How to Provide Accurate and Robust Traffic Forecasts Practically?

Zhang, Yang

doi:10.5772/27741

Cited by 5 publications

(2 citation statements)

References 127 publications

(102 reference statements)

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“…The hybrid prediction models typically have high accuracy in most different traffic conditions and with any kind of data; structure flexibility is their other characteristic (Zhang, 2012). Different types of simple parametric and nonparametric prediction models can be combined using various hybrid algorithms to develop a new hybrid model.…”

Section: The Combination Of Modelsmentioning

confidence: 99%

Sensor Data Fusion Using Mutual Information Algorithm

Shabanian¹,

Hosseini²

2015

CeN

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Traffic flow prediction is one of the congestion avoidance methods in highways. According to previous studies, no comprehensive model has been proposed for traffic flow prediction which can prevent congestion in many different traffic conditions. Using data fusion to reduce prediction error is an interesting idea to solve this problem. In this paper, a new hybrid algorithm based on mutual informat ion for traffic flow prediction will be proposed and compared with various types of previous hybrid algorithms and predictors. The Mutual Information (MI) algorithm is used to calculate the interdependency of data, so we expect this new hybrid algorithm to have high precisio n in comparison with others. Simulations will be implemented based on real data in MATLAB environment

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Section: The Combination Of Modelsmentioning

confidence: 99%

Sensor Data Fusion Using Mutual Information Algorithm

Shabanian¹,

Hosseini²

2015

CeN

View full text Add to dashboard Cite

show abstract

“…Traffic flow is a macroscopic feature of traffic and is a real-time, completely non-linear, high-dimensional and non-stationary stochastic process [2]. Traffic flow forecasting is mainly classified into long-term and short-term prediction.…”

Section: Literature Reviewmentioning

confidence: 99%