From Isolation to Inclusion

Introducing the Multidimensional Transport Poverty Index tool

Rosa Félix
CERIS, Instituto Superior Técnico
University of Lisbon

Mauricio Orozco-Fontalvo
Gonçalo F. Matos
Miguel Alvelos
Camila Garcia
Filipe Moura

July 2026 — NECTAR 2026, Munich

Why Transport Poverty?

Transport Poverty

Transportation is essential — its absence or inadequacy creates:

🚫 Limited access to work, education, health, leisure, services and opportunities
🏘️ Social exclusion & territorial inequality
💸 Disproportionate financial burden on low-income households
☠️ Unequal exposure to road risk

Transport Poverty (Lucas et al. 2016) has direct consequences in social exclusion and territorial cohesion (Pritchard et al. 2014; Allen and Farber 2019; Mejia-Dorantes and Murauskaite-Bull 2022)

EU Regulation 2023/955 (Social Climate Fund) provides the first legally binding definition of Transport Poverty at European scale (European Parliament and the Council of the European Union 2023)

A Multidimensional Problem

Based on Lucas et al. (2016), Mejia-Dorantes and Murauskaite-Bull (2022) and the European Commission (2024):

🗺️ Accessibility
Ability to reach work, health, education, and other essential services within reasonable time

🚌 Mobility
Quality of transport supply: frequency, coverage, travel times, active travel infrastructure

💶 Affordability
Households’ capacity to afford transport (+ housing) relative to income

🦺 Exposure to Externalities
Exposure to road externalities: safety, crash severity, air pollution, noise

What is Missing?

❌ No composite index integrating all 4 dimensions, across multiple transport modes
❌ Existing indexes rely on costly primary data (surveys)
❌ No operational tool translating research into policy support

👉 We wanted to create an index
Multi-dimentional · Multi-modal · Scalable · Replicable

Methodology

Case Study: Lisbon Metropolitan Area

Polycentric structure (strong employment / opportunities core in Lisbon)
Strong urban/peripheral PT supply contrast
High car modal share in periphery

18 municipalities · 124 parishes · ~2.8M inhabitants, 3,000 km²
Analysis unit: freguesia (parish) — lowest admin level with full data coverage
Also computed at municipality and H3 grid level, resolution 8 (~3,686 hexagons)

Open Data

Source	Used for
INE Census 2021 / IMOB 2017	Income, housing, commuting OD pairs, modal share
OpenStreetMap	Road network, cycling/pedestrian infra, POIs
GTFS (9 operators)	PT frequency, travel times, coverage
ANSR (road safety)	Accident locations and severity (2019-2023)
Carris Metropolitana	Health centres, schools
PMMUS / TML	Shared mobility docks
GlobalBuildingAtlas	Buildings height (population density)

All computations done in R using odjitter, r5r, accessibility, sf, tidytransit, FactoMineR (Carlino and Lovelace 2022; Pereira et al. 2021; Pereira and Herszenhut 2022)

Computing Accessibility

Travel time matrix for all modes × all H3 res 8 grid cells
📍 Points of interest: healthcare, schools, groceries, green spaces, recreation, jobs

Commulative accessibility by car to health centres

Computing Mobility

Systemic quality of the transport system:

⌛💼 Average commuting time (by mode)
🔁 Average transfers (public transport)
🕑 PT headways (peak / off-peak / night / weekend)
🚏 PT population coverage (walking isochrones to stops)
🚴 Cycling and pedestrian infrastructure ratio (from OSM)

Population covered by walking-distance PT stops

Computing Affordability

“Housing + transport costs ≤ 45% of income” threshold (Isalou et al. 2014; Litman 2026)

\[transp\_inc\_comp = \frac{HH_{size} \cdot P_{mobile}}{I_{household}} \cdot \left[\frac{C_{car,day}}{O} \cdot \frac{s_{car}}{\Sigma s} + C_{PT,day} \cdot \frac{s_{PT}}{\Sigma s}\right] \cdot N_{working\_days}\]

Car cost: €0.40/km + tolls (via Infraestruturas de Portugal routing)
PT cost: single ticket (~€1.91) or monthly pass Navegante (~€25.25/month)

Computing Safety

Based on ANSR road accident data (2019–2023), within-locality crashes only:

Indicator	Formula
Accident rate	\(\sum Acc_{5y} / Pop \times 1000\)
Fatality rate	\(\sum MV_{30d} / Pop \times 1000\)
Severity index	\(\sum MV_{30d} / \sum Victims_{5y}\)
Mode-specific severity	fatalities / vehicles of that mode involved

Normalization & Aggregation

All indicators (>1700) normalized to [0, 100] using min-max scaling, depending if is a cost or a benefit:

\[X_{norm} = \frac{X - X_{min}}{X_{max} - X_{min}} \times 100\ \text{ or }\ X_{norm} = \frac{X_{max} - X}{X_{max} - X_{min}} \times 100\]

Intra-dimension: PCA (PC1 score) aggregates multiple indicators (OECD et al. 2008)

Inter-dimensional aggregation

Variant	Method	Best for
🔵 Contrasting	Entropy-weighted mean	Territorial contrast
🔴 Critical	Geometric mean	Worst-dimension penalization
🟡 Balanced	Arithmetic mean	Compensatory / communication
⚙️ Custom	Analytic Hierarchy Process (AHP) weighting	Decision-maker preferences

The IMPT Dashboard

Dashboard: 🎮 Live Demo

Available at:
🔗 ushift.pt/apps/impt

Features:

🗺️ Interactive maps at parish, municipality & grid scales
📊 All dimensions × transport modes × indicators
⚙️ Political IMPT: user-defined AHP weights
📥 Downloadable results (>1700 indicators)

🚀 Open IMPT Dashboard

Results

🗺️ Accessibility

High accessibility in the center
Deficiencies concentrated in rural/low-density areas

🚌 Mobility

Low scores in most AML except the center
Periphery is severely underserved
Highlights the radial structure of the LMA

Mobility justice?

💶 Affordability

High scores overall
The Navegante monthly pass greatly mitigates costs (inexpensive)
Worst cases in low-income, car-dependent areas
Housing more expensive where Transportation is more affordable and vice-versa

🦺 Safety

Urban core scores poorly (high volume of crashes): inverse spatial pattern
Worst rates in coastal EN6 corridor (Oeiras/Cascais), and Lisbon centre, with very high incomes

IMPT Index: Three Aggregation Methods

All modes · Parish level · Higher score = higher transport poverty

The Critical IMPT (geometric mean) is best for identifying specific dimensional deficiencies

Validation

Workshop with Municipalities

13/18 municipalities represented + transport authorities + operators
Participants assessed transport poverty levels in their territories and compared with IMPT scores

Result: Higher convergence than expected for a secondary-data index

Stakeholders were very optimistic regarding transport poverty in their territories

Main divergence: Safety — participants systematically underestimated crash risks in their territories

Conclusions

The IMPT allows

To diagnose the territory and quantify transport poverty
To support decision-makers in prioritizing interventions and investments
To compute transport poverty disregarding the indicators available
To compute transport poverty at different scales (region, city, parish, grid)

Contributions

First multi-dimensional, multi-modal composite index of Transport Poverty for Portugal
4 dimensions (Accessibility, Mobility, Affordability, Safety) × 4 transport modes × 3 aggregation methods
Entirely based on open, replicable data
Policy-ready: AHP weighting + interactive dashboard
Validated with stakeholders
Replicable to other metropolitan areas

Limitations

Affordability: too few indicators for PCA
Digital literacy dimension dropped (data gaps)
Environmental externalities not included (air pollution)

Future Work

🌍 Transferability: application to other metropolitan areas
🧠 Include perceived transport poverty as a dimension
💨 Include air pollution and noise exposure as environmental externalities
📡 Longitudinal monitoring: IMPT updates as data becomes available (eg.APIs) to assess interventions’ impacts

Thank you!

Rosa Félix

✉️ ushift@tecnico.ulisboa.pt
💾 Methodological Report: u-shift.github.io/IMPT-data
🌐 Dashboard and Data: ushift.pt/apps/impt
📄 Paper in preparation

This research was funded by Science4Policy, a PLANAPP - Centre for Planning and Evaluation of Public Policies iniciative to support the definition and implementation of public policies based on scientific evidence, under the Science4Policy (S4P): Concurso de Estudos de Ciência para as Políticas Públicas call (PLANAPP-S4P/8042/2025).
This research was funded in part by the FCT - Fundação para a Ciência e Tecnologia under Grant UID/6438/2025 of the research unit CERIS.

References

Allen, Jeff, and Steven Farber. 2019. “Sizing up Transport Poverty: A National Scale Accounting of Low-Income Households Suffering from Inaccessibility in Canada, and What to Do about It.” Transport Policy 74: 214–23. https://doi.org/10.1016/j.tranpol.2018.11.018.

Carlino, Dustin, and Robin Lovelace. 2022. Odjitter: Jitter Origin-Destination Data. https://github.com/itsleeds/odjitter.

European Commission. Directorate General for Employment, Social Affairs, and Inclusion. 2024. Transport Poverty: Definitions, Indicators, Determinants, and Mitigation Strategies: Final Report. Publications Office of the European Union. https://doi.org/10.2767/0662480.

European Parliament and the Council of the European Union. 2023. “Regulation (EU) 2023/955.” In Official Journal of the European Union, 2023/955, vol. L130. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32023R0955.

Isalou, Ali A., Todd Litman, and Behzad Shahmoradi. 2014. “Testing the Housing and Transportation Affordability Index in a Developing World Context: A Sustainability Comparison of Central and Suburban Districts in Qom, Iran.” Transport Policy 33: 33–39. https://doi.org/10.1016/j.tranpol.2014.02.006.

Litman, Todd. 2026. “Evaluating Transportation Affordability.” In Victoria Transport Policy Institute. https://www.vtpi.org/affordability.pdf.

Lucas, Karen, Ersilia Verlinghieri, and Alvaro Guzman. 2016. “Transport Poverty and Its Adverse Social Consequences.” Proceedings of the Institution of Civil Engineers - Transport 169 (6): 353–65. https://doi.org/10.1680/jtran.15.00073.

Mejia-Dorantes, L., and I. Murauskaite-Bull. 2022. Transport Poverty: A Systematic Literature Review in Europe. Publications Office of the European Union. https://doi.org/10.2760/793538.

OECD, European Union, and Joint Research Centre - European Commission. 2008. Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing. https://doi.org/10.1787/9789264043466-en.

Pereira, Rafael H. M., and Daniel Herszenhut. 2022. Accessibility: Transport Accessibility Measures. https://ipeagit.github.io/accessibility/.

Pereira, Rafael H. M., Marcus Saraiva, Daniel Herszenhut, Carlos K. V. Braga, and Matthew Wigginton Conway. 2021. “R5r: Rapid Realistic Routing on Multimodal Transport Networks with R5 in R.” Findings, ahead of print. https://doi.org/10.32866/001c.21262.

Pritchard, John P., Filipe Moura, João de Abreu e Silva, and Luiz M. Martinez. 2014. “Spatial Analysis of Transportation-Related Social Exclusion in the Lisbon Metropolitan Area.” Procedia - Social and Behavioral Sciences 111: 440–49. https://doi.org/10.1016/j.sbspro.2014.01.077.