This presentation discusses the temporal dimension of accessibility and its application in detecting causes of low accessibility. It reviews existing approaches to measuring accessibility over time and how new data sources can help capture temporal changes in land use, individual movement, and transportation networks. As a case study, it uses speed profiles and public transit data to analyze job accessibility in Madrid at different times of day and under different transportation scenarios. The temporal analysis helps identify specific constraints on accessibility like congestion levels, routing networks, and frequency of public transit.

1. Temporal dimension of accessibility.
Application for detection of causes
of low accessibility
NECTAR Cluster 6 International Workshop • Gran Canaria
Marcin Stępniak • Borja Moya-Gómez • Javier Gutiérrez Puebla • Amparo Moyano 14/12/2018
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. Structure of presentation
• Review: Temporal dimension of accessibility
• Existing approaches
• Recent developments
• Potential of new data (availability)
• Empirical study: application of temporal dimension
• Detecting causes of low accessibility
• Madrid case study
• Accessibility to jobs
• SpeedProfiles & GTFS data
Marcin Stępniak • NECTAR CL6 Workshop
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

3. Temporal dimension of accessibility
• Geurs & van Wee (2004): temporal constraints
the availability of opportunities at different times of the day, and the time available for
individuals to participate in certain activities
• Boisjoly and El-Geneidy (2016): accessibility measures
• constant: constant travel time and number of jobs
• static: fluctuation of travel times and constant number of jobs
• dynamic: fluctuation of travel times and number of jobs
• Järv et al. (2018): temporal dymanics of components of accessibility
• people: dynamic spatial distribution of population
• transport: dynamics of travel time (cost)
• activities: temporal availability of activities (desired destinations)
Marcin Stępniak • NECTAR CL6 Workshop
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

4. Temporal scale
short medium long
Temporal dimension of accessibility
Temporal
dimension
Land use component
Individual component
Transport component
Marcin Stępniak • NECTAR CL6 Workshop
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

5. Temporal dimension of land use component
land use
Origins’
oriented
Destinations’
oriented
de facto location
home / work
residential mobility
migrations
tourism
traditional
data sources
social media
active / passive
location
other data
sources
census survey
GPSGSM
Credit
card
ANPR
cameras
smart
card
shared
mobility
...
fluctuation of
attractiveness
jobs starting time
(dis)apperance of
destinations
opening hours
(new) data sources
Marcin Stępniak • NECTAR CL6 Workshop
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

6. Temporal dimension of individual component
needs and
opportunities
interaction
potentials
(new) data sources
individual
individual temporal
restrictions
transport mode:
preferences / ability
individual schedule
/ budget of time
socio-economic
profile
individual trajectory
geo-
located
data
sport
apps
GPS
tracker
social
media
smart
card data
credit
card data
activity
tracker
Marcin Stępniak • NECTAR CL6 Workshop
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

7. Temporal dimension of transport component
evolution of
travel time
temporal
resolution
daily fluctuation
week-day /
weekend
(dis)appearance of
infrastructure
season / out of
season
sampling
frequency
sampling strategy
car
public
transport
other (slow)
modes
speed limits /
free flow
pick /
pick-off hours
speed
profiles
routes
frequency
schedules
routes
navigation
systems
speed
profiles
GTFS
route
planners
sport
apps
shared
mobility
data
real-time
data
(new) data sources
transport
real-time
data
Marcin Stępniak • NECTAR CL6 Workshop
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

8. Case study
Marcin Stępniak • NECTAR CL6 Workshop
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
Madrid
• 586 transport zones (1171 in metropolitan area)
• Accessibility to jobs (potential accessibility):
• negative exponential distance decay
• Half-life approach (Östh et al. 2014): average commuting time (30 minutes)
• Morning peak hours (7 – 10 am), typical weekday (April 2018)
• Private car – TomTom speed profiles:
• temporal resolution: 15 minutes
• 10 minutes handicap (parking etc.); not applicable if walking faster than car
• Public transport – GTFS data:
• 5 transport modes
bus; metro, tram (metro ligero), suburban trains (cercanias), suburban buses
• Sampling: hybrid sampling model with 5-minute temporal resolution;

9. Accessibility to jobs
Marcin Stępniak • NECTAR CL6 Workshop
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
Euclidean distance
Car: free flow
Car: average
Public transport:
no waiting times
Public transport: average

10. Accessibility constraints
Marcin Stępniak • NECTAR CL6 Workshop
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
jobs distribution
geographical constraints
quality of road network
congestion
routing scheme
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

11. Accessibility constraints: global view
Marcin Stępniak • NECTAR CL6 Workshop
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

12. Accessibility constraints: global view
Marcin Stępniak • NECTAR CL6 Workshop
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
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 95.4
Avg 60.4 62.0 90.3 94.7
Worst 57.9 59.4 86.5 90.7 95.8
Publictransport
Max.Freq 56.6 58.1 84.6 88.7 93.7 97.7
Best 48.3 49.6 72.3 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

13. Spatial patterns: impact of congestion
Marcin Stępniak • NECTAR CL6 Workshop
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
• Temporal approach: evaluation of
congestion impact
• Reduced congestion: impact
on accessibility below average
(90% of free flow accessibility)
• Green areas – PT might be
considered as alternative to private
cars.
• Red & orange areas: PT requires
intervention
• Transport poverty – high impact of
congestion AND reduced PT options
(PT < 40% of car accessibility)
• Negative congestion impact:
sparsely populated areas

14. Spatial patterns: Intermodal disparities
Marcin Stępniak • NECTAR CL6 Workshop
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
• Temporal approach: evaluation of PT
accessibility & intermodal disparities
• + Transport poverty limited to
peripheral, sparsely populated areas
• PT accessibility > 66% of car
accessibilty:
• City centre
• Southern part of the city
• Along metro lines (exceptions)
• Around stations of suburban trains
(corridor effect)
• Big, populated areas with limited
PT accessibility

15. Spatial patterns: Routing scheme
Marcin Stępniak • NECTAR CL6 Workshop
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
• Even idealistic scenario of PT
(no waiting times) underpins
intermodal disparities;
• Importance of suburban railways &
metro lines.
• Centralized PT system
(limited perimeter connections)
• PT > Car accessibility
only in very limited central zone
• Impact of zone 30 km/h
(implementation: October 2018)
needs to be investigated

16. Spatial patterns: Frequency
Marcin Stępniak • NECTAR CL6 Workshop
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
• Temporal approach: evaluation of
impact of PT frequency and/or
reliability of PT travel times
• Frequency impact: reduced around
metro & train stations
• Low level of schedules
synchronization (lack of spillovers of
stations).

17. Conclusions
Marcin Stępniak • NECTAR CL6 Workshop
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
Temporal dimension of accessibility:
• Emerging field of research (in particular: short temporal scale)
• temporal dimension of transport component;
• Potential: real-time analyses
• Inter- / multi-modal analyses
• Land use component:
• important developments already made;
• potential: new data sources
• Individual component – potential of new data sources:
• Faciliates to address e.g. equity
• Tailor-made analyses (focus on particular social groups)
• Need for the development of methods
• Privacy concerns

18. Conclusions
Marcin Stępniak • NECTAR CL6 Workshop
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
Empirical study – temporal dimension enables to:
• Properly address accessibility by public transport;
• Identify several types of accessibility constraints (congestion, frequency etc.)
• Evaluate travel time vulnarability
• Evaluate impact of congestion
• Evaluate impact of frequency

19. http://www.ucm.es/tgis
Twitter: @tGIS_ucm
Thank you for your attention!
Marcin Stępniak
marcinstepniak@ucm.es • @marcin_stepniak