南京地铁网络化运营票价方案研究

分析了国内外地铁主要票制情况，探讨了票价制定过程中需考虑的因素，回顾了单线运营情况下南京地铁票价的制定原则、票制选择、票价方案及执行情况，提出了网络化运营情况下票价的制定原则及票价方案，总结出单线运营与网络化运营票价制定过程中需注意的关键问题。     

质的方法在高职院校研究性教育活动中的应用思考——以基层教学单位教研活动为例

质的研究方法是一种通过在自然情境下的双向互动，对研究现象进行整体性探究、继而形成解释性理解的方法。在高职院校，基层教学单位借助该种方法开展教研活动，能够提升教学实践的效果，有助于锻炼教师的教研能力、推进院本教研模式的创新。     

GSM-R中FAS话务量计算

介绍了GSM-R系统的结构,FAS系统及子系统在整个网络中的位置以及系统话务量的计算方法.分别按不同站型计算各站话务量,以此来确定网络的组网方式.     

浅议实践教学管理与改革

分析了我校实践教学管理中存在的问题,提出了深化实践教学管理和改革的具体措施。     

内容中心网络系统及其在船联网中的应用

文章根据信息技术发展趋势,提出将内容中心网络与现有技术结合,建设新的船基信息网络的方法。介绍了内容中心网络的系统结构与工作原理,并以船基数据传输系统为研究对象,建立了相应仿真模型。通过分析网络的内容缓存命中率、内容服务器负载、系统传输速度与完成一次数据传输所需经过的路由跳数等性能评价指标,指出了内容中心网络相比于IP网络等现有网络的优点,并验证将该技术应用在船联网中的优势与可行性。     

基于混沌理论的高速公路网短时交通流量预测研究

随着高速公路网的建成及其交通流量的不断增大,对高速路网交通流实时控制和诱导服务的需求日益突出,而高速公路网短时交通流量的预测,不仅是交通流实时控制和诱导服务的基础和依据,而且预测结果的准确性对改善高速公路网的通行能力和服务水平有重要影响。基于混沌时间序列分析和预测的理论,建立了高速公路网短时交通流预测模型,计算给定区域高速公路网多断面短时交通流量预测值,结果表明利用多维混沌时间序列法预测高速公路网短时交通流量可行且具有较高的精度。     

Modeling capacity flexibility of transportation networks

Flexibility of the transportation system is one of the important performance measures needed to deal with demand changes. In this paper, we provide a quantitative assessment of capacity flexibility for the passenger transportation network using bi-level network capacity models. Two approaches for assessing the value of capacity flexibility are proposed. One approach is based on the concept of reserve capacity, which reflects the flexibility with respect to changes in terms of demand volume only. The second approach allows for variations in the demand pattern in addition to changes in demand volume in order to more fully capture demand changes. Two models are developed in the second approach to consider two types of capacity flexibility. The total capacity flexibility allows all users to have both route choice and destination choice when estimating capacity flexibility. The limited capacity flexibility estimates how much more demand volume could be added to a fixed demand pattern by allowing the additional demand to deviate from the fixed demand pattern. Numerical examples are provided to demonstrate the different concepts of capacity flexibility for a passenger transportation system under demand changes.     

A topological route choice model for metro

This article presents a route choice model for public transit networks that incorporates variables related to network topology, complementing those found in traditional models based on service levels (travel time, cost, transfers, etc.) and users’ socioeconomic and demographic characteristics (income level, trip purpose, etc.). The topological variables represent concepts such as the directness of the chosen route and user knowledge of the network. For both of these factors, the necessary data is endogenous to the modelling process and can be quantified without the need for information-gathering beyond what is normally required for building route choice models. Other novel variables in the proposed formulation capture notions of user comfort such as vehicle occupancy rates and certain physical characteristics of network stations. We conclude that these new variables significantly improve the explanatory and predictive ability of existing route choice specifications.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.     

On the fundamental diagram and supply curves for congested urban networks

Macroscopic fundamental diagrams (MFD) of traffic for some networks have been shown to have similar shape to those for single links. They have erroneously been used to help estimate the level of travel in congested networks. We argue that supply curves, which track vehicles in their passage through congested networks, are needed for this purpose, and that they differ from the performance curves generated from MFD. We use a microsimulation model, DRACULA and two networks, one synthesizing the network for Cambridge, England, and one of the city of York, England, to explore the nature of performance curves and supply curves under differing patterns of demand.We show that supply curves differ from performance curves once the onset of congestion is reached, and that the incorrect use of performance curves to estimate demand can thus seriously underestimate traffic levels, the costs of congestion, and the value of congestion relief measures. We also show that network aggregated supply curves are sensitive to the temporal distribution of demand and, potentially, to the spatial distribution of demand. The shape of the supply curve also differs between origin–destination movements within a given network.We argue that supply curves for higher levels of demand cannot be observed in normal traffic conditions, and specify ways in which they can be determined from microsimulation and, potentially, by extrapolating observed data. We discuss the implications of these findings for conventional modelling of network management policies, and for these policies themselves.