A hybrid scheme for real‐time prediction of bus trajectories

The uncertainty associated with public transport services can be partially counteracted by developing real‐time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model combines schedule, instantaneous and historical data. The contribution of each predictor as well as values of respective parameters is estimated by minimizing the prediction error using a linear regression heuristic. The hybrid method was applied to five bus routes in Stockholm, Sweden, and Brisbane, Australia. The results indicate that the hybrid method consistently outperforms the timetable and delay conservation prediction method for different route layouts, passenger demands and operation practices. Model validation confirms model transferability and real‐time applicability. Generating more accurate predictions can help service users adjust their travel plans and service providers to deploy proactive management and control strategies to mitigate the negative effects of service disturbances. Copyright © 2017 John Wiley & Sons, Ltd.     

Urban traffic flow prediction: a spatio‐temporal variable selection‐based approach

Short‐term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio‐temporal‐based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high‐density road networks. Therefore, we propose a spatio‐temporal variable selection‐based support vector regression (VS‐SVR) model fed with the high‐dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two‐stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high‐dimensional spatio‐temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real‐world traffic volume collected from a sub‐area of Shanghai, China, demonstrate that the proposed spatio‐temporal VS‐SVR model outperforms the state‐of‐the‐art. Copyright © 2015 John Wiley & Sons, Ltd.     

共享单车停放需求影响因素分析和预测研究

为了解决城市共享单车的乱停乱放问题,本文基于北京市的共享单车出行大数据,提出了共享单车停放需求预测的多项Logit模型。首先分析了单车停放需求的影响因素,然后选取了时间、空间及天气方面的12个因素为自变量,通过Wald检验分析了这些因素与停放需求的相关性和显著性,基于多项Logit模型建立了共享单车的停放需求预测模型。结果表明:工作日、时段、商业区、所临道路类型、临近轨交站、高温、下雨、以及风力等级与共享单车停放需求显著相关;构建的预测模型总体预测准确率为77.5%,其中对出现频率最高的低停放需求预测准确率高达86.49%。     

人工智能科学在软土地下工程施工变形预测与控制中的应用实践——理论基础、方法实施、精细化智能管理（示例）

首先,介绍了基于人工神经网络的智能预测方法（多步滚动预测）和基于智能模糊逻辑法则的施工变形控制方法对策;其次,介绍了基坑施工和盾构掘进施工变形智能预测与控制案例。经过应用实践,认为智能方法的优点是： 对于结构变形位移和周边地表沉降/隆起,智能方法所得的预测值（3~5 d）与其相应实测值的精度偏差一般为5%~10%;不只是可以了解到当天已发生的信息,还可预见3~5 d将要发生的变形位移和沉降/隆起等的预测定量值;在施工变形达到超限阈值前,采用智能模糊逻辑控制法则作处理,通过调整相应的施工技术参数,即可使后续变形始终处于允许的限值之内,而无需附加额外的巨大花费,节约造价,节省工期,还可实现远程、无线、视频监控。在探讨地铁施工变形智能预测与控制的基础上,开发了盾构掘进施工中工程周边地表沉降/隆起变形的多媒体三维动态可视化仿真程序软件,研制了盾构掘进施工计算机智能管理系统。目前,上海隧道工程有限公司已在上海市沿江通道盾构施工中进行试验性应用,取得了良好的技术效益。最后,对人工智能科学发展的前景及存在的一些问题进行了探讨。     

基于HOTTEL日照辐射模型的船体温度变形预报方法

基于HOTTEL日照辐射模型,提出计及日照因素影响的船体温度变形预报方法,根据赤纬角和太阳时角等参数计算船体表面日照辐射强度,与二维稳态热传导理论和薄壁梁弯曲理论结合,计算船体梁水平和垂向弯曲变形。以某典型超大型油船为例,计算不同季节考虑日照辐射的船体变形,预报结果与有限元计算结果在夏季和冬季分别相差约3.97%和14.29%,满足工程应用要求。     

动态管理模式下路侧停车泊位占有率预测方法

城市停车已逐步实现信息化和动态化管理,本文对动态管理模式下大范围路侧泊位占有率预测方法进行研究.在收集美国旧金山492万条停车交易数据的基础上,利用可同时提取数据空间关联和时序趋势特征的卷积长短时记忆神经网络(Convolutional LSTM Network,ConvLSTM),分别构建考虑停车费率和时限动态变化的有政策模型,和没有动态管理信息输入的无政策模型.结果显示,有政策模型的训练效率和预测精度会显著提升.在政策平稳阶段,两种模型均能够有效预测泊位占有率;在政策发生变化时段,无政策模型的预测误差出现激增,但有政策模型的预测误差依然保持平稳,表明本文提出的方法能够很好地应对动态管理模式下停车需求的变化.     

综合交通枢纽客流识别系统的应用研究

本研究意在探究客流感知对综合交通枢纽运营管理的影响,提出客流预测系统的功能需求,构建客流预测系统的架构。     

Variability in sound power levels: Implications for static and dynamic traffic models

Classically, one mean vehicle representative of each category is used by both static and dynamic traffic noise prediction models. The spectrum associated with this mean vehicle is determined from a linear statistical regression analysis based on measurement campaigns on a track or in situ. However, the variability of individual vehicle emissions can influence predictions and hinder comparison between static and dynamic models. In order to estimate the induced bias, statistical analysis of the distributions of sound power levels emitted by the individual passage of vehicles during 82 measurement campaigns was carried out. The results show that 92% of the residual regression distributions are Gaussian and that standard deviations can reach 3.6 dBA. The value of the proposed correction term for this case study could reach 1.4 dBA for light vehicles and 1.2 dBA for heavy vehicles. This analysis also shows that the variability in sound power levels and thus the corresponding corrections are higher at the lowest speeds that correspond to urban driving conditions.     

有限域液体附加质量对板结构振动特性的影响

基于附加质量法、公式估算法以及流固耦合有限元方法对有限域流体与结构耦合振动特性进行计算分析,以方箱底板单面接触有限域的液体,以及双面分别接触有限域液体和无限域液体工况为例,通过一系列计算得出工程中处理附连水质量影响的一般方法和经验。给出处理有限流体域问题时附加质量法的一般适用准则,即当浸水参数h/b0.1时,利用附加质量法具有足够的工程计算精度,而当h/b>0.1时,建议采用流固耦合方法进行计算。     

破冰船阻力性能预报方法综述

随着近年来北极航道的通航以及各国对北极油气资源的开发,破冰船在极地开发领域的重要性也随之凸显。而破冰船阻力性能作为直接决定破冰船破冰能力的一项关键指标,对破冰船的设计和优化有重要的意义。由于国内对冰阻力的研究开展较晚,缺乏相关参考。本文针对实船试验、模型试验和理论预报等国际上研究破冰船阻力性能的主要手段进行介绍,并分析各种方法的优势及缺点,以便于国内研究者开展关于破冰船阻力的研究工作。