Real time
 performance
 of 
tube
 and 
traffic in
 London
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Real time
 performance
 of 
tube
 and 
traffic in
 London

on

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Presented at:

Presented at:

Possibilities 
of 
Real 
Time
 Data
 Workshop
London
 Data
 Store

City
 Hall

19
 Apr 
2010


www.placr.co.uk


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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&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 'out of town' 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

Real time
 performance
 of 
tube
 and 
traffic in
 London Real time
 performance
 of 
tube
 and 
traffic in
 London Presentation Transcript

  • 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
  • Travel network sensors
    • Real-time travel: requires sensors embedded in the network, including:
      • Vehicles with GPS;
      • Dedicated sensors on tube /rail networks.
    • Increasingly this information is streamed live:
      • within organisations for fleet mgmt;
      • available to public via websites.
    • How can we use this to improve the traveller experience?
  • Network health
    • Caching live feeds allows past performance of networks to be analysed.
    • Identifies which parts of network are problematic at what times: the where and when of things to avoid.
    • Temporal analysis allows travel and wait time estimates based on previous behaviour: manages expectations.
    • High spatial resolution:
      • individual stations;
      • road sections.
    • High temporal resolution:
      • by hour of day.
  • Tube departure board archiving
    • Scraping tube departure data from TFL board
      • 180,000 observations per day
    • Allows analysis of past behaviour
    • Predict future behaviour (expected)
    • Comparing current with expected behaviour highlights incidents
    • Following analysis based on 40mn observations
  • 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
    • For road network data, 500mn GPS data points from eCourier vehicles (vans, motorbikes, pushbikes)
    • Validated to include only purposeful journeys
    • Cross referenced with road data
    • Provides unique speed estimates for each road section, for combinations of:
      • Mode of transport
      • Time of day
  • eCourier floating car data
  • Vans: 0000-0700
  • Vans: 0700-1000
  • Vans: 1000-1500
  • Vans: 1500-1900
  • Vans: 1900-0000
  • Temporally sensitive routing
    • Marble Arch to Gray’s Inn Fields
    • Alternative routes for 4-wheel vehicles based on time of day
  • The morning rush: tubes vs roads
  • Thanks
    • Contact
      • www.placr.co.uk
      • [email_address]