Name
Abstract | ||
|---|---|---|
This paper proposes alternative approaches for the prediction of short-term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5- and 30-min traffic flow rates. New LGP- and MLP-based prediction equations are derived for the traffic flow rates in the 5- and 30-min time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht-Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP-based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1111/j.1468-0394.2010.00567.x | EXPERT SYSTEMS |
Keywords | Field | DocType |
traffic flow,prediction,genetic programming,artificial neural network,fuzzy logic,formulation | Data mining,Traffic flow,Computational intelligence,Computer science,Fuzzy logic,Genetic programming,Multilayer perceptron,Artificial intelligence,Artificial neural network,Linear genetic programming,Machine learning | Journal |
Volume | Issue | ISSN |
29.0 | 2.0 | 0266-4720 |
Citations | PageRank | References |
6 | 0.93 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shahriar Afandizadeh Zargari | 1 | 6 | 0.93 |
| Salar Zabihi Siabil | 2 | 6 | 0.93 |
| Amir Hossein Alavi | 3 | 1016 | 45.59 |
| Amir Hossein Gandomi | 4 | 1836 | 110.25 |