Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences

Chang, Hyun‐Kyung; Lee, Y.; Yoon, Boram; Baek, Seungjae

doi:10.1049/iet-its.2011.0123

Cited by 185 publications

(105 citation statements)

References 27 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…Likewise, model complexity and high dependency to a lot of data are the most significant disadvantages of them. Neural networks such as MLP, RBF and TDNN (Bin, 2006, Nagare, 2012, K Nearest Neighborhood (Chang, 2012) and SVM models (Castro-Neto, 2009) are among the most common nonparametric prediction methods that have been used more often for traffic flow prediction.…”

Section: Nonparametric Prediction Methodsmentioning

confidence: 99%

Sensor Data Fusion Using Mutual Information Algorithm

Shabanian¹,

Hosseini²

2015

CeN

View full text Add to dashboard Cite

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

show abstract

Section: Nonparametric Prediction Methodsmentioning

confidence: 99%

Sensor Data Fusion Using Mutual Information Algorithm

Shabanian¹,

Hosseini²

2015

CeN

View full text Add to dashboard Cite

show abstract

“…Artificial neural network (ANN), Moving average, Autoregressive Integrated Moving Average Method, Bayesian networks, Kalman filter and hybrid approaches were used for traffic flow prediction. H. Chang et al proposed traffic flow prediction on multiple intervals using a non parametric Knearest neighbour method [2]. This method requires large amount of historical data to produce satisfactory results.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

Deshpande¹,

Bajaj²

2017

IJCA

View full text Add to dashboard Cite

Short term traffic flow prediction has become one of the important research fields in intelligent transportation system. The prediction of this traffic flow information quickly and accurately is important for traffic control and guidance to initiate the measuring steps well in advance. It makes the transport users better informed and makes the transport network smarter, safer and more coordinated. It plays a crucial role in individual dynamic route guidance, advance traffic information system (ATIS) and advance traffic management system (ATMS). This paper discusses the implementation of traffic flow prediction model using support vector machine. Rough set is used as a post processing tool to validate the prediction result. The objective is to improve traffic flow prediction performance. Data near Perungudi toll plaza in IT corridor in Chennai, India is used for the analysis. It is found that the use of rough set results in satisfactory performance improvement which is evaluated using mean square error as the performance measures.

show abstract

“…Chang et al [13] displayed an element multi interim movement volume expectation model in light of the k-NN nonparametric relapse. The improvement of a piece smoother for the auto relapse capacity to do fleeting movement stream forecast, in which useful estimation procedures were connected.…”

Section: Literature Reviewmentioning

confidence: 99%