  A multi-modal transport system is acknowledged to have robust failure
tolerance and can effectively relieve urban congestion issues. However,
estimating the impact of disruptions across multi-transport modes is a
challenging problem due to a dis-aggregated modelling approach applied to only
individual modes at a time. To fill this gap, this paper proposes a new
integrated modelling framework for a multi-modal traffic state estimation and
evaluation of the disruption impact across all modes under various traffic
conditions. First, we propose an iterative trip assignment model to elucidate
the association between travel demand and travel behaviour, including a
multi-modal origin-to-destination estimation for private and public transport.
Secondly, we provide a practical multi-modal travel demand re-adjustment that
takes the mode shift of the affected travellers into consideration. The pros
and cons of the mode shift strategy are showcased via several scenario-based
transport simulating experiments. The results show that a well-balanced mode
shift with flexible routing and early announcements of detours so that
travellers can plan ahead can significantly benefit all travellers by a delay
time reduction of 46%, while a stable route assignment maintains a higher
average traffic flow and the inactive mode-route choice help relief density
under the traffic disruptions.
