基于虚拟地理环境的客车客流预测

Zhihan Lv, Xiaoming Li, Jinxing Hu, Ling Yin, Baoyun Zhang, Shengzhong Feng
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引用次数: 16

摘要

缺乏对多种实时动态交通信息的综合分析和可视化显示。本研究在此基础上提出了在虚拟地理环境下的深入研究和应用实例。目前,交通客流预测模型有很多种,常用的模型有回归预测模型和时间序列预测模型。客车客流具有较强的规律性和稳定性,没有长期的变化趋势,因此本研究采用回归预测模型对客车客流进行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual geographic environment based coach passenger flow forecasting
There are lacks of integrated analysis and visual display of multiple real-time dynamic traffic information. This research proposed a deep research and application examples on this basis which is conducted in virtual geographic environment. Currently, there are many kinds of traffic passenger flow forecasting models, and the common models include regression forecasting model and time series prediction model. The coach passenger flow shows strong regularity and stability without long-term change trend, so this research adopts regression forecasting model to forecast the coach passenger flow.
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