Participatory Project

http://www.ntu.edu.sg/intellisyshttp://www.ntu.edu.sg/intellisys 11
Participatory Platform for
Trip Quality Measurement
Intelligent Systems Centre, NTU

Outline
 Core Work Done For The Project
 System Architecture
 iOS/Android App Development
 Backend Development
 Data Analytics
 Project Website
 Data Collection and Testing
 Performance Evaluation
 Demo

Core Work Done For The Project
 TripQ app (both iOS and Android). Developed the trip rating
module and feedback interface for users to report bumping and
jamming events.
 Released the TripQ app to both the Apple App Store and the
Google Play Store.
 Backend server development, including database and data
processing.
 Develop the web portal for user (personal trip data visualization
and retrieval) and agency (global information visualization).
 Trip quality analytics development.

System Architecture

iOS/Android App Development
 High sampling rate (100 Hz) for accelerometer (low battery usage),
because according to sampling theory the sampling frequency should
be two times that of the signal bandwidth.
 Capture all the raw accelerometer data.
 Set the GPS sampling rate as 1 point per second (1Hz).
 Map and merge the accelerometer data between two GPS sampling
points to match with the same sampling rate of GPS.

Backend Development
 Platform access logics and portal:
• For user: obtain the trip analytics results and trip data
retrieval.
• For agency: process and obtain the global information
about the traffic based on the participatory data.
 Service Modules:
• Data Management Service: Data upload/Storage/Summary
modules
• Data Retrieve Service: ID Manager/File locator/Filter
• Data Provisioning Service
• Data Analytics Service

Data Analytics
 Core Data Analytics Algorithms
• TQI calculation and statistics (e.g. mean, standard
error)
• Pumping and jamming detection algorithm
• Distinguish bumping and jamming
• Moving time percentage calculation
• Data aggregation per day
• Data summary versus time for different areas

Bumping and Jamming Detection
Algorithm Complexity: O(n)
 Using peak searching
algorithm
• Define a peak function
• Define a window size
• Filter the results
 Peak function is based on
information entropy .
 Large value of entropy
means an event occurred.

Distinguish Bumping and Jamming
2 2
2 2 2
( ) /
| | | |
| | ( ) ( )
| | ( ) ( ) ( )
event v h total
z
v t
x y
h t t
x y z
total t t t
R Vector Vector Vector
Vector a
Vector a a
Vector a a a
= −
=
= +
= + +
eventR has value between [-1, 1], the sign can distinguish the
bumping and jamming events, the value gives the information
of how pure of the event is bumping or jamming
 Bumping will result in vertical acceleration.
 Jamming will result in horizontal acceleration.
 Bumping indicates the road situation.
 Jamming may be caused by diver behavior or road situation.
 We define an event ratio to distinguish the bumping and
jamming events.

Moving Time Calculation
Algorithm Complexity: O(n)
 Define a stop time left and right
boundary based on the location
speed data
 One time scan to calculate the
total stop time
 Calculate the moving time and
percentage

Overall TQI for Whole Trip
Using our algorithm, we compute a list of statistical metrics:
 Expected Value of instant TQI for whole trip, Ev
 Standard Deviation of instant TQI, Sd
 Events (normalized bump/jam) per minute, events
 Percentage Time in moving, moving
 User Ratings, ratings
By weighting the list of metrics and mapping them to [0,100] for the
whole trip, we compute the Overall TQI :
0.3 0.1 0.3 0.1 0.2Ev Sd events moving ratings+ + + +

Global Info Summary
 Data aggregation per day
• Aggregate the data from all the users each day to analyze
and visualize the global traffic information.
• Generate heat map to visualize a list of trip metrics
including TQI, bumping/jamming, speed.
 Data summary versus time for different areas
• Analyze the area based trip behavior along the time axis.
• For each area, a list of metrics including TQI,
bumping/jamming event, user number, trip number, data
sampling number are summarized.
• Geo location algorithms etc. such as to calculate
longitude/latitude in which area are applied to do the
statistical summary.

Project Website
The project website is :
http://sensorgrid.ntu.edu.sg/tripq/

Using the Project Website

Web GUI for User

Web GUI for Agency

Data Collection and Testing
 Internal testing:
• Start from Nov 2014. Mainly conduct data collection in NTU
campus and surrounding area.
 Collaborate with Cedar Girls’ Secondary School for platform
functional testing:
• Testing activity started from early March until now.
• More than 100 users use the App/platform.
• More than 700 trips data uploaded (over 500 trips with
enough correctly recorded data).
• After launching the activities, every day we have about 10
new trips and over 1000 data samples per day.
• The global information is summarized from Nov until now.

Performance Evaluation
 TQI calculation is based on ISO 2631 standard by processing
the accelerometer data in three axis.
 For bumping/jamming detection algorithm, we check all the
trips during 1 Mar-15 Apr 2015 with user feedback and obtain
consistent results.
 The accuracy rate (for each trip, if all the bump/jam points
feedback are covered by the algorithm, then it counts a
successful calculation) is over 86%(31/36).
 Accuracy rate for reported event detection is over
93%(169/180).
 It should be noted that our users may not always correctly give
feedback, and there is no doubt that sensor data are more
sensitive than peoples’ subjective feedback.

 After processing by our algorithm, the participatory data from
users’ smartphone provide consistent results with basic intuition
and facts:
• The trip TQI on highway is better than on small streets.
• Jamming normally happen near road turning/branch,
pedestrian crossing, or traffic lights.
• Traffic speed is lower near traffic lights and turning areas.
• The statistics about the contributed trip data and users
distribution for each area are also consistent with fact.

We calculate the distribution of all the trips’ TQI
 The TQI of most trips is below 50.
 Only small number of trips at both ends of the profile.
 Two peaks at [7-14] and [21-28].
 More than half the trips have TQI in the range [17-31].

Demo
 Client App Demo(iOS/Android)
 Web Portal Demo
 Trip Analytics

Questions ?

' cos 0 sin 1 0 0 cos sin 0 0
' 0 1 0 0 cos sin sin cos 0 0
sin 0 cos 0 sin cos 0 0 1'
x
y
z
g
g
gg
ω ω θ θ
φ φ θ θ
ω ω φ φ
  −       
         = −         
  −               
' cos 0 sin 1 0 0 0
' 0 1 0 0 cos sin 0
sin 0 cos 0 sin cos'
x
y
z
g
g
gg
ω ω
φ φ
ω ω φ φ
       
       = −       
  −           
' cos sin sin cos sin 0
' 0 cos sin 0
sin sin cos cos cos'
x
y
z
g
g
gg
ω φ ω φ ω
φ φ
ω φ ω φ ω
     
     = −     
  −       
' cos sin
' sin
cos cos'
x
y
z
g g
g g
gg
φ ω
φ
φ ω
   
   = −   
     
1 '
tan
'
x
z
g
g
φ −
=
1
2 2
'
tan
' '
y
x z
g
g g
ω − −
=
+
Step 1: calculate the rotation angle between device status and inertial coordinate
Trip Quality Index Calculation

Step 2: calculate acceleration vector component in inertial coordinate
Note:
1.The value of θ is not determined by the sensed value of acceleration (g’), but of magnetic
field. However, the value of θ has no effect on trip quality index, so it is not necessary to obtain
the value of magnetic field.
2.The sign of the accelerometer components are not important which will be squared while
calculating the trip quality index.
1 1
cos 0 sin 1 0 0 '
0 1 0 0 cos sin '
sin 0 cos 0 sin cos '
x x
y y
zz
Accel Accel
Accel Accel
AccelAccel
ω ω
φ φ
ω ω φ φ
− −
       
       = −       
  −           
cos sin sin cos sin '
0 cos sin '
sin sin cos cos cos '
x x
y y
zz
Accel Accel
Accel Accel
AccelAccel
ω φ ω φ ω
φ φ
ω φ ω φ ω
  −   
     =     
  −       
Trip Quality Index Calculation

Step 3: calculate acceleration and trip quality index
ISO 2631 standard[1]:
[1] ISO. ISO 2631-1-1997: Mechanical vibration and shock Evaluation of human exposure to whole-body vibration. International Organization for
Standardization, Geneva, Switzerland, 1997.
*Cheng-Yu Lin, Ling-Jyh Chen, Ying-Yu Chen, and Wang-Chien Lee, “Comfort Measuring System for Public Transportation Systems Using
Participatory Phone Sensing”
2 2 2
(1.4a ) (1.4a )x y z
t t t ta a= + +
Expressed in log(dB):
5 2
20log , 10 / sect
t ref
ref
a
L a m
a
−
= =
100, 103
5 ( 83)
0, 83
t
index t
t
L
Q L
L
>

= × −
 <
Trip Quality calculation:
Trip Quality index calculation

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