Optimal synthesis of tours from multi-period origin-destination matrices using elements from graph theory and integer programming
Nowadays, mobility modelling at individual level is receiving significant attention. Moreover, the technological advances in the field of travel behaviour analysis have supported and promoted the modelling paradigm shift to disaggregate methods such as agent/activity-based modelling Nonetheless, such approaches usually require significant amounts of detailed and fine-grained data which are not always easily accessible. The methodology presented in this paper aims to generate individual home-based trip-chains (i.e. tours) utilising aggregated sources of information, primarily, typical Origin-Destination matrices (ODs) and secondarily travel surveys. A suitable framework able to optimally identify ‘hidden’ tours in typical ODs is proposed and evaluated through its application on a set of multi-period OD matrices, covering an urban area of realistic size. This novel methodological framework synthesises the individual tours by combining and elevating advanced graph theory and integer programming concepts. The performance of the proposed methodology proves particularly encouraging since high estimation accuracy (greater than 85%) was achieved even for the most challenging examined test-case. The presented results provide positive evidence that information regarding travel behaviour on an individual level can be produced based on aggregated data sources such as OD matrices. This element is particularly valuable towards the analysis of mobility at the person-level, especially within the framework of agent-based modelling.