Evaluating the Interplay of Micro-Mobility Integration on Dynamic Vulnerability of Public Transport Bus Networks

The complex nature of the public transport network for metropolitan cities makes them more vulnerable to unwanted disruptions. To deal with disruptions, it is important to identify the critical locations in which the disruptions pose the worst effect on the network and its users. In this research, an innovative dynamic methodology is formulated by refining the traditional centrality measures to include the dynamic passenger load followed by a mixed machine learning method to identify the critical locations. Based on the identified locations different combinations of the disruptions scenarios with the usage of micro-mobility options are coded and simulated to evaluate the vulnerability of the network. The developed methodology is applied to the public transport bus network of the city of Barcelona. The results of our analysis suggest that the provision and integration of micro-mobility options with public transport have significant positive impacts but in some cases, it also worsens the situation.