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
@article{arxiv.1504.01057,
title = {Preprint Virtual Geographic Environment Based Coach Passenger Flow Forecasting},
author = {Zhihan Lv and Xiaoming Li and Jinxing Hu and Ling Yin and Baoyun Zhang and Shengzhong Feng},
journal= {arXiv preprint arXiv:1504.01057},
year = {2015}
}
Comments
This paper has been withdrawn by the author due to a crucial sign error in section 3