Micro-interventions in urban transport from pattern discovery on the flow of passengers and on the bus network
This work addresses urban transportation inefficiencies for city planners and transit authorities, but it is incremental as it applies existing methods like community discovery to new data.
The paper tackled the problem of optimizing urban bus networks by analyzing passenger flow and bus route data to identify bottlenecks and propose targeted interventions, resulting in a case study that demonstrated how micro-interventions like express routes can be suggested based on discovered patterns.
In this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.