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Mode Choice Modeling Using Artificial Neural Networks by Praveen Mode Choice Modeling Using Artificial Neural Networks by Praveen Kumar Edara Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering Dušan Teodorović, Chair John Collura Antonio Trani October 2003 Falls Church, Virginia Key words: Mode choice, Neural networks, Multinomial logit, Linear regression, Clustering, American travel survey database Mode Choice Modeling Using Artificial Neural Networks Praveen Edara ABSTRACT Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data become available. The primary goal of this thesis is to develop mode choice models using artificial neural networks and compare the results with traditional mode choice models like the multinomial logit model and linear regression method. The data used for this modeling is extracted from the American Travel Survey data. Data mining procedures like clustering are used to process the extracted data. The results of three models are compared based on residuals and error criteria. It is found that neural network approach produces the best results for the chosen set of explanatory variables. The possible reasons for such results are identified and explained to the extent possible. The three major objectives of this thesis are to: present an approach to handle the data from a survey database, address the mode choice problem using artificial neural networks, and compare the results of this approach with the results of traditional models vis-à-vis logit model and linear regression approach. The results of this research work should encourage more transportation researchers and professionals to consider artificial intelligence tools for solving transportation planning problems. ACKNOWLEDGEMENTS I would like to thank my advisor, Dr. Dušan Teodorović, for all the encouragement and ideas he has provided during my research. I am greatly indebted to Dr. Antonio Trani and Dr. John Collura for being on my thesis advisory committee and for their support and assistance. In addition, I thank Dr. Hojong Baik and Junhyuk Kim for the interesting ideas and suggestions they have made which were beneficial to my research. This research work is done as part of the NEXTOR project, sponsored by the Federal Aviation Administration (FAA). I thank FAA for supporting the project. I extend my gratitude to Tammy Prailey who has helped me in writing this thesis document. Finally, I would like to thank my parents and brother for so many things. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS ............................................................................................ iii LIST OF FIGURES.......................................................................................................... v LIST OF TABLES.......................................................................................................... vii 1 INTRODUCTION .................................................................................................... 1 2 RESEARCH GOAL AND OBJECTIVES ............................................................. 4 3 MOTIVATION FOR RESEARCH......................................................................... 5 4 THESIS ORGANIZATION..................................................................................... 6 5 LITERATURE REVIEW ........................................................................................ 7 5.1 Introduction......................................................................................................... 7 5.2 Logit model......................................................................................................... 7 5.3 Linear regression approach................................................................................. 9 5.4 Artificial neural networks approach.................................................................. 11 6 DESCRIPTION OF NEURAL NETWORKS...................................................... 12 6.1 Introduction....................................................................................................... 12 6.2 Characteristics of neural networks.................................................................... 12 6.3 Training of a neural network............................................................................. 15 6.4 Sample calculations.......................................................................................... 16 6.5 Generalization................................................................................................... 18 6.6 Testing of a neural network .............................................................................. 18 7 MODELING............................................................................................................ 19 7.1 Data extraction.................................................................................................. 20 7.2 Data processing................................................................................................. 22 7.2.1 K-means clustering procedure .................................................................. 23 7.3 Development of mode choice models............................................................... 25 7.3.1 Artificial neural networks approach.......................................................... 25 7.3.2 Multinomial logit model ........................................................................... 28 7.3.3 Linear regression method.......................................................................... 28 7.3.4 Comparison of models .............................................................................. 29 8 RESULTS ................................................................................................................ 30 9 CONCLUSIONS ..................................................................................................... 41 9.1 Comments on using neural networks................................................................ 42 9.2 Scope for future research .................................................................................. 43 10 REFERENCES........................................................................................................ 44 11 APPENDICES......................................................................................................... 46 11.1 Appendix A – GLOSSARY OF TERMS ......................................................... 46 11.2 Appendix B – MATLAB CODES .................................................................... 47 11.3 Appendix C- SAS OUTPUTS .......................................................................... 62 12 VITA ........................................................................................................................ 64 iv LIST OF FIGURES Figure 1: Four step planning procedure 1 Figure 2: Processing element: artificial neuron with activation function 13 Figure 3: Two-layered feedforward neural network 14 Figure 4: Taxonomy of training a multilayered perceptron: 16 the input signal extends- forward and the computed error backward. Figure 5 : Sample calculation of the output 17 Figure 6: Overview of the Mode choice modeling procedure 19 Figure 7: Data Processing step 23 Figure 8: Best Neural network model 27 Figure 9: Commercial airline mode - Comparison of models- 32 based on model probability vs real probability- a) Neural network model b) Logit model c) Linear regression model. Figure 10: Automobile mode - Comparison of models- 34 based on model probability vs real probability- a) Neural network model b) Logit model c) Linear regression model. Figure 11: General Aviation mode - Comparison of models- 36 based on model probability vs real probability- a) Neural network model b) Logit model c) Linear regression model. Figure 12: Commercial airline mode - Comparison of models- 38 based on Absolute Residuals– a) Neural network model b) Logit model c) Linear regression model. v Figure 13: Automobile mode - Comparison of models- 39 based on Absolute Residuals – a) Neural network model b) Logit model c) Linear regression model. Figure 14: General aviation mode - Comparison of models- 40 based on Absolute Residuals – a) Neural network model b) Logit model c) Linear regression model. vi LIST OF TABLES Table 1: Travel Time Assumptions 21 Table 2: Travel Cost Assumptions 21 Table 3: Comparison of the models- 31 neural networks vs. multinomial logit vs. linear regression- mean square error and average relative variance vii 1 INTRODUCTION Travel demand forecasting is an essential aspect of transportation planning. Given the land-use pattern, socioeconomic, and environmental conditions, travel demand can be expressed as the number of persons or vehicles per unit time that can be expected to travel on a given segment of a transportation system. There is a need to forecast the travel demand in order to estimate the vehicular volume for the future, based on which various transportation system alternatives can be designed or modified. The methods for forecasting travel demand can
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