Volume List  / Volume 6 (3)

Article

MULTIMODAL CHOICE MODELING USING RANDOM FOREST DECISION TREES

DOI: 10.7708/ijtte.2016.6(3).10


6 / 3 / 356-367 Pages

Author(s)

Ch.Ravi Sekhar - Transportation Planning Division, Central Road Research Institute, New Delhi 10025, India -

Minal - Transportation Planning Division, Central Road Research Institute, New Delhi 10025, India -

Errampalli Madhu - Transportation Planning Division, Central Road Research Institute, New Delhi 10025, India -


Abstract

Mode choice analysis forms an integral part of transportation planning process as it gives a complete insight to the mode choice preferences of the commuters and is also used as an instrument for evaluation of introduction of new transport systems. Mode choice analysis involves the procedure to study the factors in decision making process of the commuter while choosing the mode that renders highest utility to them. This study aims at modeling the mode choice behaviour of commuters in Delhi by considering Random Forest (RF) Decision Tree (DT) method. The RF model is one of the most efficient DT methods for solving classification problems. For the purpose of model development, about 5000 stratified household samples were collected in Delhi through household interview survey. A comparative evaluation has been carried out between traditional Multinomial Logit (MNL) model and DT model to demonstrate the suitableness of RF models in mode choice modeling. From the result, it was observed that model developed by Random Forest based DT model is the superior one with higher prediction accuracy (98.96%) than the Logit model prediction accuracy (77.31%).


Download Article

Number of downloads: 428


References:

Ali, J.; Khan, R.; Ahmad, N.; Maqsood, I. 2012. Random Forests and Decision Trees, International Journal of Computer Science Issues, 9(5): 272-278.

 

Ben-Akiva, M.E.; Lerman, S.R. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, Massachusetts, USA.

 

Black, W.R. 1995. Spatial interaction modeling using artificial neural networks, Journal of Transport Geography, 3(3): 159-166.

 

Breiman, L. 1999. Random Forests-Random Features. Available from Internet: http://oz.berkeley.edu/users/breiman/randomforest2001.pdf.

 

Breiman, L. 2001. Random forests, Machine Learning, 45: 5-32.

 

Breiman, L. 2002. Manual on setting up, using, and understanding random forests v3. 1. Statistics Department University of California Berkeley, CA, USA.

 

Chen, X.; Liu, X.; Li, F. 2013. Comparative study on mode split discrete choice models, Journal of Modern Transportation, 21(4): 266-272.

 

Cybenko, G. 1989. Approximation by Superposition of a Sigmoid Function, Mathematics of Control Signals Systems, 2: 303-314.

 

Dougherty, M. 1995. A review of neural Networks applied to transport, Transportation Research Part C: Emerging Technologies, 3(4): 247-260.

 

Haleem, K.; Abdel-Aty, M.; Santos, J. 2010. Multiple Applications of Multivariate Adaptive Regression Splines Technique to Predict Rear-End Crashes at Unsignalized Intersections, Transportation Research Record: Journal of the Transportation Research Board, 2165: 33-41.

 

Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. 2009. The WEKA Data Mining Software: An Update, SIGKDD Explorations, 11(1): 10-18.

 

Hasegawa, H.; Naito, T.; Arimura, M.; Tamura, T. 2012. Modal choice analysis using ensemble learning methods, Journal of Japan Society of Civil Engineering, 68(5): 773-780.

 

Hasegawa, H.; Naito, T.; Arimura, M.; Tamura, T. 2013. Hybrid Model of Random Forests and Genetic Algorithms for Commute Mode Choice Analysis. In Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 9.

 

Hensher, D.A.; Ton, T. 2000. TRESIS: A transportation, land use and environmental strategy impact simulator for urban areas, Transportation, 29(4): 439-457.

 

Hornik, K.S. 1989. Multilayer feed forward Networks are Universal Approximators, Neural Networks, 2(5): 359-366.

 

Hossain, M.; Muromachi, Y. 2011. Understanding Crash Mechanisms and Selecting Interventions to Mitigate Real-Time Hazards on Urban Expressways, Transportation Research Record: Journal of the Transportation Research Record, 2213: 53-62.

 

Karlaftis, M.G.; Golias, I. 2001. An International Comparative Study of Self-Reported Driver Behavior, Transportation Research Part F: Traffic Psychology and Behaviour, 4(4): 243-256.

 

Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of International Joint Conference on AI, 1137-1145.

 

Landis, J.R.; Koch, G.G. 1977. The measurement of observer agreement for categorical data, Biometrics, 33(1): 159-74.

 

Nijkamp, P.; Reggiani, A.; Tritapepe, T. 1996. Modeling inter-urban transport flows in Italy: A comparison between Neural Network analysis and logit analysis, Transportation Research Part C: Emerging Technologies, 4(6): 323-338.

 

Pande, A.; Das, A.; Abdel-Aty, M.; Hassan, H. 2011. Estimation of Real-Time Crash Risk, Transportation Research Record: Journal of the Transportation Research Record, 2237: 60-66.

 

SubbaRao, P.V. 1998. Another insight into artificial neural networks through behavioural analysis of access mode choice, Computers, Environment and Urban Systems, 22(5): 485-496.

 

Xie, C.; Lu, J.; Parkany, E. 2003. Work Travel Mode Choice Modeling Using Data Mining: Decision Trees And Neural Networks, Transportation Research Record: Journal of the Transportation Research Record, 1854: 50-61.

 

Zhang, G.; Patuwo, B.E.; Hu, M.Y. 1998. Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting, 14(1): 35-62.


Quoted IJTTE Works



Related Keywords