Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between commuters and non-commuters in Sweden

Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.     

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.     

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.     

长凼子隧道岩溶突水、突泥特征及控制因素分析

长凼子隧道横穿长江支流大溪河分水岭--齐耀山复式背斜,为非单一碳酸盐岩溶区.区内岩溶及岩溶水受自然地理、地形地貌、地文期、地层岩性、构造地质控制,岩溶发育,岩溶地下水丰富,给隧道施工带来很大危害.文章对长凼子隧道岩溶突水、突泥特征及控制因素进行了分析,其分析结果对类似岩溶隧道的勘察、设计、施工,以及综合超前地质预报具有重要的指导作用和参考价值.     

相对标高法立模在西江大桥施工中的应用

西江大桥施工监控过程中,在长悬臂施工状态下,采用相对标高法立模,尽量消除温度对立模标高的影响以及在实际工程中取得的效果。     

C值大于2.5m悬臂板计算解析

对于c值大于2.5m的悬臂板,分别按桥规悬臂板车轮荷载分布宽度法、巴赫方法及沙柯-巴赫方法进行计算,并对计算结果进行了分析。     

浅谈我国LRIT船载设备解决方案

SOLAS公约关于LRIT即船舶远程识别和跟踪系统的条款已经开始生效,我国LRIT系统也开始正式运行。文章对LRIT系统构成、建设情况、配备要求,以及目前船舶上比较常见的几种类型的INMARSAT-C和MINI-C船站的解决方案做了介绍,可供船舶业内人员参考。     

铁路桥梁刚度描述方法

针对我国新建桥梁跨度和结构形式远远超出现有桥梁设计规范的适用条件,以及国内外桥梁刚度指标和取值存在较大差异,进行铁路桥梁刚度描述方法和预应力混凝土连续梁(刚构)桥刚度的限值研究。提出一种铁路桥梁刚度的统一描述方法,即采用设计荷载下的梁端处桥面(轨面)的横向、竖向转角和扭转角及非梁端处桥面(轨面)的横向、竖向转角和扭转角沿桥纵向的变化率(即曲率)描述桥梁刚度,并推导出大跨度铁路预应力混凝土连续梁(刚构)桥刚度设计参考限值。该方法不仅能方便描述简支梁的刚度问题,与现有规范相衔接,而且能描述规范所不能涵盖的大跨度桥梁和特殊桥梁结构的刚度问题。通过代表性桥梁的车桥系统动力响应计算分析,证明了统一描述方法的可行性和较好的统一性。     

长短平台复合型牵索挂篮设计

在挂篮的高度和长度受限的情况下，通过对短平台牵索挂篮、普通3角挂篮、长平台牵索挂篮3个方案优缺点的比较和对长平台牵索挂篮方案的详细计算和试验，设计出适合仓安路斜拉桥现场施工的长短平台复合型牵索挂篮施工方案。实践证明该方案是切合可行的。     

Turbulence in coastal fronts near the mouths of Block Island and Long Island Sounds

Measurements of turbulence were performed in four frontal locations near the mouths of Block Island Sound (BIS) and Long Island Sound (LIS). These measurements extend from the offshore front associated with BIS and Mid-Atlantic Bight Shelf water, to the onshore fronts near the Montauk Point (MK) headland, and the Connecticut River plume front. The latter feature is closely associated with the major fresh water input to LIS. Turbulent kinetic energy (TKE) dissipation rate, ε, was obtained using shear probes mounted on an autonomous underwater vehicle. Offshore, the BIS estuarine outflow front showed, during spring season and ebb tide, maximum TKE dissipation rate, ε, estimates of order 10^− 5 W/kg, with background values of order 10^− 6 to 10^− 9 W/kg. Edwards et al. [Edwards, C.A., Fake, T.A., and Bogden, P.S., 2004a. Spring–summer frontogenesis at the mouth of Block Island Sound: 1. A numerical investigation into tidal and buoyancy-forced motion. Journal of Geophysical Research 109 (C12021), doi:10.1029/2003JC002132.] model this front as the boundary of a tidally driven, baroclinically adjusted BIS flow around the MK headland eddy. At the entrance to BIS, near MK, two additional fronts are observed, one of which was over sand waves. For the headland site front east of MK, without sand waves, during ebb tide, ε estimates of 10^− 5 to 10^− 6 W/kg were observed. The model shows that this front is at the northern end of an anti-cyclonic headland eddy, and within a region of strong tidal mixing. For the headland site front further northeast over sand waves, maximum ε estimates were of order 10^− 4 W/kg within a background of order 10^− 7–10^− 6 W/kg. From the model, this front is at the northeastern edge of the anti-cyclonic headland eddy and within the tidal mixing zone. For the Connecticut River plume front, a surface trapped plume, during ebb tide, maximum ε estimates of 10^− 5 W/kg were obtained, within a background of 10^− 6 to 10^− 8 W/kg. Of all four fronts, the river plume front has the largest finescale mean-square shear, S² ~ 0.15 s^− 2. All of the frontal locations had local values of the buoyancy Reynolds number indicating strong isotropic turbulence at the dissipation scales. Local values of the Froude number indicated shear instability in all of the fronts.