A model to identify airport hubs and their importance to tourism in Brazil

Air transportation in Brazil has been recently liberalized and one of the consequences of this process is the concentration of flights in a few hubs. During the years 2006-2007 two fatal accidents created unprecedented chaos in both land and air sides of the system with harmful consequences to tourism in Brazil. The consequences were more airport congestion and many episodes of flight delays and cancellations that lasted for several months. We argue that, among other factors, this state of blackout was a result of the increase in the degree of concentration in few airports, particularly Congonhas (in São Paulo) and Brasília. Using data obtained from a survey with Brazilian experts, a comparison was made with two existing methods (the one used by the US Federal Aviation Administration and the usual Herfindahl-Hirschman method) in order to calculate the number of hubs in Brazil. Due to the huge discrepancy obtained between data from the survey and the other two methods considered, a new mathematical method based on the Herfindahl-Hirschman Index was proposed to identify the number of hubs in a given network. Drawing from the examples of what happened to tourist destinations during and after the air transport crisis in Brazil, the article concludes discussing the need for a more accurate tool to identify and to monitor the concentration of flights at the Brazilian air transportation network and its importance to tourism.     

城乡河网清漂设备发展

研究城乡河网漂浮物的组成,分析了它的产生给人类生活及环境所带来的危害,突出了清漂的必要性,详细介绍了目前我国的清漂技术和设备,针对城乡河网的特点指出清漂设备存在的不足,并提出了综合清漂设备的构思方案.     

A Model to Predict Rolling Force of Finishing Stands with RBF Neural Networks

In view of intrinsic imperfection of traditional models of rolling force, in order to improve the prediction accuracy of rolling force, a new method combining radial basis function（RBF） neural networks with traditional models to predict rolling force was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models.     

考虑关联性的舰艇生命力评估

舰艇各系统之间的关联性一直是舰艇生命力评估中的棘手问题。为了使评估结果更加有效,以更好地指导舰艇生命力设计,采用贝叶斯网络理论构建了考虑关联性的评估模型。在建模过程中,一方面借鉴层次法,以系统性能为节点构建中间层;另一方面系统关联的实际情况,引入有环贝叶斯网络,并通过求解环上节点的极限边缘概率分布进行消环,将有环贝叶斯网络等效为经典贝叶斯网络。最后采用MSBNX软件对模型进行仿真计算,可以有效地评估出电力系统和损管系统对其他系统的关联影响,同时还得出在系统生命力一定的情况下,提高损管能力可以较大幅度提高舰艇生命力。     

U型盾构推进力的计算与应用分析

U型盾构采用移动式盾体进行基坑支护,推进力是设计与应用环节中的重要参数之一。为解决U型盾构推进力的设计问题,以岩土力学和结构力学理论为基础,分析U型盾构在推进过程中推进阻力的产生原因和组成部分,建立U型盾构推进阻力的分项模型,分别对U型盾构推进时的侧部岩土及附加载荷产生的阻力、前部推板的推进阻力、自重摩擦阻力、爬坡阻力、盾尾与管节的摩擦阻力进行理论模型推导,结合地勘及设计参数对各分项阻力进行计算,并对各部分的构成占比进行分析。在实际工程应用中,分别检测统计出2种工况下的总推进力数据,并与理论计算进行比对。结果显示在2种工况下,理论模型的计算数据和实际检测的数据基本相符,表明计算模型的正确性,其分析方法及结果可为类似项目设计提供参考。     

手机信令数据在交通枢纽客流监测中的应用 ——以重庆市为例

为及时、准确地掌握节假日期间综合客运枢纽的出行规律,针对客流数据采集的非实时性 问题,采用手机信令数据研究了交通枢纽客流监测方法。以重庆北客运枢纽为例,根据枢纽和基站的分布特性划分了监测范围,采用手机信令数据实现了在站客流、全日客流和集散时间的实时 监测,并分析了不同节假日期间交通枢纽的运行特征。最后,通过交通枢纽的历史客流数据对本文提出的监测指标进行了验证。结果显示,利用手机信令数据监测的交通枢纽客流量准确率达 90%以上,可用于综合客运枢纽的客流实时监测和预警。     

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.     

Optimization of mixed cycle length traffic signals

Mixed cycle length operation has been recommended for networks where individual intersections process considerably different traffic volumes. The signals to operate at lower or higher cycle lengths are determined heuristically. This paper demonstrates that the use of mixed cycle lengths as given by the heuristic is inferior to operation under a common cycle length. This contradicts findings in earlier studies, and the difference in conclusion is due to the use of updated optimization methodology. A procedure for incorporating the allocation of mixed cycle lengths into the global optimization of all signal timing variables by a genetic algorithm is proposed. The mixed cycle length timing plans obtained from this procedure are an improvement over those determined heuristically. Mixed cycle length operation is found to be of a more limited application than indicated in previous studies. Copyright © 2012 John Wiley & Sons, Ltd.     

A behavioural comparison of route choice on metro networks: Time,transfers, crowding,topology and socio-demographics

Understanding travellers’ behaviour is key element in transportation planning. This article presents a route choice model for metro networks that considers different time components as well as variables related to the transferring experience, train crowding, network topology and socio-demographic characteristics. The route choice model is applied to the London Underground and Santiago Metro networks, to make a comparison of the decision making process of the users on both cities. As all the variables are statistically significant, it is possible to affirm that public transport users take into account a wide variety of elements when choosing routes. While in London the travellers prefer to spend time walking, in Santiago is preferable to spend time waiting. Santiago Metro users are more willing to travel in crowded trains than London Underground users. Both user groups have a similar dispreference to transfers after controlling for the time spent on transfer, but different attitudes to ascending and descending transfers. Topological factors presented on a distorted Metro map are more important than actual topology to passengers’ route choice decisions.     

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.