Inclusive Multimodal Routing: Who Gets Left Behind?

The data and code scripts used for the analysis in the paper entitled "Inclusive Multimodal Routing: Who Gets Left Behind?", submitted to AGILE (Association of Geographic Information Laboratories in Europe) 2026 Conference. The analysis focuses on the city of Vienna, Austria, which serves as the case study area for the routing experiments.

It comprises three folders within the zip file:

1. data: Contains the datasets used for the analysis. These datasets are derived from real-world transport network data. This folder also includes one serialized Python object (.p file)
2. code: Python scripts required to perform the analysis.
3. results: Routing results used in the analysis and discussed in the associated paper.
4. plots: Visualizations and figures presented in the paper.

Programming Language: Python 3.13 

For reproducibility, read the READ_ME.txt file included in the zip folder.

All data files are licensed under CC BY 4.0, all software is licensed under the MIT License.

Abstract

Conventional routing algorithms in urban settings typically optimize travel time, assuming that travelers will accept any combination of public and active transport modes to have the fastest route. Empirical evidence, however, shows that people value more than just time: they usually avoid routes with excessive walking or transfers, even if they are technically the fastest. These existing studies mostly assume an average, healthy population, but many people cannot or do not want to meet these average thresholds for active or multimodal travel. As cities promote walking, cycling, and public transport as part of sustainable mobility strategies, it becomes critical not to overlook these groups’ mobility needs. This paper presents the results of a data driven analysis concerning the mobility patterns of these groups. Using a synthetic mobility data generation pipeline, we produce thousands of Origin-Destination (OD) pairs and systematically reduce key preference thresholds by -25%, -50%, and -75% relative to the average values. The goal is to investigate how results change in terms of average travel time, distance, and modal share, and to estimate the proportion of trips that cannot satisfy these preferences. The results show that, in a well-developed European city, average travel times remain stable for routes that satisfy the thresholds. However, many OD pairs become unreachable as these thresholds are tightened, representing people with lower abilities or willingness, deviating from the average.