smart ride

Post on 08-Jul-2015

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DESCRIPTION

Smart Ride is my final semester masters project. It is a very innovative idea that exploits the daily travel pattern of people. It is an android app that learns a users’ day to day travel pattern and determines the locations a user spends most of his time. With this information Smart Ride makes intelligent decisions to pair him up with people having similar travel patterns to share cars and encourage car pooling.

TRANSCRIPT

SMART RIDEA Location Profiling Service

Overview

Manual input from the user – NONE

Collect GPS history of a user

Find out the stay points (Time-space based clustering) Ex: Home, College

Find out the tracks ( Home -> College)

Predict users location

Suggest car poolers to the predicted location

3rd Milestone -

Accomplishments

Suggesting neighbors

Providing confidence value(most likely, likely, may be) of

the co-travelers.

Better ranking and matching of the car poolers. Adding

social strength between two users.

Finding common places among users, and suggest them

for the new incoming users.

Improving accuracy of prediction.

Neighbors in the co-traveler

suggestions

Find primary stay points of all users

If two primary stay points are near by, then the two users

are neighbors

Android App - Screenshots

DRIVER SUGGESTIONS DRIVER DETAILS

Android App - Screenshots

POPULAR PLACES LIST OF PLACES

Android Application

FAVOURITE PLACES SCREEN

Android Application

LOGIN

SCREEN

SIGN UP

FORMSTATUS BAR

NOTIFICATION

SETTINGS

SCREEN

Confidence Level

How likely is a user going to a particular location at a

particular time.

More similar his mobility profile, higher will be confidence.

Number of days he has gone to a location

at that time

Total number of days the user has travelled

CONFIDENCE

LEVEL

Ranking Score

Drivers are sorted based on Ranking Score

Factors influencing Ranking Score–

Time difference between request and departure

Distance Difference

All factors are weighted and normalized to get the Ranking

Score

Accuracy of Prediction

0

20

40

60

80

100

120

16 27 28 32 8 19 4 31 18 24 29 30 23 33 21 20 7 14 15 12 13 17 1 34 35 26 9 5 11 10 6 2 3 22

Accu

rac

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erc

en

tag

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User IDs

Accuracy Prediction of Individual users - using DOW

Acc

interval

Users

%

0-33 26%

34-66 38%

67-100 36%

Accuracy of Prediction

0

20

40

60

80

100

120

16 27 28 32 8 19 4 31 18 24 29 30 23 33 21 20 7 14 15 12 13 17 1 34 35 26 9 5 11 10 6 2 3 22 25

Accu

rac

y p

erc

en

tag

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User IDs

Accuracy Prediction of Individual users - without DOW

Acc

interval

Users

%

0-33 33%

34-66 42%

67-100 25%

Suggestion Hit Rate

0

20

40

60

80

100

120

16 27 28 32 8 19 4 31 18 24 29 30 23 33 21 20 7 14 15 12 13 17 1 34 35 26 9 5 11 10 6 2 3 22 25

Hit

Rate

User IDs

Other experiments - Improving

the Accuracy

Step 1: Get the transition matrix, which indicates the

probabilities of going from 1 location to another location.

Step 2: Get all the possible locations he/she has gone in

the given time frame from his current location.

Step 3: Now combine these frequencies of visits with their

respective probabilities from the transition matrix. And

then suggest the location with the highest combined

value.

Other experiments - Static

Zoning

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Source Zone 7

Dest. Zone 14

Other experiments – Social

strength of a connection

Collected friends details of users from Facebook API

Thank You

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