big data analysis for the high-resolution view of urban public transportation accessibility
TRANSCRIPT
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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, Israel2Performit LTD, Israel (http://www.performit.co.il)
3Institute for Management Research, Radboud University Nijmegen, Holland4Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Israel
http://www.tau.ac.il/~bennya/[email protected]
Dresden, August 20131
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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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How many activities can be reached with the car from the given origin during the given time?
Transport-based component of accessibility is car-based and aggregate
Dresden, August 20133
Accessibility changes abruptly at the boundary of an area
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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
Guangzhou, June 20134
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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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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 betweenPublic 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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Service areas ratio: SAO(t) = PSAO(t)/CSAO(t)
Access area ratio: AAD(t) = PAAD(t)/CAAD(t)
We focus on measuring relative accessibility
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IN A NEW ERA OF BIG DATA WE ARE ABLE TO ESTIMATE ACCESSIBILITY EXPLICITLY!
Dresden, August 20138
Tel Aviv Metro 600 km2
2.5*106 pop 300 bus lines
Utrecht Metro500 km2
0.6*106 pop 150 bus lines
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BIG URBAN TRANSPORTATION
DATA
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Street network 104 ÷ 105 links
Attributes: traffic directions,speed
Necessary for measuring accessibility by car
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Bus lines –102 ÷ 103
Bus stops102 ÷ 103
Relation between bus lines and stops.
Necessary for measuring bus accessibility
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Bus time-table 105 ÷ 106
Necessary for measuring bus accessibility
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Buildings and jobs, 105 - 106
Necessary for measuring activity component of accessibility
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Socio-economic level
Car ownership
Necessary for measuring activity component of bus accessibility
Socio-economic level by traffic zones
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Land-uses, 105 ÷ 106
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AccessCity
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From transportation networks to graphs
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Typical metropolitan road network graph has 104 - 105 nodes and links
Node JunctionLink Road segmentImpedance Travel time
Translation of Road network into Graph is easy…
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Every travel should be represented explicitly
Origin
Initial Stop
Transfer Stop 1
Final Stop
Transfer Stop 2
Destination
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Public Transport Graph, the idea
1 2 3 4 5 6 7 8
11 12 13 14 15 16 17 18
Route 1
Route 2
6:57 7:01 7:03 7:05 7:08 7:09 7:12 7:15
6:50 6:56 7:02 7:06 7:10 7:14 7:18 7:21
Start
Travel
Destination
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1 2 3 4
Bus starts every 10 minutes
Bus starts every 30 minutes
[1, 6:57, 1, 6:57] [1,6:57, 2,7:01] [1, 6:57, 4,7:05][1,6:57, 3,7:03]
15 16 17 18
[2, 6:50, 1,7:10] [1, 6:50, 3,7:14] [1, 6:50,1,7:18] [1, 6:50,1,7:21]
[Bus route = 1, Start Time = 6:57, Stop = 4,Arrival time = 7:05]
0:04 0:02 0:02
0:05
0:04 0:04 0:03
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Node BuildingNode [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 walkNode impedance (a) Population, Number of jobsLink Impedance (a) Walk time Link Impedance (b) PT travel time Link Impedance (c) Walk time + waiting time (Transfer time)
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Public Transport Graph, the process
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Public Transport Graph, the outcome
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AccessCity parameters
Day of the weekTrip 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 areaCalculate service area
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AccessCity works with any partition of the urban space: Cells
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AccessCity works with any partition of the urban space: buildings
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AccessCity is built on the neo4j graph database http://www.neo4j.org/
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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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AccessCity is a scalable applicationCALCULATION FOR ALL BUILDINGS CAN BE DONE IN PARALLEL
Performance: Service area for one building, 1-hour trip ~ 0.1 sec
Processor
Threads
Processor
Two-level parallelization
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SOME RESULTS
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Car service area is essentially larger than bus service areas
Entire metropolitan area Urban Land-uses
Car service areas versus bus service area
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07:0007:0507:1007:1507:2007:2507:30
The center of Tel-Aviv metropolitan: Accessibility maps between 07:00 – 07:30
Job Accessibility
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Average accessibility: 0.336 Average accessibility: 0.356Relatively higher in the center Relatively higher at the periphery
We must work at high-resolution!
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Passengers waste more time with the short trips!Trip start: 7:00, No of transfers: 1
50 minutes tripHigh-resolution: 0.257Low-resolution: 0.308
60 minutes tripHigh-resolution: 0.336Low-resolution: 0.356
40 minutes tripHigh-resolution: 0.179Low-resolution: 0.266
30 minutes tripHigh-resolution: 0.157Low-resolution: 0.263
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We could not see that at the low resolution
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50 minutes tripAv improvement: 2.5%
Light rail, if combined with the existing bus network does not improve much…
Trip start: 7:00, No of transfers: 1
60 minutes tripAv improvement: 1.5%
40 minutes tripAv improvement: 3.3%
30 minutes tripAv improvement: 4.6%
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Towards transportation justice7:00, trip duration 60 min, 1 transfer
r2 = 0.054 (r = 0.23)
TAZ Socio-economic index (1 - 20)
Acc
essi
bili
ty
Socio-economic level
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TA public transportation system is not just!
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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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The future: Trial-And-Error public transport planning with AccessCity
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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
Questions?