ICMU2019

1. A Smartphone Application
that Automatically Provides Arrival Time
based on Estimation of Train on Board
Haruki Inoue
Graduate School
of Business Administration
and Computer Science
Aichi Institute of Technology
Katsuhiko Kaji
Faculty of Information Science
Aichi Institute of Technology

2. Study Introduction
Launch
Application
A Sta. C Sta.
10:45Dep.Time
SEARCH
Searching
We consider
about the Train
Automatically
When will I arrival
at My Destination Station?
• Departure Station
• Departure Time
• Arrival Station
Input Information
None
Propose Method
Transit App We don’t remember
the Departure Time
Hurry up!
Input Information
We don’t consider
about the Train

3. Mobility as a Service (MaaS)
This is a concept that provides Various Transportation Services
through a Single Interface
Before
Ferry App Railway App Bus App
ServiceService Service
After
Mobility App
Service
Users get Information
from Each Application
Users get Information
from One Application

4. Relationship between MaaS and This Study
Users receives the Arrival Time through a Single Interface
Before
Ferry App Railway App Bus App
Arrival Information
After
Mobility App
Users considers
about Transportations
Users doesn’t considers
about Transportations
Arrival Information
Automatic
Estimation
I’m on the train
now
I want to know
the arrival time
If Users wants to know the Arrival Time

5. Related Work
An online localization method for a subway train utilizing
the barometer on a smartphone [Hyuga et al,. 2016.]
A Sta. B Sta. C Sta.
Subway Tracks
Between
A Sta.
and
B Sta.
Barometer 1010mb
Application analyzes Barometric Sensor
Log data and measured Values of Barometric Sensor
Log Data
Measured Values
*Sample

6. Related Work
An online localization method for a subway train utilizing
the barometer on a smartphone [Hyuga et al,. 2016.]
Unexpected Environment
High-Class Train
Rapid Train
Overtaking Train
Pass
Parallel Section
A Line
B Line

7. System Abstract
User Web Server
Database
Movement
History SQL
Result
Geographic
Calculation (PostGIS)
Rapid
ASta.
16:30
Dsta.
16:40
• Geographic data uses National Land Numerical Information
• Train Information is saved in GTFS format
Arrival
Information
{time,lon,lat},
{time,lon,lat},
{time,lon,lat},
• Line Geo Data
• Station Geo Data
• Time Schedule
• Train Information
1. Line Estimation
2. Direction Estimation
3. Tran Estimation
4. Fetch Arrival Time
Application can support Other Public Transportation in the future

8. System Abstract
User Web Server
Database
Movement
History SQL
Result
Geographic
Calculation (PostGIS)
Rapid
ASta.
16:30
Dsta.
16:40 Arrival
Information
{time,lon,lat},
{time,lon,lat},
{time,lon,lat},
• Line Geo Data
• Station Geo Data
• Time Schedule
• Train Information
1. Line Estimation
2. Direction Estimation
3. Tran Estimation
4. Fetch Arrival Time
• Geographic data uses National Land Numerical Information
• Train Information is saved in GTFS format
Application can support Other Public Transportation in the future

9. Calculate the likelihood of the Line
We calculate the Number that is in the Allowable Range ( )w
The number of inp w = x Total number of Geo Points = n
We set the to 140m from the Log Data of the Movement Historyw
It can cover the shift of the Movement History while stopped
Line Likelihood
l =
x
n(Range )0.0 ≤ l ≤ 1.0
Calculate this likelihood
for All Lines
Railway
p1 p2
p3
p4
p5
p6
w
Geo Point = p
Movement History by GPS of Smartphone

10. Processing in Parallel Sections
Parallel Section
A Line
B Line
If there are Multiple Lines with High Likelihood
Those Lines are also Provided
However, These cases need not be provided
• Branch from Another Lines to a the Single Line
• Merge from the Single Line to Another Lines
ld = lmax − la
• Highest Likelihood
• Likelihood of Any Line
lmax
la
When is greater than a Fixed Value
Those Lines are not also provided
ld
Therefore, we set a Threshold Value
Merge
Branch
Sta.
Sta.
Line A
LineB

11. System Abstract
User Web Server
Database
Movement
History SQL
Result
Geographic
Calculation (PostGIS)
Rapid
ASta.
16:30
Dsta.
16:40 Arrival
Information
{time,lon,lat},
{time,lon,lat},
{time,lon,lat},
• Line Geo Data
• Station Geo Data
• Time Schedule
• Train Information
1. Line Estimation
2. Direction Estimation
3. Tran Estimation
4. Fetch Arrival Time
• Geographic data uses National Land Numerical Information
• Train Information is saved in GTFS format
Application can support Other Public Transportation in the future

12. First
Sta.
p1
p3
p4
p2 Last
Sta.
First Half r1
Average Degree of Progress
r2
r1 < r2 For Last Station⋯ r1 > r2 For First Station⋯
r1 = r2 ⋯ We redo the Estimation
Direction Estimation
Second Half
Average Degree of Progress
r2
r1
r3
r4
Geo Point
We calculate the Degree of Progress ( ) for the Line using PostGISr
(0.0 ≤ r ≤ 1.0)… Percentage of the Train running from the First Station to the Liner

13. System Abstract
User Web Server
Database
Movement
History SQL
Result
Geographic
Calculation (PostGIS)
Rapid
ASta.
16:30
Dsta.
16:40 Arrival
Information
{time,lon,lat},
{time,lon,lat},
{time,lon,lat},
• Line Geo Data
• Station Geo Data
• Time Schedule
• Train Information
1. Line Estimation
2. Direction Estimation
3. Tran Estimation
4. Fetch Arrival Time
• Geographic data uses National Land Numerical Information
• Train Information is saved in GTFS format
Application can support Other Public Transportation in the future

14. Train Estimation
Sta Sta Sta
Railway
Estimated Position Estimated Position
Sta Sta Sta
Railway
Estimated Position Estimated Position
Currently
Position
d2
We calculate the Estimated Position of the Running Train on the Line
We calculate the Distance between the Estimated Position and the Current Position
These calculations are performed in Time Series on the Movement History
We calculate the RMS of Time Series of Distance We convert RMS into Likelihood
Boarding Train = Highest Likelihood Train
d1

15. How to calculate the Estimated Position?
B Sta. C Sta.
Railway
Accelerating DeceleratingConstant Speed
We propose a Model that estimates the Position of a Train
in consideration of Acceleration and Deceleration
A Sta. B Sta. C Sta.
Railway
I’m running between B sta. and C sta.
10:40 10:45
Time : 10:42
10:35
We estimate the Position between Stations based on Time
We consider the Movement of a Train

16. Total Mileage
vmax(a + 2r + d)
2
s =
Train Movement Model
Speed
Time
vmax
0
Departure
Time
as ae ds de
p1 p2a dr
AcceleratingStop Time Constant Speed Decelerating Stop Time
Arrival
TimeStart End Start End

17. Calculate the Estimated Position
vmax
v0
as ae ds de
p1 p2a dr
AcceleratingStop Time Constant Speed Decelerating Stop Time
Speed
Time
Departure
Time
Arrival
Time
t
Estimated
Position
m =
asecond(t − as)2
2
(as < t < ae)
vmaxa
2
+ vmax(t − ae) (ar ≤ t ≤ ds)
s −
esecond(de − t)2
2
(ds < t < de)
Distance from
Departure Station

18. Evaluation Experiment
We calculate Train Likelihood Ranking (in the Line)
We calculate the Percentage of Boarding Train within the 1st and 2nd
Implemented Section
We randomly generated a Movement History for 180 seconds
60 Samples (Up and Down Line) Only High-Class Cars
Nagoya Railway
Nagoya Line
Kintetsu Railway
Kyoto Line
Kintetsu Railway
Osaka Line

19. Calculate Percentage in Time Series
100%
90.0%
We calculated the Percentage every second
Because we want to observe the Time Series Change of the Percentage
Time Series Likelihood Ranking Graph
96.2%

20. Consideration
We confirmed that the Percentage
of being within 2nd was 100%
The Application provides a Train within 2nd
(in the Line)
We confirmed that the Percentage increased in Time Series
After a while, Accuracy Increases

21. Summary
We propose An Application that Automatically Provides
Transportation Arrival Information
Future Tasks
• We examine Estimation Methods in the Underground Section
• We examine Estimation Methods for Other Vehicles
We confirmed that the Percentage of being within 2nd was 100%
We confirmed that the Percentage increased in Time Series
We estimate the Train on Board from the Movement History