This document summarizes a study that used a comparative approach to analyze urban accessibility in Madrid, Spain. It calculated accessibility to jobs from origin-destination pairs using different transport scenarios for private cars and public transit. The study found that private cars provided much higher accessibility than public transit. Accessibility was highest in central areas and lowest in peripheries for both transport modes. For public transit, travel times varied more in peripheries and were more affected by frequencies. The analysis identified constraints on accessibility to help define policy responses to improve transport equity and sustainability.

1. Detecting causes of low urban accessibility:
a comparative approach
Marcin Stępniak
Borja Moya-Gómez & Javier Gutiérrez Puebla
CALCULUS 19/01/2018
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. CALCULUS 19/01/2018
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
CAlCULUS project
Causes and Consequences of low urban accessibility.
Defining proper policy responses
CAlCULUS project
Causes and Consequences of low urban accessibility.
Defining proper policy responses
Introduction

3. CALCULUS 19/01/2018
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
Accessibility
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
accessibility
Land use
Individual
TransportTemporal
Accessibility components
Geurs & van Wee (2004)

4. CALCULUS 19/01/2018
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
Importance
Challenges for urban areas:
• sustainable development
• improvement of the quality of life
• reduction of transport-related air
and noise pollution
• social and spatial inequalities
Urban Agenda
White Paper on Transport
5th Cohesion Report
Limited
accessibility
social
exclusion
quality of life
economic
activity
utilisation of
public
services
Sustainable
development
Air & noice
pollution
Transport planning: shift from mobility-centered to accessibility-centeredTransport planning: shift from mobility-centered to accessibility-centered

5. CALCULUS 19/01/2018
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
Knowledge gap
accessibility
Spatial patterns
Evaluation of transport
investments
Intermodal comparisons
Vulnerable groups
Equity

6. CALCULUS 19/01/2018
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
Comparative approach
Quality of transport network
Congestion
Routing scheme
Frequency
Travel time fluctuations
Private cars Public transport

7. CALCULUS 19/01/2018
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
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)
Car Public transport
Based on: Salonen & Toivonen, 2013

8. CALCULUS 19/01/2018
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
Data: speed profiles
ArcGIS®
Network Analyst
Every road segment:
speed every 5 minutes
288 values / day

9. CALCULUS 19/01/2018
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
Data: schedule-based travel time information
• calendar_dates.txt
• fare_attributes.txt
• shapes.txt
• frequencies.txt
• transfers.txt
ArcGIS®
Network Analyst
+
ArcGIS® Network

10. CALCULUS 19/01/2018
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
Data: accessibility to public transport
ArcGIS
Network
1171 OD
nodes
PT & walking
network
287 026 edges
Public transport network
• 5 transport 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;
• Morning peak hours: 7-10 am
study
walking
speed (km/h)
comment
(Reyes et al., 2014) 3.2 Minimum typical speed for children aged 5-11
(Fransen et al., 2015) 4.0 Adult's average
(Ritsema van Eck et al.,
2005)
4.0 Distance as the crow flies
(Hadas, 2013) 4.0 -
(Nettleton et al., 2007) 4.8 -
(Farber et al., 2014) 4.8 -
(Willis et al., 2004) 5,3 mean walking speed of individuals
(Reyes et al., 2014) 5.4 Maximum typical speed for children aged 5-11
(Krizek et al., 2012) 5.4 average walking speed for 14-64 year old
Walking speed: 4.5 km/h

11. CALCULUS 19/01/2018
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
Methods (1)
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
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)

12. CALCULUS 19/01/2018
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
Data: Madrid Metropolitan Area
• 1171 Transport zones (TAZ)
• 2.4 mln of Jobs
• 1104 TAZ with Jobs
• Uneven distribution of Jobs:
Gini coefficient = 0.643

13. CALCULUS 19/01/2018
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
Land use component
Not all distributions should be considered as unfair Geurs & van Wee (2011)
Space by its very nature is divided into center and periphery
-> inequality in accessibility is inevitable
Martens (2012)
Land use component:
- Euclidean distance
- Network distance
Domain of
urban planners
distribution of jobs
Geography:
Road network

14. CALCULUS 19/01/2018
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
Methods (2)
• Temporal variation: accessibility vs travel times• Temporal variation: accessibility vs travel times
• Land use indepedent: accessibility vs travel speed• Land use indepedent: accessibility vs travel speed
• Inspirations
1. Potential Mobility Indicator:
2. Weighted mean of network impedance
divided by Euclidean distance:
Martens (2015)
Gutiérrez et al., (1998)
Ad 1 – no destination weights
Ad 1&2 – no distance decay
Interaction-weighted average speed (As)
Stępniak & Jacobs-Crisioni (2017)
𝑡 𝛿 𝑖𝑗 = ln
𝑗∊𝐽
𝑛
𝑃𝑗/
𝑗∊𝐽
𝑛
𝑃𝑗
𝑒𝑥𝑝𝛿𝑡𝑖𝑗
𝛿𝐴𝑠𝑖 =
𝑑
𝑡 𝛿 𝑖𝑗

15. CALCULUS 19/01/2018
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
Transport component
Accessibility constraints
Private cars:
• Quality of road network
• Congestion
Public transport:
• Routing scheme
• Applied resources
• Frequency
Free flow speed
Average travel speed
lowest travel speed (worst case scenario)
Maximum frequency (no waiting times)
Fastest possible connection (best case scenario)
Average travel speed
Lowest travel speed (worst case scenario)
Travel time variation

16. CALCULUS 19/01/2018
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
Scenarios
Transport
mode
Scenario Aerial speed
(Euclidean
distance)
Network
speed
Private car Best case (free flow) CarMin
Average speed* CarAvg
Worst case scenario CarMax
Public
transport
Max frequency PTFF
Best case (fastest connection) PTMin
Average speed* PTAvg
Worst case scenario PTMax
* Morning peak hours: 7-10 am

17. CALCULUS 19/01/2018
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
Global results – relative change
Network
distance
CarMin CarAvg CarMax PTMin PTAvg
CarMin 13,4
CarAvg 13,5 6,9
CarMax 13,5 10,2 3,5
PTMin 12,9 48,6 44,7 42,7
PTAvg 13 57,2 54,1 52,4 16,9
PTMax 13 64,0 61,3 59,9 30,0 15,8
Comments:
• No areas (TAZ) with PT > Car
• Only 0.7% OD pairs with average travel
speed by public transport faster than by car
(only short distances)
• Enourmous difference: Car >> PT
• Best vs worst case scenarios: more important for PT than Car

18. CALCULUS 19/01/2018
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
Results: correlations
Correlations between scenarios
• High for cars (0.99)
• High for public transport (≥0.9)
• Low but significant between
transport modes (~0.3)

19. CALCULUS 19/01/2018
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
Results: spatial regularities
Obvious conclusions:
• central vs peripheral location
• car >> public transport

20. CALCULUS 19/01/2018
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
Results: spatial regularities (car)
Quality of road network:
• Core of metropolitan area
Congestion:
• No difference between
average & worst case scenario

21. CALCULUS 19/01/2018
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
Results: spatial regularities (public transport)
Comments
Change of tranpsort mode:
• mosaic distribution
Travel time variability:
• peripheries
Frequency:
• Repeat (partly) change of
transport mode
• Average scenario covered
the by worst case scenario

22. CALCULUS 19/01/2018
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
Conclusions
• Comparative approach: to identify accessibility constraints.
• Intermodal imbalance (Car >> Public transport).
• Public transport users: more vulnerable to temporal dimension.
• Congestion: problem limited to the core city area.
• Instability of travel time (public transport): limited to peripheries.
• Public transport: not only the problem of peripheries.
Further steps
• Full frequency scenario.
• Typology -> towards policy recommendations.
• Effect on equity.

23. CALCULUS 19/01/2018
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
Thank you for your attention!
marcinstepniak@ucm.es