The development of an adapted Markov chain modelling heuristic and simulation framework in the context of transportation research

摘要

This paper has developed and evaluated the implementation of an adapted Markov Chain modelling heuristic and simulation framework in the context of transportation research. In order to gain insight into the travel patterns of individuals and in the decision making that people use to make transport mode decisions, a new methodology is presented in this paper to extract knowledge from data. The presented approach shows two ways to store the sequential information (sequences of activities and travel) that is typically incorporated in activity diary data. The approach is novel, especially with respect to store information in 'codebooks', a term which is introduced to reflect that the information which is kept, represents the combinations of activities that typically sequentially occur in a persons' diary. In order to test the validity of the heuristic, new data is simulated and compared with the original observed data. The new data is generated by means of Monte Carlo simulation and the empirically derived information from the codebooks is used as a constraint in the simulations. In order to make a mature evaluation of the simulated diaries, different performance indicators were considered by using pattern-, trip- and activity-level measures, It is shown in the paper that the results are satisfactory and that the framework that was developed holds out considerable promise; both for gaining behavioural decision making insights and for simulating activity diary data that can assist practitioners and researchers in the calibration of travel demand models.