Developing a disaggregate travel demand system of models using data mining techniques

Abstract The travel demand modelling has experienced a paradigm shift from aggregate to disaggregate models, leading to an increase in computational time and simulation cost. Meanwhile, transferability models have emerged to reduce the associated cost and computational burden, but haven’t discounted the disaggregation level. This research proposes the proof of the concept of an innovative transferability modelling framework to estimate total number of trips and trip attributes in a tour of trips at a disaggregate level. In contrast to tour-based or activity-based models, the focus of transferability models is on replicating trip patterns rather than reflecting travellers’ behaviour. Similar to previous transferability models, classifying decision tree is utilized as one of the modelling techniques in this study. Moreover, the merits of a modified version of decision tree and the random forest methods are examined. Victorian Integrated Survey of Travel and Activity (VISTA) in 2007 and 2009 are utilized to calibrate and validate the proposed framework, respectively. According to the results, the random forest method shows highest individual-level accuracy while matching the system-level observed distributions.

[1]  F. Koppelman,et al.  Activity-Based Modeling of Travel Demand , 2003 .

[2]  Abolfazl Mohammadian,et al.  Investigating Transferability of National Household Travel Survey Data , 2007 .

[3]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[4]  Hjp Harry Timmermans,et al.  Using ensembles of decision trees to predict transport mode choice decisions: effects on predictive success and uncertainty estimates , 2014 .

[5]  T. Therneau,et al.  An Introduction to Recursive Partitioning Using the RPART Routines , 2015 .

[6]  Joan L. Walker,et al.  Behavioral Realism in Urban Transportation Planning Models , 1998 .

[7]  K. Krizek.  Neighborhood services, trip purpose, and tour-based travel , 2003 .

[8]  P. S. Hu,et al.  Transferability of Nationwide Personal Transportation Survey Data to Regional and Local Scales , 2002 .

[9]  Moinul Hossain,et al.  Understanding Crash Mechanisms and Selecting Interventions to Mitigate Real-Time Hazards on Urban Expressways , 2011 .

[10]  Matthew J. Roorda,et al.  A tour-based model of travel mode choice , 2005 .

[11]  Gregory W. Corder,et al.  Nonparametric Statistics : A Step-by-Step Approach , 2014 .

[12]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[13]  Toshiyuki Naito,et al.  MODAL CHOICE ANALYSIS USING ENSEMBLE LEARNING METHODS , 2012 .

[14]  Abolfazl Mohammadian,et al.  Investigating the Transferability of Individual Trip Rates: Decision Tree Approach , 2013 .

[15]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[16]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[17]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[18]  Stephen Greaves,et al.  Simulating Household Travel Survey Data: Application to Two Urban Areas , 2003 .

[19]  M. E. Williams,et al.  TRANSIMS: TRANSPORTATION ANALYSIS AND SIMULATION SYSTEM , 1995 .

[20]  Mohamed Abdel-Aty,et al.  Estimation of Real-Time Crash Risk , 2011 .

[21]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[22]  S. Travis Waller,et al.  A Novel System of Disaggregate Models for Travel Demand Modelling, Using Decision Tree and Random Forest Concepts , 2015 .

[23]  K. Axhausen,et al.  Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems , 1992 .

[24]  Abolfazl Mohammadian,et al.  Household travel attributes transferability analysis: application of a hierarchical rule based approach , 2011 .

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[26]  Richard L. Schmoyer,et al.  Transferring 2001 National Household Travel Survey , 2007 .

[27]  R. Kitamura.  An evaluation of activity-based travel analysis , 1988 .

[28]  Kirolos Haleem,et al.  Multiple Applications of Multivariate Adaptive Regression Splines Technique to Predict Rear-End Crashes at Unsignalized Intersections , 2010 .

[29]  David A. Cairns,et al.  Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls , 2008, Statistical applications in genetics and molecular biology.

[30]  Chandra R. Bhat,et al.  Destination Choice Modeling for Home-Based Recreational Trips: Analysis and Implications for Land Use, Transportation, and Air Quality Planning , 2001 .