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Real time performance of tube and traffic in London Dr David Mountain  Placr Ltd [email_address] www.placr.co.uk Possibili...
Travel network sensors <ul><li>Real-time travel: requires sensors embedded in the network, including: </li></ul><ul><ul><l...
Network health <ul><li>Caching live feeds allows past performance of networks to be analysed.  </li></ul><ul><li>Identifie...
Tube departure board archiving <ul><li>Scraping tube departure data from TFL board </li></ul><ul><ul><li>180,000 observati...
Tube departure boards
Tubes departures: mins between trains vs time of day (avg all lines)
Tubes departures: mins between trains vs time of day (avg by line)
Spatial variation in tube frequency
Spatial variation in tube frequency
Spatial variation in tube frequency
Spatial variation in tube frequency Tube freq (mins)
Spatio-temporal variation
0600-0700 Tube Freq (mins)
0700-0800 Tube Freq (mins)
0800-0900 Tube Freq (mins)
0900-1000 Tube Freq (mins)
1000-1100 Tube Freq (mins)
1100-1200 Tube Freq (mins)
1200-1300 Tube Freq (mins)
1300-1400 Tube Freq (mins)
1400-1500 Tube Freq (mins)
1500-1600 Tube Freq (mins)
1600-1700 Tube Freq (mins)
1700-1800 Tube Freq (mins)
1800-1900 Tube Freq (mins)
1900-2000 Tube Freq (mins)
2000-2100 Tube Freq (mins)
2100-2200 Tube Freq (mins)
2200-2300 Tube Freq (mins)
2300-0000 Tube Freq (mins)
Recent vs expected for Farringdon
Real-time dashboard of network health
Real-time dashboard of network health Deviation from expected frequency (mins)
Problems on Circle line Deviation from expected frequency (mins)
Dashboard over 24hrs
0600-0700
0700-0800
0800-0900
0900-1000
1000-1100
1100-1200
1200-1300
1300-1400
1400-1500
1500-1600
1600-1700
1700-1800
1800-1900
1900-2000
2000-2100
2100-2200
2200-2300
2300-0000
Traffic analysis <ul><li>For road network data, 500mn GPS data points from eCourier vehicles (vans, motorbikes, pushbikes)...
eCourier floating car data
Vans: 0000-0700
Vans: 0700-1000
Vans: 1000-1500
Vans: 1500-1900
Vans: 1900-0000
Temporally sensitive routing <ul><li>Marble Arch to Gray’s Inn Fields </li></ul><ul><li>Alternative routes for 4-wheel veh...
The morning rush: tubes vs roads
Thanks <ul><li>Contact </li></ul><ul><ul><li>www.placr.co.uk </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul>
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Real time
 performance
 of 
tube
 and 
traffic in
 London

695

Published on

Presented at:

Possibilities 
of 
Real 
Time
 Data
 Workshop
London
 Data
 Store

City
 Hall

19
 Apr 
2010


www.placr.co.uk


Published in: Travel
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  • David is the Technical Director of Placr Ltd. He has an MSc in Geographic Information Systems from Leicester University (1998) and a PhD in Information Science from City University (2006). David was a researcher and lecturer at City University from 1999 - 2008, where he developed some of the earliest location-based services and helping to launch commercial spinouts from R&amp;D projects, including the Camineo mobile guide platform now widely deployed across Europe and North America. In this academic role, David has published widely in academic journals and international conferences. In 2008, David formed Placr Ltd with Jonathan Raper, specialising in advancing geolocation technologies from proof-of-concept to commercial services. Active projects include location-aware mobile guides, meta-positioning systems, and context sensitive travel information.
  • Need viz examples of each
  • A generic overview, but what about detail
  • [could have some arrows with oddities pointed out eg hi freq Northern line south, better freq on E District line than any other &apos;out of town&apos; line, Bayswater and Old Street are lowest frequency cent London stations] A spatial, nontemporal analysis
  • Xx legend xX Red is short waiting time Yellow is long waiting time
  • How does tube frequency vary across the network over the course of a day?
  • Starting up – low frequency everywhere
  • Service gets going – good performance for main routes
  • Still improving
  • Lower freq in early afternoon
  • Freq picks up for late afternoon
  • Tailing off in evening
  • Let’s drill down to one station. What’s its expected profile (grey) What are the recent conditions (brown) Last 24hrs was generally better than expected, but what went wrong at 1400-1500?
  • So we can do a
  • For last last hour, how has performance been, relatively to expected Green higher freq Orange – as expected Ref lower freq - problems
  • Southern loop of circle line has problems – avoid?
  • Yellow is fast Red is slow
  • Transcript of "Real time
 performance
 of 
tube
 and 
traffic in
 London"

    1. 1. Real time performance of tube and traffic in London Dr David Mountain Placr Ltd [email_address] www.placr.co.uk Possibilities of Real Time Data London Data Store City Hall 19 Apr 2010
    2. 2. Travel network sensors <ul><li>Real-time travel: requires sensors embedded in the network, including: </li></ul><ul><ul><li>Vehicles with GPS; </li></ul></ul><ul><ul><li>Dedicated sensors on tube /rail networks. </li></ul></ul><ul><li>Increasingly this information is streamed live: </li></ul><ul><ul><li>within organisations for fleet mgmt; </li></ul></ul><ul><ul><li>available to public via websites. </li></ul></ul><ul><li>How can we use this to improve the traveller experience? </li></ul>
    3. 3. Network health <ul><li>Caching live feeds allows past performance of networks to be analysed. </li></ul><ul><li>Identifies which parts of network are problematic at what times: the where and when of things to avoid. </li></ul><ul><li>Temporal analysis allows travel and wait time estimates based on previous behaviour: manages expectations. </li></ul><ul><li>High spatial resolution: </li></ul><ul><ul><li>individual stations; </li></ul></ul><ul><ul><li>road sections. </li></ul></ul><ul><li>High temporal resolution: </li></ul><ul><ul><li>by hour of day. </li></ul></ul>
    4. 4. Tube departure board archiving <ul><li>Scraping tube departure data from TFL board </li></ul><ul><ul><li>180,000 observations per day </li></ul></ul><ul><li>Allows analysis of past behaviour </li></ul><ul><li>Predict future behaviour (expected) </li></ul><ul><li>Comparing current with expected behaviour highlights incidents </li></ul><ul><li>Following analysis based on 40mn observations </li></ul>
    5. 5. Tube departure boards
    6. 6. Tubes departures: mins between trains vs time of day (avg all lines)
    7. 7. Tubes departures: mins between trains vs time of day (avg by line)
    8. 8. Spatial variation in tube frequency
    9. 9. Spatial variation in tube frequency
    10. 10. Spatial variation in tube frequency
    11. 11. Spatial variation in tube frequency Tube freq (mins)
    12. 12. Spatio-temporal variation
    13. 13. 0600-0700 Tube Freq (mins)
    14. 14. 0700-0800 Tube Freq (mins)
    15. 15. 0800-0900 Tube Freq (mins)
    16. 16. 0900-1000 Tube Freq (mins)
    17. 17. 1000-1100 Tube Freq (mins)
    18. 18. 1100-1200 Tube Freq (mins)
    19. 19. 1200-1300 Tube Freq (mins)
    20. 20. 1300-1400 Tube Freq (mins)
    21. 21. 1400-1500 Tube Freq (mins)
    22. 22. 1500-1600 Tube Freq (mins)
    23. 23. 1600-1700 Tube Freq (mins)
    24. 24. 1700-1800 Tube Freq (mins)
    25. 25. 1800-1900 Tube Freq (mins)
    26. 26. 1900-2000 Tube Freq (mins)
    27. 27. 2000-2100 Tube Freq (mins)
    28. 28. 2100-2200 Tube Freq (mins)
    29. 29. 2200-2300 Tube Freq (mins)
    30. 30. 2300-0000 Tube Freq (mins)
    31. 31. Recent vs expected for Farringdon
    32. 32. Real-time dashboard of network health
    33. 33. Real-time dashboard of network health Deviation from expected frequency (mins)
    34. 34. Problems on Circle line Deviation from expected frequency (mins)
    35. 35. Dashboard over 24hrs
    36. 36. 0600-0700
    37. 37. 0700-0800
    38. 38. 0800-0900
    39. 39. 0900-1000
    40. 40. 1000-1100
    41. 41. 1100-1200
    42. 42. 1200-1300
    43. 43. 1300-1400
    44. 44. 1400-1500
    45. 45. 1500-1600
    46. 46. 1600-1700
    47. 47. 1700-1800
    48. 48. 1800-1900
    49. 49. 1900-2000
    50. 50. 2000-2100
    51. 51. 2100-2200
    52. 52. 2200-2300
    53. 53. 2300-0000
    54. 54. Traffic analysis <ul><li>For road network data, 500mn GPS data points from eCourier vehicles (vans, motorbikes, pushbikes) </li></ul><ul><li>Validated to include only purposeful journeys </li></ul><ul><li>Cross referenced with road data </li></ul><ul><li>Provides unique speed estimates for each road section, for combinations of: </li></ul><ul><ul><li>Mode of transport </li></ul></ul><ul><ul><li>Time of day </li></ul></ul>
    55. 55. eCourier floating car data
    56. 56. Vans: 0000-0700
    57. 57. Vans: 0700-1000
    58. 58. Vans: 1000-1500
    59. 59. Vans: 1500-1900
    60. 60. Vans: 1900-0000
    61. 61. Temporally sensitive routing <ul><li>Marble Arch to Gray’s Inn Fields </li></ul><ul><li>Alternative routes for 4-wheel vehicles based on time of day </li></ul>
    62. 62. The morning rush: tubes vs roads
    63. 63. Thanks <ul><li>Contact </li></ul><ul><ul><li>www.placr.co.uk </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul>
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