通道工程70m跨非通航孔主桥和62.5m跨非通航孔引桥采用螺旋焊缝钢管桩(每根桩上、下部分壁厚不同),桩径分1.6m、1.8m、2m三种规格,最大桩长109m。针对钢管桩大直径、超长、不等壁厚等特点,钢管桩采用全自动化整桩螺旋卷制生产制造方案。制造过程中,将2根桩相同壁厚部分连起来制造,采用在线预精焊、全自动激光跟踪扫描智能化纠偏焊接技术、钢带对接外焊缝自动焊接设备和剪力环自动焊接专用机器设备,建立大直径、超长钢管桩全自动化生产线,实现钢管桩高质、快速制造。该技术的应用,使钢管桩焊接质量一次检测合格率达99.5%以上,加工效率提高2.5倍,仅用16个月,完成了24万吨钢管桩的制造。     

桥吊智能制动器改造测试

桥吊智能制动器研制成功后,为确保其性能,对其进行测试.介绍测试准备工作和测试做法.测试表明,智能型制动器能有效减小制动冲击,保护传动链,并能有效减小结构晃动.     

機車騎士未戴安全帽辨識之研究

臺灣地區機車車輛數眾多,但在應用在機車騎士執法上這一層面較少討論到。國人普遍駕駛習慣不良,而道路交通事故的發生常常就是因爲用路人不守法的行爲所致,近年來未戴安全帽而招舉報的案件上昇10萬多件,使得交通安全的問題日益嚴重。文中使用監視攝影機,透過模糊類神經演算法的運算,篩選出未戴安全帽者,並可結合顔色特徵作進一步分析,提高其辨識率,進行未戴安全帽的辨識工作。這一研究經簡單測試有九成的準確率,在智慧型執法系統中有輔助的效應,也殿立了後續發展的基礎。     

BIM技术在西安动车段项目深化设计中的应用

论述动车段的主要功能,简明介绍西安动车段整体工程概况和DCDSG标项目室外综合工程的特点。由于该项目室外综合工程错综复杂,决定应用BIM技术进行深化设计来弥补传统工作模式的不足,以期达到提高各专业间沟通效率、节约施工成本和周期的目的。首先建立室外工程BIM模型,利用BIM技术强大的可视性、虚拟性和优化性,对图纸进行复核,及时发现图纸问题并纠正;然后进行碰撞检查计算和设计方案优化,降低施工阶段可能存在的返工风险,减少材料浪费。实践结果表明,BIM技术在本工程深化设计中优势明显,为推动BIM技术在站场类项目相关工程中的应用提供基本思路和参考依据。