利用移动电话开展铁路购票电子商务的研究

提出利用移动电话和银行卡绑定,通过移动电话和铁路客运服务网络、金融网络的交互,实现铁路旅客购票电子化服务的方法.介绍铁路电话购票系统的网络及硬件结构,以及系统处理流程和操作处理流程.     

铁路分局DMIS及其常见故障的分析与维护

介绍了铁路分局DMIS的结构和功能,对DMIS的故障进行了分类统计和分析,并给出了维护策略.     

无线场强测试系统的软件结构设计与实现

无线场强测试系统的软件结构设计,是各种测试功能是否实现的关键,通过合理的软件设计,将无线通信技术、计算机技术、GPS卫星定位技术、传感器技术、USB总线通信技术和信号分析等技术实现综合运用,并将计算机软硬件技术、数据采集存储技术、单片机编程技术,有机地融合于一体.     

客运服务综合控制系统的设计与实现

分析车站客运信息发布的现状,提出客运服务信息综合控制系统的设计目标,从功能、网络和软件结构等方面介绍系统的构成,描述系统实现过程中所采用的技术,最后对系统的技术特性、应用情况进行总结.     

刍议海事行政执法工作中妨害公务罪的适用性

近年来,非法干扰、抗拒海事执法的现象层出不穷。如何在行政执法过程中达到有理有利有节,是摆在众多海事公务人员面前的一个重大课题。笔者结合海事行政执法工作实践,就如何正确理解妨害公务罪,打击妨害海事依法执行公务的行为、防止海事公务人员野蛮执法,谈几点看法。     

Feeder-trunk or direct lines? Economies of density,transfer costs and transit structure in an urban context

A feeder-trunk scheme has been labeled as superior in urban areas due to the presence of economies of density (decreasing average operating cost) along the avenues served by trunk lines. We compare this structure against three types of direct lines structures (no transfers) to serve a stylized public transport network where several flows converge into a main avenue, simultaneously optimizing fleet and vehicle sizes considering both users’ and operators’ costs. The best structure is shown to depend not only on the total passenger volume but also on demand imbalance, demand dispersion in the origins and the length of the trunk line. The region where the feeder-trunk structure dominates depends largely on the value assigned to the pure transfer penalty.     

Changes concerning commute traffic distribution on a road network following the occurrence of a natural disaster – The example of a flood in the Mazovian Voivodeship (Eastern Poland)

The purpose of this article is to determine the size and spatial structure of changes in traffic density within the regional road network following an occurrence of a flood in the Mazovian Voivodeship, Poland. The use of the application developed for the purpose of this article – offers a possibility to react accordingly when there are non-typical obstructions (here: a flood). On the basis of the conducted study, it has been stated that the greatest changes in vehicle traffic density (the analysis of commute traffic) regard the capital of Mazovian Voivodeship, which – first of all – stems from the fact that it is Warsaw that the largest number of employees commute to. Secondly, it is influenced by the location of the capital city in relation to the river system. In the case of the analysed voivodeship and in ‘normal’ circumstances (no flood), commuting to work remains approximately within the 160-min isochrone. In the second variant, this time would extend nearly eightfold, and in the remaining scenarios fivefold. As far as ‘normal’ circumstances (no flood) and commuting in the Mazovian Voivodeship are concerned, the greatest load refers in particular to the following road classes: main road of accelerated traffic, main road and cumulative road. In this case, express and motorways play a marginal role. On the other hand, in the remaining scenarios, the importance of the class of main road of accelerated traffic decreases at the expense of the classes of main road and cumulative road.     

浅谈计算机GIS在交通工程中的应用

介绍了GIS的基本功能及在交通工程中的应用情况。实际工作中,采用GIS会极大地提升效率,实现智能化交通。     

A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network

Most previous work in addressing the adaptive routing problem in stochastic and time-dependent (STD) network has been focusing on developing parametric models to reflect the network dynamics and designing efficient algorithms to solve these models. However, strong assumptions need to be made in the models and some algorithms also suffer from the curse of dimensionality. In this paper, we examine the application of Reinforcement Learning as a non-parametric model-free method to solve the problem. Both the online Q learning method for discrete state space and the offline fitted Q iteration algorithm for continuous state space are discussed. With a small case study on a mid-sized network, we demonstrate the significant advantages of using Reinforcement Learning to solve for the optimal routing policy over traditional stochastic dynamic programming method. And the fitted Q iteration algorithm combined with tree-based function approximation is shown to outperform other methods especially during peak demand periods.