Download - Urban Computing with Taxicabs Yu Zheng Microsoft Research Asia.

Transcript
  • Slide 1
  • Urban Computing with Taxicabs Yu Zheng Microsoft Research Asia
  • Slide 2
  • Motivation Urban computing for Urban planning Developing countries: Urbanization and city planning Developed countries: Urban reconstruction, city renewal, and sub- urbanization Questions Whats wrong with the city configurations? Does a carried out urban planning really works?
  • Slide 3
  • GPS-equipped taxis are mobile sensors
  • Slide 4
  • RankCitiesCountry/RegionTaxicabs 1The Mexico cityMexico103,000+ 2BangkokThailand80,000+ 3SeoulSouth Korea73,000+ 4BeijingChina67,000 5TokyoJapan60,000 6ShanghaiChina50,000+ 7New York CityUSA48,300 8buenos airesArgentina45,000 9MoscowRussia40,000 (1000,000) 10St.PaulBrazil37,000 11TianjinChina35,000 12TaipeiTaiwan31,000+ 13New Taipei CityTaiwan23,500 14Singapore 23,000 15OsakaJapan20,000 16Hong KongChina18,000+ 17WuhanChina18,000 18LondonEngland17,000 19HarbinChina17,000 20GuangzhouChina16,000+ 21ShenyangChina15,000+ 22ParisFrance15,000
  • Slide 5
  • What We Do Detect flawed urban planning using taxi trajectories Evaluate the carried out city configurations Reminder city planners with the unrecognized problems Challenges City-wide traffic modeling Embodying flaws and reveal their relationship
  • Slide 6
  • Methodology Partition a city into regions with major roads
  • Slide 7
  • Methodology Partition the trajectory dataset into some portions TimeWork dayRest day Slot 17:00am-10:30am9:00am-12:30pm Slot 210:30am-4:00pm12:30pm-7:30pm Slot 34:00pm-7:30pm7:30pm-9:00am Slot 47:30pm-7:00am WorkdayRest day
  • Slide 8
  • Methodology Project taxi trajectories onto these regions Building a region graph for each time slot
  • Slide 9
  • Methodology
  • Slide 10
  • Slide 11
  • Formulate skyline graphs Mining frequent patterns To avoid false alert Deep understanding
  • Slide 12
  • Evaluations Datasets2009. 3-52010.3-6 Number of taxis29,28630,121 Effective days89116 Number of points Total679M1,730M Per taxi/day306528 Distance (KM) Total310M600M Per taxi/day128171 Average sampling rate (s)10074 Ave. dist. between two points (m)457349
  • Slide 13
  • WorkdaysRest Days 2009 2010
  • Slide 14
  • Some flaws occurring in 2009 disappeared Example 1: Two roads launched in late 2009 Results
  • Slide 15
  • Some flaws occurring in 2009 still exist in 2010 Example 1: Subway line 14 and 15 Results
  • Slide 16
  • Conclusion Video
  • Slide 17
  • Thanks! Yu Zheng http://research.microsoft.com/en-us/people/yuzheng/ The Released Dataset: T-Drive taxi trajectories A demo in the demo session on Sept. 20.