Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation

Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is the possibility of reducing discretionary fuel loading by dispatchers. In this study, we propose a novel discretionary fuel estimation approach that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approach is found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. We apply ensemble learning techniques to improve fuel burn prediction and construct prediction intervals (PIs) to capture the uncertainty of model predictions. The upper bound of a PI can then be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO₂ reduction per year for our study airline. This study also builds a link between discretionary fuel estimation and aviation system predictability in which the proposed models can also be used to predict benefits from reduced fuel loading enabled by improved Air Traffic Management (ATM) targeting on improved system predictability.     

The inherent precision of regression estimated route probabilities

From basic assumptions about independent and consistent driver behaviour, and with data from traffic counts, we derive statistical properties of regression or correlation estimates of route selection probabilities, turning probabilities and travelling times. Our modelling is conditional in a way that avoids most traffic generation problems and permits an asymptotic analysis of the precision under mild assumptions allowing non-stationarity. This allows us to put together non-stationary data from the corresponding time intervals during several days when we aim at high precision estimates.     

基于凝聚函数法的稳态热传导区间反演

考虑观测数据的不确定性,建立了识别稳态热传导边界条件和导热系数的有限元数值反演模型.采用凝聚函数法将此非线性规划问题转化为一个可微的单约束优化问题,在此基础上采用乘子罚函数法求解,获得了反演变量所在区间范围.     

粉煤灰、膨胀剂双掺技术对CRCP路面裂缝间距和宽度的影响

粉煤灰、膨胀剂的掺入使混凝土干缩应变减小,抗弯弹性模量下降,从而可以适当增大连续配筋混凝土路面的裂缝间距并减小缝宽,在缝宽基本不变时,可以减少配筋量。本文研究了粉煤灰、膨胀剂双掺技术对连续配筋混凝土路面裂缝间距和宽度的影响。     

OFDM仿真系统的快速创建方法

阐述OFDM的基本原理，分步骤展示了OFDM仿真番统建立过程，重点提出从仿真OFDM调制与解调技术的基本模型，到仿真全部OFDM相关技术的模型的扩展方法。     

双线区段通过能力及相关因素分析

用数学方法分析和推导双线区段内各种因素对旅客列车和除系数的影响关系，给出旅客列车在多种不同场合下求扣除系数的数学模型和计算公式，设计了求双线区段旅客列车扣除系数和区段通过能力的多项式算法，通过大量实例计算，分析了客货列车速度、追踪列车间隔、旅客列车对数等各种因素对通过能力影响的变化关系，得到了一系列有意义的结果。     

信号控制交叉口绿灯间隔时间的设计方法

采用停车视距原理,阐述了机动车绿灯间隔时间在信号控制交叉口的作用,提出了机动车绿灯间隔时间的计算方法,并通过1个简单算例进行说明。最后,给出了绿灯间隔时间在黄灯和全红之间的分配方式。     

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交通信息集成能够综合分析和利用各种交通信息系统所产生和采集的数据,从而实现对城市交通的综合管理和统一调度。但由于交通信息数据量大且有较强的实时性,并且不同的交通信息对传输性能的要求也不尽相同,因此交通信息传输的QoS要求尤为重要。基于统计分析对交通信息集成的优化是在利用消息总线实现对数据集成,对实时交通信息的传输延时和信息之间的传输相干性进行统计分析,来获取交通信息传输的定量特性,为交通信息传输过程中的消息传输通道、缓冲队列和时间限制等参数设置提供依据,从而优化交通信息的集成,有助于提高实时交通信息的响应时间和处理效率。     

考虑区间重叠的多运营商公交调度优化

为解决多运营商在重叠区间的公交线路调度问题,在分析重叠区间特性的基础上,提出一种双层规划模型.上层模型代表政府机构,目标为使公交乘客总出行时间最小,变量为公交线路分配方案;下层模型代表运营商,目标为各运营商追求自身利润最大化,变量为运营线路的公交车发车间隔.应用NSGA-II算法(Elitist Non-Dominated Sorting Genetic Algorithm) 求解模型,并在长春市南关区北侧区域的公交线网进行案例分析.实验结果表明,优化后网络中公交乘客的总出行时间降低了5.93%,验证了模型的有效性.