Big Data Analysis for the High-Resolution View of Urban Public Transportation Accessibility

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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

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Big Data Analysis for the High-Resolution View of Urban Public Transportation Accessibility

  1. 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 1 Dresden, August 2013
  2. 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 2 Dresden, August 2013
  3. 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 3 Dresden, August 2013
  4. 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. 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) 5 Dresden, August 2013
  6. 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) 6 Dresden, August 2013
  7. 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) 7 Dresden, August 2013
  8. 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 8 Dresden, August 2013
  9. 9. BIG URBAN TRANSPORTATION DATA 9 Dresden, August 2013
  10. 10. Street network 104 ÷ 105 links Attributes: traffic directions, speed Necessary for measuring accessibility by car 10 Dresden, August 2013
  11. 11. Bus lines – 102 ÷ 103 Bus stops 102 ÷ 103 Relation between bus lines and stops. Necessary for measuring bus accessibility 11 Dresden, August 2013
  12. 12. Bus time-table 105 ÷ 106 Necessary for measuring bus accessibility 12 Dresden, August 2013
  13. 13. Buildings and jobs, 105 - 106 Necessary for measuring activity component of accessibility 13 Dresden, August 2013
  14. 14. Socio-economic level by traffic zones Land-uses, 105 ÷ 106 Car ownership Necessary for measuring activity component of bus accessibility Socio-economic level 14 Dresden, August 2013
  15. 15. AccessCity 15 Dresden, August 2013
  16. 16. From transportation networks to graphs 16 Dresden, August 2013
  17. 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 17 Dresden, August 2013
  18. 18. Every travel should be represented explicitly Destination Transfer Stop 1 Transfer Stop 2 Final Stop Initial Stop Origin 18 Dresden, August 2013
  19. 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] 19 0:04 16 0:04 [1, 6:50, 3,7:14] 17 [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. 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) 20 Dresden, August 2013
  21. 21. Public Transport  Graph, the outcome
  22. 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 22 Calculate service area Dresden, August 2013
  23. 23. AccessCity works with any partition of the urban space: Cells 23 Dresden, August 2013
  24. 24. AccessCity works with any partition of the urban space: buildings 24 Dresden, August 2013
  25. 25. AccessCity is built on the neo4j graph database http://www.neo4j.org/ 25 Dresden, August 2013
  26. 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 26 Dresden, August 2013
  27. 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 27 Dresden, August 2013 Threads
  28. 28. SOME RESULTS 28 Dresden, August 2013
  29. 29. Car service areas versus bus service area Entire metropolitan area Urban Land-uses Car service area is essentially larger than bus service areas 29 Dresden, August 2013
  30. 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 30 Dresden, August 2013
  31. 31. TAZ-resolution calculations High-resolution calculations We must work at high-resolution! Average accessibility: 0.336 Relatively higher in the center 31 Average accessibility: 0.356 Relatively higher at the periphery Dresden, August 2013
  32. 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 32 Dresden, August 2013
  33. 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% 33 50 minutes trip Av improvement: 2.5% 40 minutes trip Av improvement: 3.3% Dresden, August 2013 30 minutes trip Av improvement: 4.6%
  34. 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) 34 Dresden, August 2013
  35. 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 35 Dresden, August 2013
  36. 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 36 Dresden, August 2013

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