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

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event v h total

zv t

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R Vector Vector Vector

Vector a

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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 WebsiteThe project website is :http://sensorgrid.ntu.edu.sg/tripq/

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.


Performance Evaluation 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.


Performance EvaluationWe 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

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' cos 0 sin 1 0 0 0' 0 1 0 0 cos sin 0

sin 0 cos 0 sin cos'

x

y

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gg

' cos sin sin cos sin 0' 0 cos sin 0

sin sin cos cos cos'

x

y

z

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gg

' cos sin' sin

cos cos'

x

y

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1 'tan'x

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Step 1: calculate the rotation angle between device status and inertial coordinateTrip Quality Index Calculation


Step 2: calculate acceleration vector component in inertial coordinateNote: 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 1cos 0 sin 1 0 0 '0 1 0 0 cos sin 'sin 0 cos 0 sin cos '

x x

y y

zz

Accel AccelAccel Accel

AccelAccel

cos sin sin cos sin '0 cos sin 'sin sin cos cos cos '

x x

y y

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Accel AccelAccel Accel

AccelAccel

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”

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Expressed in log(dB): 5 220log , 10 / sectt ref

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Trip Quality calculation:

Trip Quality index calculation

Documents

Participatory Project