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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.
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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
y p
erc
en
tag
e
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
e
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
![arXiv:1606.03276v3 [cs.CY] 6 Jul 2016 · Fig. 1: Smart City Ride Share Prediction System. ride sharing systems are accompanied by a smartphone or tablet based application, with which](https://img.pdfslide.net/doc/110x75/5e8b5b36bffcfd34bb0488d7/arxiv160603276v3-cscy-6-jul-2016-fig-1-smart-city-ride-share-prediction-system.jpg)
