公交路线客流动态二维可视化智能系统

Y. Matseliukh, M. Bublyk, V. Vysotska
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引用次数: 0

摘要

为了增加公共交通对城市居民的吸引力,为运输公司开发了一款软件产品,通过可视化乘客交通,有助于提高城市内公共交通服务的质量。本文分析了现有和当前的科学发展和文献来源,展示了大量不同算法和方法的优缺点,以及解决公共路线客流二维可视化问题的途径和方法。研究的结果是,在评估客运服务质量所涉及的因素和标准之间建立了稳定的联系。对所设计的系统进行了系统分析,并给出了客流二维可视化智能系统的结构实例。分析了系统与外部世界基本要素的联系。对于可视化表示,使用变量、类、序列、状态和活动的图表是根据UML符号创建的。我们已经创建了自己独特的算法,用于以两种不同的模式显示可视化:示意图和“在地图上”。在“地图上”模式下,考虑到世界地理坐标的绝对值,成功地将运输单元在路线上的运动数据计算方法应用于屏幕上的二维可视化。这避免了计算中不必要的错误和不准确。开发了一种使用RMSprop学习算法运行的人工神经网络。人工神经网络预测在调整路线上运输单元的调度时客流量值的变化情况。所得结果可以形成并证实,为了更有效地利用高峰时段的比赛,可以改变路线上运行车辆的时间表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent system of passenger flows dynamic 2D-visualization for public transport routes
In order to increase the attractiveness of public transport for urban residents, a software product has been created for transport companies that, by visualizing passenger traffic, helps to improve the quality of public transport services provided within the city. The paper analyses existing and current scientific developments and literature sources, which show the advantages and disadvantages of a large number of different algorithms and methods, approaches, and methods for solving problems of 2D- visualization of passenger flows on public routes. As a result of the research, stable connections have been established between the factors and criteria involved in assessing the quality of passenger transport services. The system analysis of the designed system is executed, and examples of the structure of an intelligent system of 2D visualization of passenger flows are created. The connections of the system with the essential elements of the external world are analysed. For a visual representation, diagrams of usage variants, classes, sequences, states, and activities are created according to UML notation. Our own unique algorithms have been created for displaying visualizations in two different modes: schematic and “on the map”. In the “on the map” mode, a method of calculating data on the movement of transport units on the route was successfully applied for 2D visualization on the screen, taking into account the absolute values of geographical coordinates in the world. This avoids unnecessary errors and inaccuracies in the calculations. An artificial neural network has been developed that operates using the RMSprop learning algorithm. The artificial neural network predicts how the values of passenger traffic will change when adjusting the schedule of the transport unit on the route. The obtained results make it possible to form and substantiate the expediency of changing the schedule of the vehicle running on the route in order to make more efficient use of races during peak times.
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