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.     

基于协同共享理念的汽车文献与情报资源服务平台开发

介绍了以＂协同、共享＂理念为宗旨,以多年积累的科技信息资源以及情报部门第一手情报研究资料为基础,以实现信息资源共享为目标而开发设计的＂汽车文献信息与情报资源共享平台＂。该平台开发了基于＂领域本体＂概念的汽车专业标引检索体系,首次实现了企业和本地高校资源的同平台发布和共享,实现了异构库跨库检索功能。该平台的开发应用将为推动企业技术创新、科学决策和人才培养提供重要的支持和保障作用。     

船舶机舱综合管理与信息共享技术的研究

论述了基于集散型结构的多级分布式船舶机舱综合管理系统。它把机舱的实时监控与船舶的综合管理融为一体,实现数据共享。软件实现上,在基于COMMAND对象行为模式的基础上,就船舶机舱下位监控网,船舶综合管理局域网,这两种网络之间的数据通讯与数据共享技术以及网络数据库技术实现等问题进行论述。     

罗兰C导航与通信天线共用技术研究

文章简要介绍了罗兰C导航台链的组成和信号格式,针对罗兰C共用短波或超短波频段天线需要解决的问题进行了分析,在理论分析的基础上设计出了具有很好实用价值的天线匹配分配器。     

城市轨道交通网络运营资源共享方法与技术进展

网络条件下的城市轨道交通运营资源共享是一项复杂的工作.本文首先提出了城市轨道交通网络运营资源管理研究的三大主要领域，即前期建设与运营一体化方法与技术、网络环境下的负荷均衡方法与技术，以及基于资源共享的运营组织方法与技术.其次，结合案例分析评述了三大领域中既有的成果与伦敦、东京等典型城市的运营实践案例和经验.第三，针对当前亟需深化研究的资源共享方法与技术领域，以车辆基地为案例，从实践角度调研了部分城市车辆基地共用的案例，提出了其对提高网络运营效率的贡献.最后，研究指出了我国城市轨道交通网络化运行环境下资源共享领域值得深化研究的主要方向.     

Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system

This article explores the effects of perceived green value, perceived green usefulness, perceived pleasure to use, subjective norms and perceived behavioral control on green loyalty to a public bike system. The mediators between perceived green value and green loyalty and a moderator of general attitude toward protecting the natural environment are also discussed. The aim of this research was to understand how to establish green loyalty via the other dimensions based on the sustainable modified technology acceptance model (modified TAM), the theory of planned behavior (TPB), and a moderator. The findings reveal that perceived pleasure to use and subjective norms have the strongest power to influence loyalty for both users and non-users. The implications of this finding are that fun in people’s lives has a strong influence on sustainable continuous use of public bikes, and that subjective norms are more effective for non-users. In addition, environmental attitude has stronger moderating effects for non-users than for users on perceived green usefulness, perceived pleasure and subjective norms. Therefore, governmental policies should promote the attitude of protecting the natural environment, perceptions of pleasure, and subjective norms so as to increase green loyalty to public bike-sharing.     

An analysis of airport–airline vertical relationships with risk sharing contracts under asymmetric information structures

We analyze the double moral hazard problem at the joint venture type airport–airline vertical relationship, where two parties both contribute efforts to the joint venture but neither of them can see the other’s efforts. With the continuous-time stochastic dynamic programming model, we show that by the de-centralized utility maximizations of two parties under very strict conditions, i.e., optimal efforts’ cost being negligible and their risk averse parameters both asymptotically approaching to zero, the vertical contract could be agreed as the optimal sharing rule, which is the linear function of the final state with the slope being the product of their productivity difference and uncertainty (diffusion rate) level index.If both parties’ productivities are same, or the diffusion rate of the underlying process is unity, optimal linear sharing rule do not depend on the final state. If their conditions not dependent on final state are symmetric as well, then risk sharing disappears completely. In numerical examples, we illustrate the complex impact of uncertainty increase and end-of-period load factor improvement on the optimal sharing rule, and the relatively simple impact on total utility levels.     

Electric vehicles in car sharing networks – Challenges and simulation model analysis

In this paper, we examine the operation of electric vehicles in urban car sharing networks. After surveying strategic and operational differences and comparing them to gasoline-fueled cars, a simulation study was carried out. The proposed discrete event simulation tool covered important operational characteristics of electric vehicles, including realistic charging routines. Different vehicle types were compared under various conditions and on multiple markets to determine their performance. The data obtained indicated the competitiveness of electric vehicles in car sharing networks. Key success factors included advantageous relations between the market environment (e.g. electricity and fuel prices) and important characteristics of electric cars (e.g. price and range).     

Analyzing the transition from a public bicycle system to bicycle ownership: A complex relationship

Despite the success achieved by Public Bicycle Sharing Systems (PBSS) across the world, several researchers provide evidence on their limitations and constraints in a medium-long term, and bicycle ownership may be considered as a complementary tool to promote a ’bicycle-culture’. This paper aims to cover the gap about the interaction between both systems (public bicycle/private bicycle) and which are the key aspects to explain the bicycle-buying decision. After a fieldwork based on surveys conducted in Seville (Spain), one of the cities currently acknowledged worldwide for its successful policy of promoting cycling, we apply a Discrete Choice Model. Our findings show that among the socio-demographic factors that favor the move from the PBSS to the private bicycle are: having a higher level of education, being more progressive ideologically-speaking, and being a resident of the city itself; while age and gender do not appear to be conclusive. Experienced users, for whom the bicycle is a part of his/her healthy lifestyle, state a greater willingness to buy a bicycle. And the main obstacles to make the jump from the PBSS to the private bicycle, and that any action plan to support private bicycle usage should take into account, are: the lack of proper parking at the origin/destination, and fear of theft.     

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.