@inproceedings{a69d9b463a6c461e98a5e8cadb3d785b,
title = "A short-term forecast method for highway traffic conditions based on CHMM",
abstract = "Short-term traffic flow forecasting has been the most important application of the intelligent transportation system (ITS). This paper presents a model structure with a coupled hidden Markov model (CHMM) for short-term traffic prediction in the highway system with real-time traffic flows data. Data used in this study was gathered from simulation software. The model defines traffic states in a two-dimensional space with speed and volume observations. The decoding function of CHMM is used in this study to estimate the most likely sequence of traffic states. The forecasting model is accessed by predicting errors. The CHMM is compared to autoregressive moving average (ARIMA), which is one of the most widely used regression techniques. These results present that the CHMM outperforms the regression model. Consequently, the paper concludes that CHMM is more robust and successful in modelling unstable traffic conditions.",
author = "Jiadong Liang and Jianqun Wang and Jingxuan Chen",
note = "Publisher Copyright: {\textcopyright} ASCE; 17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017 ; Conference date: 07-07-2017 Through 09-07-2017",
year = "2018",
doi = "10.1061/9780784480915.065",
language = "English",
series = "CICTP 2017: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation - Proceedings of the 17th COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "633--644",
editor = "Haizhong Wang and Jian Sun and Jian Lu and Lei Zhang and Yu Zhang and ShouEn Fang",
booktitle = "CICTP 2017",
address = "United States",
}