公路隧道施工智能监测预警系统研发与应用

针对公路隧道监控量测常规工作模式面临的主要问题,结合现代信息化技术手段设计并应用有关系统,通过蓝牙技术采集测量数据,实现自动计算和超限预警,保障数据的实时性、准确性和可靠性,提高公路隧道施工监控量测的工作效率,为保障隧道施工质量和安全提供重要技术手段。     

铁路调度系统通讯传输的数据安全

目前很多信息系统的数据要通过网络进行传输,为提高系统数据的安全性,在分析数据传输存在问题的基础上,论述了数据传输的保护方法,重点介绍了铁路调度系统中采用的不对称加解密算法,并举例说明其在实际中的应用和安全保密作用。     

大数据视角下的铁路运输运营与管理

<正>在大数据时代,铁路运输面临着庞大的数据量和复杂的运营挑战。借助大数据的力量,铁路运输可以实现实时监控与调度、预测性维护和客户个性化服务。本文将探讨大数据视角下铁路运输运营与管理的特征和应用措施,以提升运输效率、安全性和乘客满意度。本文从大数据视角出发,探讨了铁路运输运营与管理的特征和应用措施。包括实时监控与调度、预测性维护以及客户个性化服务。通过数据分析与智能化决策,     

大数据下小微企业的财务管理问题探析

大数据、云会计技术的发展与应用对解决小微企业财务管理困境有重要的影响。文本采用大数据爬虫技术采集了新华网、腾讯、网易等16个网页数据,对小微企业财务管理问题关注研究进行大数据分析。基于大数据视角,从账务管理、税务管理、融资管理、风险控制等角度深入剖析小微企业财务管理面临的记账不规范、未能智能化的税务筹划、征信建设滞后、风险应对能力差等主要问题,以期为改进小微企业财务管理现状及问题提供借鉴。     

通信业务支撑系统的安全保障

通过对中国铁通集团有限公司业务支撑系统的安全风险分析,有针对性地提出网络隔离、音曙入侵检测系统、病毒防护、认证授权、数据存储安全等安全防护策略和措施,有效防止系统数据被盗取或数据丢失,以及各种网络安全威胁,保证应用系统的机密性、完整性和可用性.     

大数据审计证据及其应用风险探讨

随着大数据技术在企业和社会的应用，新的数据环境改变了审计证据的性质、使用和决策过程，大数据审计越来越被重视。本文从审计证据观演进的历史逻辑阐释大数据审计证据的性质、来源、特征及运用大数据审计证据面临数据安全、数据可信度、数据可获得性和数据伦理方面的风险，提出了恰当运用大数据审计证据的建议，以期为充分利用大数据技术开展审计提供有益参考。     

人脸识别技术在道路交通管理中的应用探究

正过去几年里,人脸识别技术取得了长足的发展,在公共安全领域发挥了重要的作用。而随着公安大数据应用背景的出现,人脸识别技术面临更大的挑战。结合人脸识别技术、大数据技术、深度学习技术在城市道路交通管理中的应用,设计针对行人闯红灯的交通管理报警系统及针对失驾人员的交通管控系统,能够有效解决公安交管部门遇到的"难题",对于提高道路交通安全水平、创建和谐交通环境具有重要的意义。     

公铁平交道口技术装备安全评价模型研究

针对我国公铁平交道口安全现状,研究开发了一种基于故障树和事件树分析技术,结合Bow-Tie模型的平交道口技术评价及管理模型。根据模型特点及应用要求,提出采用Bow-Tie拓展模型对平交道口技术装备进行安全评价的方法,并收集某公铁平交道口一定时间段的事故数据,针对该类平交道口的技术装备进行模拟评估,提出相应的预防及减缓措施,取得了较好效果。     

高速铁路灾害监测系统架构及关键技术研究

为了应对新时期高速铁路灾害监测面临的需求与挑战,解决工程实施中遇到的问题,进一步提升高速铁路行车安全保障能力,从信息感知层、网络传输层、数据汇聚层、综合应用层对高速铁路灾害监测系统架构进行再设计,集成整合铁路既有及待建的灾害监测相关系统,统筹考虑系统软硬件资源配置和运用维护工作。同时,提出灾害监测一体化集成、路内外系统协同联动、灾害监测大数据分析、区块链应用等关键技术,探讨高速铁路灾害监测应用场景,促进新技术在高速铁路灾害监测领域的应用,提高既有系统的智能化水平。     

施工支架垮塌事故原因分析及预防措施

近年来,国家出台了一系列施工安全管理办法和措施,对安全事故的重视不断提高,全国安全事故发生率有所下降,但仍有一些地区工程建设面临任务重、施工环境复杂等问题,施工安全管理不到位,安全生产问题仍然比较突出.支架垮塌事故是近年来比较常见的一类安全事故在支架受压时因荷载过大,造成局部失稳垮塌,或支架使用的钢管扣件的爆裂,造成支架整体失稳坍塌.     

多式联运数据交换区块链技术应用研究

针对多式联运数据交换中存在的数据中心化、作业协同效率不高等问题,对铁水联运、公铁联运过程中数据交换需求进行分析,阐述多式联运和区块链技术在数据共享、信任机制、安全机制和合约机制等方面的契合点,提出基于区块链的包括基础层、区块链核心层、应用服务层、应用接口和业务层的总体架构方案,设计基于联盟链的网络架构方式,模拟基于区块链技术的铁水联运运单流转过程,以实现共享数据的去中心化,提高多式联运作业的协同性和安全可靠性,促进多式联运"一单制"建设。     

Cooperatives for fruits and vegetables in emerging countries: Rationalization and impact of decentralization

We develop a (noncooperative) game theoretic model for a decentralized setting wherein fringe farmers compete with a two-tier cooperative network involving network farmers and a coordinator. We examine the roles of the coordinator and profit sharing in allocating costs/benefits of externalities in enhancing network efficiency and stability. Our main finding is that using profit sharing based mechanism the coordinator can overcome inherent inefficiency and instability of decentralization and noncooperative behavior of the network farmers. The roles of the coordinator and profit sharing are particularly important when it is economical for both network and fringe farmers to supply the product.     

Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption

This research analyzes the relationship between bike-sharing and public transit using bike-sharing data collected in Cologne, Germany. The selected system is one of very few in Germany that is organized as a free-floating system, which allows the generation of more detailed data. A construction site in the light rail network causing multiple disruptions in the public transit network offered the possibility to detect changes in bike-sharing usage that occur in the corresponding period. Applying negative binomial regression, spatial and temporal usage patterns are analyzed to identify connections to the public transit network and other factors influencing the usage of bike sharing. The analysis suggests the existence of a spatial relationship between bike-sharing and public transit. Therefore, an intermodal use of both means of transport can be assumed. The short-term changes in the public transit network caused by the construction site only have minor impacts on the usage patterns. Other factors that affect the usage structures could be identified. Proximity to universities as well as the number of certain points of interest nearby, such as food outlets and shops, promote bike-sharing use. Higher temperatures are also positively correlated, while rain reduces usage. The findings of the study can be beneficial to integrate bike-sharing into urban transport systems, especially regarding public transit.     

行包公司信息全程追踪系统的设计与实现

铁路行包快运专业化公司为了提高小件货物运输的服务质量,以VPN为网络平台,利用计算机网络技术,在办理行李包裹业务的同时,将货物的信息和承运信息进行集中管理,实现了在沿途办理站的信息共享及对运输过程的全程追踪,提高了工作效率。     

基于联盟区块链的铁路客运出行服务系统研究

铁路客运出行服务系统需要与多个企业进行数据交互,各主体间存在缺乏信任、数据格式不统一、对账环节低效等问题,制约了铁路客运出行服务的发展。而区块链具有去中心化、不可篡改、全程留痕等特点,在铁路客运出行服务系统中引入区块链技术,有助于解决多方主体交易难题。在总结铁路客运出行服务系统现状的基础上,结合开源分布式账本平台Fabric的特点与优势,设计基于联盟区块链技术的铁路客运出行服务系统架构,并对系统的基本框架和业务流程进行了描述和分析,提出铁路客运出行服务系统中的数据共享、数据存储、数据安全、清分结算等关键技术,可以实现多方数据的可用不可见,提高合作企业参与的积极性,将为加快促进综合交通一站式出行服务平台建设提供研究支撑。     

Identification of land-use characteristics using bicycle sharing data: A deep learning approach

Extensive research has shown that urban land-use characteristics, including resident, work, consumption, transit, etc., are significantly interrelated with travel behaviors and travel demands. Many research efforts have been made to evaluate the impact of land use planning or policies on travel behavior, however, few studies are able to quantitatively measure the land-use characteristics based on the data of travel behaviors or travel demand. In this paper, a new hybrid model that combines time series feature extraction and deep neural network is proposed to identify regional land use characteristics and quantify land use intensity using ridership data of bicycle sharing. This method consists of four main parts: (i) A set of land-use characteristic labels are evaluated based on planning and Geographic Information System (GIS) data. (ii) An ensemble clustering method is used to determine the segmentation points of ridership time series. (iii) The statistical characteristics of the segmented time series are extracted and used as input to the neural network. (iv) A deep neural network is established and trained based on the processed ridership features and land-use labels. In terms of data collection, ridership data of the bicycle-sharing parking spots and land-use planning data are obtained from bicycle-sharing system and planning department in San Francisco Bay Area, California U.S.A., respectively. The test results show that this approach has high accuracy for identifying land-use characteristics based on several standard evaluation measures and that the identification distribution can be well explained. The extension results further prove that the model can be applied to effectively analyze the main land-use characteristics of the region although the identification results may become unstable after 3–4 months.     

Sampling method for monitoring the alternative accommodation market

Tourism is an extremely competitive industry where effective destination management is necessary to compete. One of the main destination management stakeholders is the hotel industry. Since the advent of the Internet websites that facilitate the sharing economy, the hotel industry has had to compete with an alternative accommodation market. This alternative market is difficult to monitor as there is no official data. Current research works on developing methods for calculating tourism metrics for a destination based on digital footprint with the objective of offering figures to complement official statistics. The objective of our research is to develop a method to monitor the alternative accommodation market based on data collected from Airbnb. This paper reports the results of the first step: the design of a sampling method for data scraping from this website that provides a representative sample of the accommodation offer of the Basque Country distributed through it and its prices. The results show that the length-of-stay (LOS) parameter of the queries to the website is key to obtaining a representative sample of the accommodation offered through this channel. A sampling method based on the representative values of LOS inferred from a data sample is proposed.     

A model for taxi pooling with stochastic vehicle travel times

Taxi pooling means the sharing of a taxi by more than one passenger with at least a semi-common route. Currently, a trial-and-error process is adopted, taking into account expected travel times, for taxi pooling, a method which is neither effective nor efficient. This is because stochastic disturbances arising from variations in the taxi travel times that occur in actual traveling are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule can be disturbed enough to lose its optimality. Therefore, in this study, a network flow technique is used to construct a stochastic taxi pooling model incorporating stochastic vehicle travel times. A solution algorithm is also proposed to efficiently solve the problem. The performance of the proposed models and the use of the solution method in practice are evaluated by executing numerical tests using real data with suitable assumptions. The test results show that the stochastic model and the solution method could be useful references for practice.     

The potential of management networks in the innovation and competitiveness of rural tourism: a case study on the Valle del Jerte (Spain)

Since the 1980s, rural tourism has witnessed a significant and generalised growth among the European Union members. However, from the perspective of business management, rural tourism suffers from several obstacles that complicates its competitiveness as a tourism product. By focusing on the Valle del Jerte (Extremadura, Spain) case study, this study analyses a rural tourism business network, from the social network analysis perspective, to critically discuss its potential as a structure that enhances competitiveness and innovation. The results demonstrate that these networking structures contribute towards the creation of a cohesive destination, whereby the sharing of resources enables innovative local responses to the global market challenges.     

An inventory-based simulation model for annual-to-daily temporal freight assignment

In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF³ data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.