Volume List  / Volume 8 (3)



DOI: 10.7708/ijtte.2018.8(3).02

8 / 3 / 271-281 Pages


Villuri Mahalakshmi Naidu - National Institute of Technology, Warangal, India -

Chekuri Siva Rama Krishna Prasad - National Institute of Technology, Warangal, India -

Manchikanti Srinivas - GVP College of Engineering (Autonomous),Visakhapatnam, India -

Praveen Sagar - GVP College of Engineering (Autonomous),Visakhapatnam, India -


Trip rate is one of the transportation planning parameter. It is difficult to relate the amount of trips that originate in a study area and the amount of trips attracted towards that study area by conducting surveys regularly. The cost and time for each survey is not affordable. So considering Travel parameters and Land-use Parameters of an area relationship is established using Artificial Neural Network (ANN) against Trip Rates of that area. Involving more number of parameters has made computations to increase the complexity in analysis. So the data has been reduced in dimension using Principal Component Analysis and then Processed in an Artificial Neural Network. The original input data along with principal components (6PC, 5PC, 4PC and 3PC) data as input to the Artificial Neural Network (ANN) has been processed separately. The analysis has shown that 6PC as input to Artificial Neural Network(ANN) is yielding better explanation between independent and dependent variables (Trip Rate(all modes) and Trip Rate(motorised)).

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Aggarwal, R.; Kumar, R. 2015. Effect of training functions of Artificial Neural Networks (ANN) on time series forecasting, International Journal of Computer Applications 109(3): 14-17.


Burns, J.A.; Whitesides, G.M. 1993. Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition, Chemical Reviews 93(8): 2583-2601.


Gevrey, M.; Dimopoulos, I.; Lek, S. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological modeling 160(3): 249-264.


Kadali, B.R.; Rathi, N.; Perumal, V. 2014. Evaluation of pedestrian mid-block road crossing behaviour using artificial neural network, Journal of traffic and transportation engineering (English edition) 1(2): 111-119.


Lyons, G.; Hunt, J.; McLeod, F. 2001. A neural network model for enhanced operation of midblock signalled pedestrian crossings, European journal of operational research 129(2): 346-354.


Manage, A.B.; Scariano, S.M. 2013. An introductory application of principal components to cricket data, Journal of Statistics Education 21(3): 1-22.


Paul, L.C.; Suman, A.A.; Sultan, N. 2013. Methodological analysis of principal component analysis (PCA) method, International Journal of Computational Engineering & Management 16(2): 32-38.


Pham, D.T.; Sagiroglu, S. 2001. Training multilayered perceptrons for pattern recognition: a comparative study of four training algorithms, International Journal of Machine Tools and Manufacture 41(3): 419-430.


Sharma, B.; Venugopalan, K. 2014. Comparison of neural network training functions for hematoma classification in brain CT images, IOSR- Journal of Computer Engineering 16(1): 31-35.

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