Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand

This paper proposes a novel short/medium-term prediction method for aviation emissions distribution in en route airspace. An en route traffic demand model characterizing both the dynamics and the fluctuation of the actual traffic demand is developed, based on which the variation and the uncertainty of the short/medium-term traffic growth are predicted. Building on the demand forecast the Boeing Fuel Flow Method 2 is applied to estimate the fuel consumption and the resulting aviation emissions in the en route airspace. Based on the traffic demand prediction and the en route emissions estimation, an aviation emissions prediction model is built, which can be used to forecast the generation of en route emissions with uncertainty limits. The developed method is applied to a real data set from Hefei Area Control Center for the en route emission prediction in the next 5 years, with time granularities of both months and years. To validate the uncertainty limits associated with the emission prediction, this paper also presents the prediction results based on future traffic demand derived from the regression model widely adopted by FAA and Eurocontrol. The analysis of the case study shows that the proposed method can characterize well the dynamics and the fluctuation of the en route emissions, thereby providing satisfactory prediction results with appropriate uncertainty limits. The prediction results show a gradual growth at an average annual rate of 7.74%, and the monthly prediction results reveal distinct fluctuation patterns in the growth.     

A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.     

北京奥运智能交通管理系统建设与应用

奥运交通组织与安全保障,对北京奥运会成功举行起着至关重要的作用。在保证城市交通正常运行的同时,完成繁重、复杂、高标准的奥运交通组织,必须拥有一个功能强大的技术支撑系统——奥运智能交通管理系统。本文介绍了北京奥运智能交通系统的建设与应用情况。     

基于TDM的出租车供需调控方案研究

交通需求管理（TDM）是能从根本上解决城市交通问题的策略,具有现实的意义。针对成都市出租车供需现状,运用系统的思想对各供需调控方案进行评价比选,分析交通需求管理的必要性并提出针对出租车的交通需求管理的具体措施,可为解决出租车的供需矛盾提供参考。     

成都市道路交通安全分级规划方法

交通安全管理是个多层次的分级管理系统与层次化行为综合体,每一级都是个完整的系统,都有自己的控制目标和特点,而国内外交通安全管理规划往往忽视这一点,造成对策普遍存在非完美性、非灵活性和强制性等缺点。根据系统的等级性、层次性等机理,采用“物理-事理-人理”系统方法,将交通安全系统划分为“四维”体系：时间维、空间维、功能维与行为维。基于此划分,按照交通安全规划、设计、管理、维护与评价以及交通事故预防、交通安全保障、交通事故救援与交通安全管理评价两条主线对交通安全进行分级规划,并以成都市交通安全为对象进行分级规划方案设计,期望以此方法来实现交通安全有序化管理与有效管理。     

行车综合自动化系统联动的工程实现方法

将列车自动监控系统(ATS)、电力监控系统(PSCADA)、环境与设备监控系统(BAS)等深度集成为行车综合自动化系统,使得联动的实现变得更加方便.论述基于北京地铁6号线行车综合自动化系统联动的实现原理、功能设计方法和工程实现方案,说明使用联动功能矩阵的设计方法有利于工程实施、软件开发和工程归档,介绍这种方法在北京地铁6号线得到成功应用的情况.     

基于自适应神经模糊推理的交通状态判别方法

文章针对交通状态具有模糊性和主观性的特点,建立能够真实反映人对交通拥塞程度感觉的自适应-神经模糊推理系统,使具有变化连续的交通流参数模糊化处理,实现了道路交通状态的准确、快速辨别。     

带时间窗的混和车辆路径问题的优化算法研究

文章针对带时间窗约束的混合车辆路径问题的特点,建立了带时间窗的混合车辆路径问题的数学模型,并设计了变邻域禁忌搜索算法对该问题进行求解。通过标准算例测试及与现有文献计算结果的比较,验证了该算法的有效性。     

推移质间相互作用力研究

沉降性浆体中固体颗粒处于滑跳移运动状态时,固体颗粒将受到摩擦力的影响。推移质的整体摩擦力等于推移质间剪切力和推移质与管壁间摩擦力的和。任意推移质的剪切力与固体颗粒的体积分数成比例,由力的平衡方程可以推导出其比例系数。