Micro-interventions in urban transport from pattern discovery on the flow of passengers and on the bus network

TL;DR

This study analyzes urban bus passenger flow and network patterns to identify bottlenecks and suggest micro-interventions, such as express routes, to improve transportation efficiency in a large city.

Contribution

It introduces a methodology combining origin-destination data and complex network analysis to discover mobility patterns and propose targeted incremental interventions.

Findings

Identified bottlenecks in the bus network through community detection.

Demonstrated how flow analysis can inform micro-interventions like express routes.

Provided a case study applying these techniques in a large metropolis.

Abstract

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.