基于ZigBee协议的列车监控图像传输系统的设计

提出一种基于无线网络传输协议—ZigBee的列车监控图像传输系统。在车厢内部设置多个摄像头用于采集高清晰的监控图像并通过无线网络将图像实时地传送至列车监控中心。列车长在列车监控中心可实时掌握车厢内的情况,以便指挥及调度列车运行。     

浅析京沪高铁北京CTC中心网络安全

随着分散自律调度集中(CTC)系统运用于京沪高速铁路,京沪CTC中心网络安全成为保障京沪高铁行车安全的一个重要环节。对CTC中心网络安全进行风险识别与分析。从安全管理和安全技术2个方面论证,总结了解决京沪CTC中心网络安全风险的途径。实践证明,采用安全管理和安全技术相结合的策略,有效规避了京沪高铁北京局CTC中心潜在的内、外网络风险,保证了系统网络高安全性。     

香港地铁自动售检票系统运营管理分析

通过赴香港地铁考察的经历,基于香港地铁AFC自动售检票系统多年运营的经验,分析香港地铁的换乘模式、设备个性化以及票务管理模式。着重从人性化细节服务的设置情况,分析香港地铁一切以乘客为中心的宗旨,并阐述ERP在香港地铁的成功应用。     

旧水泥混凝土路面FWD实测数据的分析

基于某国道的FWD实测数据,分析FWD参数与旧水泥混凝土路面状况(现场调查实录及测试数据)间的某些相关性,探索旧水泥混凝土板的接缝传荷能力、弯曲刚度、综合承载能力等定性或定量的判别方法,提出利用FWD加载中心弯沉值总水平评价旧水泥混凝土路面综合承载能力的设想。     

浅谈FAS双中心建设及设备维护

重点分析了西安局集团公司由2K数调主系统组网更新改造为FAS双中心网络的建设方法,并根据日常维护经验,列举了调度台通道故障处理、FASB简易故障判断及计表倒换试验、FASA和FASB系统与MSC之间的业务故障处理等.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

基于虚拟化云技术的路局中心地震预警监测系统方案及联动研究

通过研究铁路地震预警监测系统现状和建设趋势,从资源共享、重复性建设、地震联动触发信号系统停车、牵引供电系统断电等多角度分析存在的问题,结合实际需求,提出铁路局中心系统采用虚拟化云技术的建设思路,并提出地震预警监测系统通过路局中心系统与信号RBC、牵引供电中心接口的联动触发策略。文中论述了采用虚拟化云技术搭建的地震预警监测铁路局中心系统功能、系统构成、硬件配置,从而解决各条铁路接入路局中心增加大量服务器等所带来的资源浪费严重、重复性建设等问题,为建设项目节省投资;阐述了通过路局中心系统与信号RBC、牵引供电中心接口的联动触发方案,为安全提供保障的同时尽可能节省建设投资和维护成本,更好地为铁路安全保驾护航。     

基于产业转移的中国汽车产业战略研究

通过对世界汽车产业四次转移的阐述,分析了世界汽车产业转移的原因与特点,认为我国面临着承接世界汽车产业第四次转移的历史性机遇,提出我国汽车产业发展的战略目标：利用20年左右时间,把我国建设成为继美、欧、日之后的第四个世界汽车产业中心。     

A two-phase model for dry port location with an application to the port of Dalian in China

This paper provides guidance for an optimal and reasonable dry port layout for the port of Dalian in China. We present a two-phase framework on the location of dry ports, which solves the selection of candidate inland cities and optimal dry port location choice, respectively. Fuzzy C-Means Clustering is applied to select alternative cities in the vast hinterland of the seaport of Dalian, with a view to identify evaluation factors that affect the location selection decision. A cost-minimisation linear programming solution is proposed, with the aid of a genetic algorithm, to choose the optimal location as well as capacity level among the candidate inland cities.