東京都市大において、東大生研の若手研究者による連続講義が「大学院特別講義」として行われています。これは、2016年11月23日に情報分野の講義を担当した際の資料です。 https://www.tcu.ac.jp/ttrenkei/news/20160913.html

1. H28
IT
:

2. (Twitter @niyalist)
◦
◦
◦
◦ 2008-2010
◦ 2010-2013
◦ 2013-

3. 1:
1.
2.
3.
4.

4. 2:
1.
2.
3.

5. 3:
1.
2.
3. Twitter LINE SNS

6. http://www.keishicho.metro.tokyo.jp/kotsu/jikoboshi/koreisha/koreijiko.html

7. 2013 CM

8. /
◦
http://giphy.com/gifs/l41lSVMtkWblDT04M

9. ◦
◦
◦
http://www.wired.com/2014/03/rising-mass-transit-ridership/

10. IT

11. LRT BRT
Uber Lyft
1

12. IT
◦ IT
◦ IT
◦

13. IT
◦
◦
◦
◦

14. NAVYA ARMA
◦ 15
◦ 5 13
◦
◦
◦ LIDAR
◦ RTK GPS cm
◦

15. ◦ 15 ARMA 2
BestMile
◦ EPFL
◦
◦

16. EasyMile
◦ 2014
EZ10
◦ 12 6
◦ 20km/h 40km/h
◦ 14
◦
◦
◦ :

17. DeNA EasyMile
◦ Robot Shuttle
2016 8
2016 11

18. 2016 2 -3
◦
◦

19. IT
IT

20. car2go
◦
◦ 8 30
◦
◦
◦
※
◦
※ 1
2015 10
http://business.nikkeibp.co.jp/atcl/report/15/110879/102600116/

21. 2009
2011 SF
2013
2015 CMU 40
2015
2016
2016

22. Uber
UberX
◦
UberPOOL
◦ Uber X
◦
◦ Lyft Lyft Line

23. ◦ 27
◦ Airbnb Uber Lyft
◦
◦
◦
◦
◦

24. Kutsuplus
2013
◦
5 10 45
◦
9
◦ 3.5
◦
AALT

25. http://www.muotoilutarinat.fi/en/project/kutsuplus/

26. 2015
◦ 2015 : 15
◦ 2016 45
◦ 2017 100

27. chariot
◦
◦ 2014
◦
◦ 28 100
2016 Ford Smart Mobility
◦

28. ◦ 31 20
http://www.nakl.t.u-tokyo.ac.jp/odt/index.html

29. IT

31. vison

32. Moovel
◦
◦
◦

34. by 2006
◦
◦ JR

35. ◦
◦
◦ 2011 6 2013 2
◦ 70

36. 2011 1
◦ 10 70 100 : 56:44
◦ 19
◦ 28
◦ 53
◦ 23
◦ 7
19%
28%
53%
PC
28%
携帯電話
31%
スマート
フォン
36%
専用設置
端末
5%

37. iPhone, Android

39. ◦
◦
◦ Web
◦
◦
◦
◦
◦
1
◦ 1
◦ : google maps →

40. AR
AR
◦
◦
◦
次発車まで 5:30
鳥取駅行

41. →
便利なシステム
見つけたよ。 こんな便利なもの
があったのか。 今度使って
みようか
な。

42. Twitter
◦
◦
URL

43. ◦ 12 14:23
Android

44. 1.
2.
3.
4.

45. 1

46. →

50. ◦
◦
◦

51. …

52. スマートフォンを⽤いた
地下鉄位置情報の実現

53. ◦ 2013 3
◦
◦

54. GPS
GPS is not available on subway because we cannot receive GPS signal.
55
?????? ???

55. Wi-Fi
Wi-Fi
Wi-Fi
56
???

56. :
◦ GPS

57. 1:
/
◦ SubwayPS
◦
◦
※ Stockx, T., Hecht, B. and Schoning, J.:
SubwayPS: To- wards Smartphone
Positioning in Underground Public
Transportation Systems, Proc. SIGSPATIAL,
pp. 93– 102 (2014).

58. 2:
→
→
/
Takamasa Higuchi,
Hirozumi Yamaguchi,
and Teruo Higashino
Tracking Motion Context of
Railway Passengers by Fusion
of Low-Power Sensors in
Mobile Devices.
In ISWC, 2015

59. 1:
◦ 281
◦

60. ◦
◦
◦
◦
◦
◦ OS

61. SmartKompano
◦
◦ GPS

64. ◦
◦ OS GPS Wi-Fi
iOS 8.1
◦
◦

65. 300m /
◦
20

66. iPhone 5S 24
◦
◦
◦
◦

68. Background Activity
◦ Moves

69. ◦ 5
◦
◦

70. 2:
◦
◦

71. 0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50 60 70 80
Altitude[m]
Time[sec]
iPhone6-A
iPhone6-B
iPhone6 Plus

72. : , , , "
", , 56(1), pp.260-272, 2015 1 .

73. Time
[hPa]

74. 1: /
◦ 10 (13 )
Time

75. Time
→
→

76. Time
/
6 9
◦

77. 2:
/

78. 2:
※
◦
◦
38.6m 10.9m 11.6m 15.2m 11.7m -4.0m -10.9m -3.2m -12.4m
10.3m -2.6m -8.3m -1.8m
※

79. /
80

80. Swift
Swift
Swift

81. / 4 3
82
Line Stations Time [min.]
25 50
14 30
18 38
21 30

82. 1: /
Recall is 85% to 97%, precision is 67% to 92%
83
Line Precision Recall F-measure
Asc 0.913 0.973 0.942
Desc 0.864 0.973 0.914
Asc 0.917 0.929 0.916
Desc 0.735 0.857 0.791
Asc 0.788 0.912 0.845
Desc 0.801 0.947 0.866
Asc 0.669 0.952 0.786
Desc 0.775 0.937 0.846
Total 0.808 0.945 0.836

83. 2:
Using actual estimated motion state.
n is the number of the station used for estimation.
Because of motion state errors, accuracy rate is low.
84
Line
Accuracy rate
n = 2 n = 3 n = 4 n = 5
Asc 0.348 0.515 0.619 0.667
Desc 0.290 0.455 0.571 0.633
Asc 0.333 0.818 0.833 0.778
Desc 0.333 0.424 0.367 0.333
Asc 0.392 0.583 0.644 0.714
Desc 0.373 0.646 0.533 0.619
Asc 0.281 0.352 0.431 0.417
Desc 0.427 0.537 0.510 0.521
Total 0.327 0.498 0.527 0.553

85. 86

86. ACM SIGSPATIAL
Satoshi Hyuga, Masaki Ito, Masayuki Iwai, Kaoru Sezaki, "Estimate a User's Location Using Smartphone's Barometer on a Subway", 5th
International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT 2015), Nov. 2015.

87. ◦ 2
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◦ /
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◦