This document discusses measuring transport-related accessibility restrictions. It defines accessibility and outlines factors that can restrict accessibility like congestion, infrastructure quality, and public transport frequency and reliability. The document then describes a case study measuring accessibility in Madrid using speed profiles, public transport schedules, and origin-destination matrices. The results show spatial patterns of congestion impacts, intermodal disparities between car and public transport accessibility, and impacts of public transport schedule frequency. Integrating biking and public transport is presented as a potential improvement to address accessibility restrictions.

1. Measuring transport related
accessibility restrictions
Expert Group Meeting on Transport Indicators • Brussels
Marcin Stępniak 27/06/2019
This project has received funding from the European Union’s Horizon 2020 research and
innovation Programme under the Marie Sklodowska-Curie Grant Agreement no 749761

2. Accessibility
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• Accessibility is the potential of oportunities for interaction Hansen (1959)
• Accessibility is the extent to which land use
and transport systems enable (groups of) individuals or goods
to reach activities (or destinations)
by means of (a) transport mode(s)
• at various times of the day. Geurs & van Wee (2004)
• Accessibility as a freedom
• Accessibility as a livability Appleyard (2014)

3. Accessibility in the scope of SDG
Accessibility as a freedomSDG Expert Group meeting

4. Restrictions of accessibility
• Quality of road infrastructure
• Congestion
• Intermodal imbalance
• Route network
vs
• Frequency
• Reliability
+
+
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5. Accessibility constraints
jobs distribution
geographical constraints
quality of road network
congestion
Route network
frequency
vulnerability
Scenarios:
Euclidean distance
Network distance
Free flow speed
Average congestion
No waiting times
Average travel time
Travel time variation
best-case scenario
worst-case scenario
best-case scenario
worst-case scenario
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6. Door-to-door approach
simple
•Road geometry
•Speed limits
intermediate
•Road geometry
•Speed limits
•Congestion
advanced
•Road geometry
•Speed limits
•Congestion
•Parking & walking
simple
•Route geometry
•Estimated speed
intermediate
•Route geometry
•Estimated / real in-vehicle time
•Estimated transfer & waiting time
advanced
•Route geometry
•Schedule-based in-vehicle time
•Schedule-based waiting time
•Walking (access / egress)
Publictransport
Based on: Salonen & Toivonen, 2013
Privatecar
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7. Data sources (1): Madrid case study
• Accessibility calculated for all the metropolitan
area, but interpretation limited to the city
(avoid border effect)
• 584 transport zones (out of 1171 in metropolis)
• Area: 604 km2 population: 3.4 M
• Job distribution: central-peripheral division
Madrid case study
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8. Potential accessibility
Östh et al. (2014)
𝑓 𝑡𝑖𝑗 = exp (−𝛽𝑡𝑖𝑗)
Distance decay function: ’half-life’ approach:
• destination loses half of its attractiveness
at the observed median travel time
• OD matrix (trip purpose: commuting)
• Negative exponential function
•
• 𝛽 = 0.02230 (~31 minutes)
Accessibility to jobs:
• Travel time between all pairs of origin-destination nodes
• Greater impact of larger centres than smaller ones
• Diminishing importance of more distantly located destinations
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9. Data sources (2): TomTom speed profiles
• Each edge has its individual speed profile
• For each departure time: origin-destination matrix
• Temporal resolution of speed profiles: 5 minutes
• Temporal resolution of origin-destination matrices: 15
minutes
• Free flow travel time
(benchmark to evaluate an impact of congestion)
• Door-to-door approach: in vehicle time +
10 minutes handicap (parking, walking etc.);
not applicable if walking is faster than car
Speed profiles (private cars)
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10. Data sources (3): GTFS (public transport)
Public transport network (5 modes):
• Metro (12 lines)
• Commuter trains (Cercanias: 10 lines)
• Light metro (tramway: 3 lines)
• EMT (Buses urbanos > 200 lines)
• Buses interurbanos (> 400 lines)
Schedule for typical week-day;
Temporal resolution: 5 minutes
No waiting time scenario: pseudo-GTFS
GTFS (General Transit Feed Specification)
http://datos.crtm.es/
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11. Results (1): global
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12. Results (1): global
Distance Car Public transport
Euclidean Network
Free
flow
Best Avg Worst
Max.
Freq
Best Avg
Network 97.4
Car
Free flow 66.8 68.6
Best 63.8 65.5 95.5
Avg 60.4 62.0 90.4 94.7
Worst 57.9 59.4 86.6 90.7 95.8
Publictransport
Max.Freq 56.6 58.1 84.6 88.7 93.7 97.7
Best 48.3 49.6 72.4 75.8 80.1 83.6 85.5
Avg 41.7 42.8 62.4 65.4 69.1 72.1 73.7 86.2
Worst 35.8 36.8 53.6 56.1 59.3 61.9 63.3 74.1 85.9
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13. Results (2): spatial patterns
Impact of congestion
• Inhabitants of which areas face reduced
accessibility due to congestion?
• Average car accessibility (7-10am)
vs Free flow accessibility (90.4%)
• Clear south/north division
• South: farther periferies – higher reduction
• North – partly mozaic pattern
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14. Results (2): spatial patterns
Public transport: intermodal disparities
• Inhabitants of which areas face reduced PT
accessibility in comparison to car accessibility?
• Average PT accessibility vs average car
accessibility (average: 69.1%)
• Mirrors the spatial pattern of PT routing
structure (correlation: 0.9)
• Visible impact of suburban rails & metro
(in particular: southern & western part of the
city)
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15. Results (2): spatial patterns
Impact of frequency
• Inhabitants of which areas have to adapt their daily
routines to PT schedules?
• PT worst-case scenario (7-10am)
vs PT best-case scenario (average: 74.1%)
& coefficient of variation (inversed for the sake of comparability)
• Center-peripery division
+ impact of metro (high frequency)
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16. Results (2): Spatial patterns
Impact of congestion &
public transport alternative
• Only in some congested areas public transport
might be considered as an alternative
• Visible: central / periphery (congestion impact) &
south / north (public transport as an alternative)
• Importance of suburban rails
(in southern part, in particular)
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17. Potential improvement: bike and ride integration?
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What can we gain if we integrate cycling and public transport?
Walk & Ride vs Bike and Ride models
Source: Pritchard JP, Stępniak M, Geurs KT (2019)
Project ASTRID https://www.astridproject.com

18. Potential improvement: bike and ride integration?
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Walk & Ride vs Bike and Ride models
Source: Pritchard JP, Stępniak M, Geurs KT (2019)
Project ASTRID https://www.astridproject.com

19. Conclusions
• Aplication of new data sources: speed profiles (cars) and GTFS (public transport)
to adress temporal dimension of accessibility
• Comparison of several scenarios of accessibility level to identify accessibility restrictions
• Delineation of areas where congestion appears together with the
lack of (efficient) public transport
• Potential to improvement resulted from integration of bike
and public transport
• Future research: accessibility improvement resulted from
integration of micromobility and public transport
• Future reserach: GTFS – convinient transfers, access for people
with reduced mobility etc.
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20. http://www.ucm.es/tgis
Twitter: @tGIS_ucm
Thank you for your attention!
Marcin Stępniak
marcinstepniak@ucm.es • @marcin_stepniak