Citation

Ballis H, Dimitriou L. Transp. Res. B Methodol. 2020; 139: 224-258.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.trb.2020.06.007

PMID

unavailable

Abstract

Over the last decades, technological advances have allowed the capturing of travel behaviour at large-scale. Despite the unprecedented volume and the variety of personal mobility data, aggregate Origin-Destination (OD) matrices are still the most widespread means to organise and represent travel demand. Nonetheless, standard ODs cannot adequately capture significant elements affecting travel behaviour such as trip-interdependency and trip-chaining, therefore they are not particularly suitable for travel behaviour analysis at person-level. The currently presented modelling framework enables the in-depth study of personal mobility by firstly combining the trips present in OD matrices into home-based trip-chains (i.e. tours) and subsequently into sequences of activities (activity schedules). The above-mentioned process is completed based on advanced graph-theoretical and combinatorial optimisation concepts. The applicability of the methodology is meticulously verified through a large-scale test case where a set of multi-period, purpose dependant ODs is converted into realistic activity schedules able to incorporate more than 99% of the inputted travel demand. The accurate and highly detailed results showcase the significant potential of the proposed methodology to support the comprehensive analysis of travel behaviour at person level.

Language: en

Keywords

Activity schedules; Graph-theory; Integer programming; Origin-destination matrices; Tours; Trip-chains