How many activities can be reached with the car from the given origin during the given time? How to compare accessibility with the private car and with the public transport (and, probably, other modes, as bike)? How to solve complex scientific problems using modern Big Data technologies in conjunction with traditional tools?  Itzhak Benenson, Dmitry Geyzersky, Karel Martens, Yodan Rofe

1. Big Data Analysis for the High-Resolution View of
Urban Public Transportation Accessibility
Itzhak Benenson1, Dmitry Geyzersky2,
Karel Martens3, Yodan Rofe4
1Department
of Geography and Human Environment, Tel Aviv University, Israel
2Performit LTD, Israel (http://www.performit.co.il)
3Institute for Management Research, Radboud University Nijmegen, Holland
4Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Israel
http://www.tau.ac.il/~bennya/
bennya@post.tau.ac.il
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2. What is accessibility?
The extent to which land-use transport system enables individuals
to reach destinations by means of transport modes1
• Given a destination:
The number of origins from which a destination can be reached,
given the amount of effort
• Given an origin:
The number of destinations that can be reached from the origin,
given the amount of effort
1K.T.
Geurs, J.T. Ritsema van Eck, 2001, “Accessibility measures: review and applications”,
RIVM report 408505 006, Urban Research Center, Utrecht University
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3. Transport-based component of accessibility is
car-based and aggregate
How many activities can be reached
with the car from the given origin
during the given time?
Accessibility changes abruptly
at the boundary of an area
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4. Accessibility components
Transportation:
Components of transportation system performance (modes, travel time, cost,
effort to travel between origin and destination)
Land-use:
Distribution of needs/activities (jobs, schools, shops) and population (workers,
pupils, customers) in space and time
Individual utility:
The demand for trips between certain origins and destination, benefits people
derive from the access to facilities
4
Guangzhou, June 2013

5. The goal: To estimate accessibility from the user’s viewpoint
The idea: To compare accessibility with the private car and with
the public transport (and, probably, other modes, as bike)
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6. Accessibility depends on a transportation mode
Public Transport Travel Time (PTT):
PTT = Walk time from origin to a stop 1 of the PT + Waiting time of PT at stop 1 +
Travel time of PT1 + [Transfer walk time to stop 2 of PT + Waiting time of PT 2 +
Travel time of PT 2] + … + Walk time from the final stop to destination
Private Car Travel Time (CTT):
CTT = Walk time from origin to the parking place + Car trip time + Parking
search time + Walk time from the final parking place to destination.
Service area:
Given origin O, transportation mode M and travel time t define
Mode Service Area - MSAO(t) - as maximal area containing all destinations D
that can be reached from O with M during MTT ≤ t.
Access area:
Given destination D, transportation mode M and travel time t define
Mode Access Area – MSAD(t) - as maximal area containing all origins O from
which given destination D can be reached during MTT ≤ t.
We distinguish between
Public Transport Service Area PSAO(t), Public Transport Access Area PAAO(t),
Private Car Service Area CSAO(t), Private Car Access Area CAAO(t)
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7. We focus on measuring relative accessibility
Service areas ratio: SAO(t) = PSAO(t)/CSAO(t)
Access area ratio: AAD(t) = PAAD(t)/CAAD(t)
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8. IN A NEW ERA OF BIG DATA
WE ARE ABLE TO ESTIMATE
ACCESSIBILITY EXPLICITLY!
Utrecht Metro
500 km2
0.6*106 pop
150 bus lines
Tel Aviv Metro
600 km2
2.5*106 pop
300 bus lines
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9. BIG URBAN
TRANSPORTATION
DATA
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10. Street network
104 ÷ 105 links
Attributes:
traffic directions,
speed
Necessary for measuring
accessibility by car
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11. Bus lines –
102 ÷ 103
Bus stops
102 ÷ 103
Relation between
bus lines and stops.
Necessary for measuring bus
accessibility
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12. Bus time-table 105 ÷ 106
Necessary for measuring
bus accessibility
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13. Buildings and jobs, 105 - 106
Necessary for measuring activity component of accessibility
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14. Socio-economic level by traffic zones
Land-uses, 105 ÷ 106
Car ownership
Necessary for measuring
activity component of
bus accessibility
Socio-economic level
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15. AccessCity
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16. From transportation
networks to graphs
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17. Translation of Road network into Graph is easy…
Node  Junction
Link  Road segment
Impedance  Travel time
Typical metropolitan road network graph has
104 - 105 nodes and links
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18. Every travel should be represented explicitly
Destination
Transfer
Stop 1
Transfer
Stop 2
Final
Stop
Initial
Stop
Origin
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19. Public Transport  Graph, the idea
Route 1
Bus starts every 10 minutes
6:57 7:01
1
2
7:03
3
7:05
4
7:08
5
7:09
6
7:12
7
7:15
8
Start
Travel
Bus starts every 30 minutes
Route 2
6:50 6:56
12
11
7:02 7:06
13
14
[1, 6:57, 1, 6:57]
1
[1,6:57, 2,7:01]
0:04
2
0:02
7:10
15
[1,6:57, 3,7:03]
7:14
16
4
0:05
15
[2, 6:50, 1,7:10]
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0:04
16
0:04
[1, 6:50, 3,7:14]
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[1, 6:50,1,7:18]
7:21
18
[1, 6:57, 4,7:05]
0:02
3
7:18
17
Destination
0:03
18
[1, 6:50,1,7:21]
Dresden, August 2013
[Bus route = 1,
Start Time = 6:57,
Stop = 4,
Arrival time = 7:05]

20. Public Transport  Graph, the process
Node  Building
Node  [PTLine_ID, Stop_ID, ArrivalTime] (triple)
Link  (a) Possible path between building and PT stop accessible by foot;
Link  (b) Possible path between two sequential stops connected by the PT line;
Link  (c) Possible path stops connected by the transfer walk
Node impedance  (a) Population, Number of jobs
Link Impedance  (a) Walk time
Link Impedance  (b) PT travel time
Link Impedance  (c) Walk time + waiting time (Transfer time)
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21. Public Transport  Graph, the outcome

22. AccessCity parameters
Day of the week
Trip start/finish time
Max time of waiting
at initial stop
Walk speed when
changing lines
Max travel time
Max number of
line changes
Calculate access area
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Calculate service area
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23. AccessCity works with any partition of the urban space: Cells
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24. AccessCity works with any partition of the urban space: buildings
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25. AccessCity is built on the neo4j graph database
http://www.neo4j.org/
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26. Service and access area in AccessCity are currently implemented
as a part of the Dijkstra shortest path algorithm
We calculate service area based
on Dijkstra algorithm, starting
from every building
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27. AccessCity is a scalable application
CALCULATION FOR ALL BUILDINGS CAN BE DONE IN PARALLEL
Performance: Service area for one building, 1-hour trip ~ 0.1 sec
Processor
Processor
Two-level parallelization
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Threads

28. SOME RESULTS
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29. Car service areas versus bus service area
Entire metropolitan area
Urban Land-uses
Car service area is essentially larger than bus service areas
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30. The center of Tel-Aviv metropolitan: Accessibility maps between 07:00 – 07:30
Job
Accessibility
07:30
07:25
07:20
07:15
07:10
07:05
07:00
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31. TAZ-resolution calculations
High-resolution calculations
We must work at high-resolution!
Average accessibility: 0.336
Relatively higher in the center
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Average accessibility: 0.356
Relatively higher at the periphery
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32. Passengers waste more time with the short trips!
Trip start: 7:00, No of transfers: 1
60 minutes trip
High-resolution: 0.336
50 minutes trip
High-resolution: 0.257
40 minutes trip
High-resolution: 0.179
30 minutes trip
High-resolution: 0.157
Low-resolution: 0.356
Low-resolution: 0.308
Low-resolution: 0.266
Low-resolution: 0.263
We could not see that at the low resolution
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33. Light rail, if combined with the existing bus network
does not improve much…
Trip start: 7:00, No of transfers: 1
60 minutes trip
Av improvement: 1.5%
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50 minutes trip
Av improvement: 2.5%
40 minutes trip
Av improvement: 3.3%
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30 minutes trip
Av improvement: 4.6%

34. Towards transportation justice
7:00, trip duration 60 min, 1 transfer
Accessibility
TA public
transportation
system is not
just!
r2 = 0.054 (r = 0.23)
Socio-economic level
TAZ Socio-economic index (1 - 20)
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35. Applications of the tool in transportation planning
• Assessment of public transport service improvements, e.g.
impacts of increase in frequencies for different population groups,
areas, land uses
• Identification of ‘pockets of inaccessibility’ in metropolitan area
• Accessibility planning for services
• Assessment of (public) transport investments, e.g., light rail
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36. The future: Trial-And-Error public transport planning
with AccessCity
Questions?
I. Benenson, K. Martens, Y. Rofé and A. Kwartler, 2010,
Measuring the Gap Between Car and Transit Accessibility Estimating Access Using a High-Resolution Transit
Network Geographic Information System, Transportation Research Record: Journal of the Transportation Research
Board, N2144, 28–35
I. Benenson, K. Martens, Y. Rofé and A. Kwartler, 2011,
Public transport versus private car: GIS-based estimation of accessibility applied to the Tel Aviv metropolitan area,
Annals of Regional Science, 47:499–515
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