航站楼离港旅客托运行李需求预测

发布时间：2018-11-19 08:38

【摘要】：随着我国航空运输业的发展，我国民航旅客运输量快速增长，航站楼内经常发生旅客在值机等区域长时间滞留的现象，造成航站楼内秩序混乱，影响民航运输的服务水平。办理行李托运手续这个环节所消耗的时间是决定离港旅客在航站楼内滞留时间长短的关键因素之一。只有对离港旅客的托运行李需求变化实时准确地把握，才能及时调度分配航站楼离港旅客行李相关服务资源，从而有效地减少旅客在港滞留时间，确保离港旅客快速登机和航班安全正点运行。论文针对航站楼内离港旅客在值机区域办理的托运行李进行了分析研究，选用人工神经网络中的BP神经网络理论，以航班为单位，定量预测航站楼离港旅客的托运行李需求。对此，论文首先通过分析航空运输的特殊性，提出航站楼离港旅客托运行李需求的量化指标和影响因素，，并阐明它们各自作用的机理。然后通过对各种预测方法比较，说明了BP神经网络解决该问题的适用性。通过研究分析有关BP神经网络的理论内容，提出了预测离港旅客托运行李需求的模型理论。最后，通过多次的试验比较，确立了最佳的预测模型网络结构，建立航站楼离港旅客托运行李需求的预测模型，并对其进行了一系列的改进与优化。经过实际案例的分析，验证了该预测模型是科学可行的。通过建立航站楼离港旅客托运行李需求的预测模型，航站楼从而可以科学合理地选择开设离港旅客值机柜台的数量，分配航站楼中为离港旅客服务的工作人员及托运行李相关的设备设施，减少了相关资源的浪费，保障了航站楼内各项作业的正常运行，提高民航运输的服务水平。
[Abstract]:With the development of China's air transportation industry, the volume of civil aviation passenger transport in China is increasing rapidly. The phenomenon of passengers staying for a long time in areas such as check-in often occurs in the terminal building, which results in chaos in the order in the terminal building and affects the service level of civil aviation transportation. The time spent in baggage check-in is one of the key factors in determining the length of stay of departing passengers at the terminal. Only by grasping the changes in the needs of departing passengers' checked baggage in real time and accurately, can we dispatch and distribute the baggage related service resources for departing passengers in the terminal building in a timely manner, thus effectively reducing the stay time of passengers in Hong Kong. Ensure fast boarding of departing passengers and safe operation of flights. This paper analyzes and studies the checked baggage handled by departing passengers in the check-in area in the terminal building, selects the theory of BP neural network in artificial neural network, and quantitatively predicts the check baggage demand of departing passengers in terminal building by taking flight as the unit. Firstly, by analyzing the particularity of air transportation, the paper puts forward the quantitative indexes and influencing factors of the baggage demand of departing passengers in the terminal, and clarifies the mechanism of their respective functions. Then, the applicability of BP neural network to solve this problem is illustrated by comparing various prediction methods. By studying and analyzing the theoretical content of BP neural network, a model theory is proposed to predict the baggage demand of departing passengers. Finally, through many experiments and comparisons, the optimal network structure of forecasting model is established, and the forecasting model of baggage demand of departing passengers in terminal building is established, and a series of improvements and optimizations are made to it. Through the analysis of practical cases, it is proved that the prediction model is scientific and feasible. By establishing a forecasting model of the baggage demand of departing passengers in the terminal building, the terminal can scientifically and reasonably select the number of departure passenger check-in counters. The allocation of departure passenger service staff and baggage related facilities in the terminal can reduce the waste of related resources, ensure the normal operation of all operations in the terminal, and improve the service level of civil aviation transportation.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：V353;F562

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