Presentation delivered at the XIII Congreso de Ingeniería del Transporte (CIT 2018) Nuevos retos en el transporte: menos es Más.

1. Detecting causes of low urban accessibility:
a comparative approach
Marcin Stępniak
Borja Moya-Gómez & Javier Gutiérrez Puebla
CALCULUS 08/06/2018
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
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
08/06/2018

3. Accessibility components
CALCULUS
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
Assumptions:
• Greater impact of larger centres than smaller ones
• Diminishing importance of more distantly located destinations
• The potential of opportunities for interaction Hansen (1959)
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)
Land use Transport
Individual Temporal
08/06/2018

4. CALCULUS
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
08/06/2018

5. CALCULUS
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
08/06/2018
accessibility
New data
New methods
Group 1: new
Group2:outcomes
Spatial patterns
Evaluation of transport
investments
Equity
New data

6. CALCULUS
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
08/06/2018
• Distribution of jobs
• Private cars: speed profiles
• Public transport: Schedule-based data (GTFS)

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
Data: schedule-based travel time information
• calendar_dates.txt
• fare_attributes.txt
• shapes.txt
• frequencies.txt
• transfers.txt
ArcGIS®
Network Analyst
+
ArcGIS® Network

8. CALCULUS
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
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
08/06/2018

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: speed profiles
ArcGIS®
Network Analyst
Every road segment:
speed every 5 minutes
288 values / day

10. CALCULUS
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
08/06/2018

11. CALCULUS
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
Causes of low accessibility
08/06/2018
Distirbution of jobs
Land use
Euclidean distance
Shortest path distance
Quality of road network Free flow speed
Congestion
Routing scheme
Frequency
Average congestion
Maximum congestion
No waiting times
Fastest possible connection
Average travel time
Worst-case scenario
PrivatecarsPublictransport

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
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

13. CALCULUS
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
08/06/2018

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
Results: spatial regularities
Obvious conclusions:
• central vs peripheral location
• car >> public transport

15. CALCULUS
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
-> speed limits
Congestion:
• No difference between
average & worst case scenario
08/06/2018

16. CALCULUS
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)
Change of tranpsort mode:
• not only peripheries
• mosaic distribution
Travel time variability:
• Peripheries (!)
Frequency:
• Repeat (partly) change of
transport mode
• Average scenario covered
the by worst case scenario
08/06/2018

17. CALCULUS
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
-> random distribution.
08/06/2018

18. CALCULUS
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
08/06/2018