Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNN_reg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNN_rec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNN_rec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R²), followed by the GCNN_reg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.     

A novel passenger flow prediction model using deep learning methods

Currently, deep learning has been successfully applied in many fields and achieved amazing results. Meanwhile, big data has revolutionized the transportation industry over the past several years. These two hot topics have inspired us to reconsider the traditional issue of passenger flow prediction. As a special structure of deep neural network (DNN), an autoencoder can deeply and abstractly extract the nonlinear features embedded in the input without any labels. By exploiting its remarkable capabilities, a novel hourly passenger flow prediction model using deep learning methods is proposed in this paper. Temporal features including the day of a week, the hour of a day, and holidays, the scenario features including inbound and outbound, and tickets and cards, and the passenger flow features including the previous average passenger flow and real-time passenger flow, are defined as the input features. These features are combined and trained as different stacked autoencoders (SAE) in the first stage. Then, the pre-trained SAE are further used to initialize the supervised DNN with the real-time passenger flow as the label data in the second stage. The hybrid model (SAE-DNN) is applied and evaluated with a case study of passenger flow prediction for four bus rapid transit (BRT) stations of Xiamen in the third stage. The experimental results show that the proposed method has the capability to provide a more accurate and universal passenger flow prediction model for different BRT stations with different passenger flow profiles.     

基于深度强化学习的分布式电驱动车辆扭矩分配策略

为在保证分布式电驱动车辆制动稳定性的前提下实现经济性的提升,提出了基于深度强化学习的分布式驱动前、后轴扭矩分配策略.在建立分布式电驱动车辆关键部件物理模型的基础上,基于车辆模型及制动稳定性约束,建立了基于深度强化学习的扭矩最优分配控制模型,并对传统固定比值的扭矩分配策略和所提出的策略进行了对比,结果表明:在新欧洲驾驶循...     

修正Q值围岩分级方法在碳酸盐地区大断裂超深埋特长隧道的应用研究

在隧道建设过程中,围岩分级的准确性直接关系着隧道施工的稳定性.文章在分析了众多的围岩分级方法以后,选择常用的Q值法作为修正对象;根据碳酸盐地区大断裂超深埋特长隧道的特殊性,提出了以断层、褶皱、地形以及岩溶的宏观影响系数对Q值法进行修正,得出了修正以后的建议Q’值法公式.以大坪山隧道为实例,进行验证分析,得出了在最深埋地段围岩级别较高、离断层最近的围岩级别较差的结论,与实际揭露的围岩级别结果基本一致.     

大直径SMW工法设计与施工关键技术分析

深基坑支护设计,不仅要保证基坑内的正常作业,而且要防止基坑及坑外土体的移动,确保基坑附近建筑物、道路、管线等的正常使用.因此,深基坑围护结构的安全性显得尤为重要.在众多围护方法中,SMW工法(型钢水泥土搅拌墙)以其适用性强、围护成本低、施工周期短而倍受关注.文章结合工程实践,对大直径SMW工法在软土地基深基坑支护中的支护结构设计及施工要点及难点进行了分析和探讨.     

深埋隧洞围岩应力分布与破坏机理

深埋隧洞围岩高应力破坏机理是研究深埋岩体力学特性和深埋地下工程实践中被关注的一个重要认识问题,深埋条件下围岩应力和围岩强度之间的矛盾更加复杂和典型,也成为认识问题的基本出发点。文章介绍了深埋隧洞开挖时不同部位围岩应力的变化路径;通过对比锦屏辅助洞出现的围岩破坏现象,分别论述了导致边墙松弛型破坏、应力集中部位的片帮破坏和岩爆破坏的围岩应力变化特征,在一定程度上解释了这些破坏的内在机理;并通过采用数值方法再现脆性围岩V型破坏形式,分析探讨了脆性围岩高应力破坏的局部化问题;指出了深埋岩石力学研究中的几个重要环节,如岩体力学特征的尺寸效应和应力路径效应等对准确认识深埋隧洞高应力破坏内在机理的重要意义。     

地铁车站深基坑变形规律的三维数值模拟分析

以西安地铁某大型车站深基坑工程为背景,采用现场监测与三维数值模拟相结合的方法,研究了开挖过程中地铁车站深基坑的变形规律。结果表明,围护桩的变形直接关系到基坑的稳定和安全;开挖使得基坑周围土体下沉,地表沉降呈抛物线型;计算结果与监测结果基本一致,运用FLAC3D数值计算方法研究深基坑的变形规律是可行的、可靠的。     

上海国际航运中心洋山深水港区平面布置方案

依托外海岛礁地形,在强潮流、高含沙量海域通过封堵汊道形成港区陆域,建设大型集装箱深水港区,工程建设的关键技术之一就是如何确定合理的港区平面布置方案。本文主要结合大、小洋山南、北两条岛链所形成的东口窄(宽约1 000 m)而水深深(平均约50 m)、西口宽(宽约7 600 m)而水深浅(平均约11 m)的喇叭型地形特征,以及海域复杂的水文泥沙条件等,通过论证,推荐实施单通道港区平面布置方案,以利于港区水深的维持、流态的平顺、船舶安全航行以及淤积强度的减少。现场监测表明,港区使用水域在流态、淤强等方面的结果与研究结论基本相符。     

摩擦生热的仿真分析

利用摩擦热产生塑形变形进行深孔无屑加工,不仅节省材料,而且可以获得高性能的工件。摩擦热深孔成形的热力分析是该工艺技术的一个关键问题。利用LS-DYNA动力有限元分析软件建立圆柱钻头和球形钻头的滑动摩擦副热结构耦合模型,分别计算了模型的温度场以及热应力场。摩擦过程中,最高温度在接触面外侧,温度从外侧向中心逐渐降低,球形钻头由于受力集中,更符合后期钣金深孔冲压成形要求。通过摩擦生热达到一定温度,为进一步研究摩擦热加工深孔工艺奠定了基础。     

洋山深水港区二期水工码头大口径嵌岩桩施工工艺

介绍了洋山深水港区二期水工码头大口径嵌岩桩的主要施工工艺及控制要求。阐述了其施工难点及处理措施,并对桩身质量检测项目和结果作一叙述。最后指出:要确保钻孔嵌岩桩的施工质量,其关键在于有效管理,施工人员的高度责任心,以防为主,精心施工。