arXiv 1504.010572015-04cs.SIphysics.soc-ph

基于预印本虚拟地理环境的客车客流预测

Preprint Virtual Geographic Environment Based Coach Passenger Flow Forecasting

Zhihan Lv, Xiaoming Li, Jinxing Hu, Ling Yin, Baoyun Zhang, Shengzhong Feng

备注：: This paper has been withdrawn by the author due to a crucial sign error in section 3

摘要：

这是我们在2015年IEEE测量系统和应用的计算智能和虚拟环境（CIVEMSA）上的论文的预印本。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中进行深入的研究和应用实例。目前，交通客流预测模型种类繁多，常见的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，无长期变化趋势，因此本研究采用回归预测模型对客车客流进行预测

英文摘要：

This is the preprint version of our paper on 2015 IEEE Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). 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 longterm change trend, so this research adopts regression forecasting model to forecast the coach passenger flow

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。