Forecasting travel behavior using Markov Chains-based approaches |
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Affiliation: | 1. Institute of Food and Resource Economics, University of Copenhagen, Rolighedsvej 25, 1958 Frederikberg C, Denmark;2. Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg C, Denmark;1. Systems Engineering, University of California, Berkeley, United States;2. Industrial Engineering & Operations Research, University of California, Berkeley, United States;3. Institute of Transportation Studies, University of California, Berkeley, United States;4. Systems Engineering & Institute of Transportation Studies, University of California, Berkeley, United States |
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Abstract: | Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems. |
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Keywords: | Travel behavior analysis Travel demand modeling Activity sequences Daily activity-travel patterns Simulation-based population synthesis Profiling analysis |
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