A graph-database approach to assess the impact of demand-responsive services on public transit accessibility

TL;DR

This paper presents a graph-based method to evaluate how demand-responsive transit affects accessibility equality in public transportation, using a Neo4j model and analytical measures on a real town network.

Contribution

It introduces a novel graph database approach combining GTFS data with analytical DRT modeling to assess accessibility and inequality impacts.

Findings

The approach effectively models multimodal transit networks.

Quantifies accessibility and inequality in transit systems.

Demonstrates the NP-completeness of DRT fleet allocation.

Abstract

This paper proposes an approach to analyze the impact of multimodal Public Transit (PT), combining conventional fixed-route transit and Demand-Responsive Transit (DRT), on equality in transport accessibility distribution. We construct a graph model of multimodal PT in Neo4j, based on General Transit Feed Specification (GTFS) data, modeling DRT analytically (using continuous approximation). We quantify service quality on any location using an accessibility measure, indicating how easily other places or opportunities can be reached. We quantify inequality of accessibility distribution. We state the problem of allocating a DRT fleet to minimize inequality and show its NP-completeness. We showcase our approach on the transportation network of the French town of Royan.