For productive taxi business based on GPS has turned into an imperative instrument, the taxi fleet management system. It can be utilized giving helpful information to cab drivers to acquire more benefit by mining the authentic GPS directions furthermore for the purpose of armada administration.

1. A
presentation on
Smart Traveller- Proficient Taxi Business
Application
Presented By
Gaurav Kumbhar
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2. Contents
• Introduction
• Literature Survey
• Existing System
• Problem Statement
• Objective of project
• Proposed System
• Architecture/ block
diagram
• Hardware and Software
Requirements.
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• Design analysis (various
Diagrams)
• Algorithmic Strategy
• Mathematical Model
• Applications
• Conclusion
• References

3. Literature Survey
• Finder: A Recom-mender System for Finding Passengers and
Vacant Taxis
Recommended System (Get on and Get off location)
Report on present location (GPS).
 This recommendation helps to reduce the cruising (without a fair)
time of taxi.
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4. • A Trajectory Recommendation System for Effective and
Efficient Hunting of Taxi Passengers
Global-optimal trajectory retrieving
Proposes a dynamic scoring system to evaluate each road segment
in different time periods by considering both pick up rate and profit
factor.
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5. Existing System
• The vacant Taxi driver have to move from point to point for picking
up of passenger which increases wastage of fuel, time and there will
be creation of more unnecessary traffic in city.
• The taxi driver has to keep himself his own record into mind where
and how he gets more income time to time.
• The problem may arise like the need for taxi in another area and taxi
driver is searching for passenger in another area.
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6. Continue…
• Sometimes problem of misguidance of route for new passengers also
arises.
• Passengers also don’t know average fare of trip before reaching
destination.
• The Existing system are:
• Easy taxi System
• CMS Mobile(Cab Management system)
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7. Problem Statement
• The proposed system provides useful information for taxi drivers to earn
more profit by mining the historical GPS trajectories. It is an important
system for efficient taxi business.
• This system is used for finding next cruising location. There are four
factors have been considered, which are
• 1. Distance between the current location and the recommended location,
• 2. Waiting time for next passengers,
• 3. Expected fare for the trip and
• 4. The last factor based on driver’s experience.
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8. Objective
• To capture the relation between the passengers get-off location and the
next passenger get-on location
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9. Proposed System
• Taxi recommender system provide the best user interface between the
taxi driver and the passengers.
• This system is more useful for increasing the revenue of taxi drives.
• This give the analysis report to taxi driver which helps him to know
how he gets more profit on the particular route and at particular time.
• To use the System both have to compulsory fill registration details.
• Taxi driver
• Passenger.
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10. System Architecture
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11. Module
There are three modules in this system,
• Taxi driver
• Central server
• Passenger
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12. UML Diagrams
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Data Flow Diagram:

13. System Requirements
Hardware Interfaces
processor – dual-core machine or latest.
Ram – 2 GB
HDD – 80 GB
Android phone
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14. Software Interfaces
Design tools – rational rose
Development tool – net bean
Development kit – java SDK and android SDK
Development platform – windows
Others – Microsoft office
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15. Algorithms Strategy
• K-means Algorithm.
• K-Medoid Algorithm.
• KNN.
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16. May 20, 2017 Data Mining: Concepts and Techniques 16
K-Means Clustering
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K=2
Arbitrarily choose K
object as initial
cluster center
Assign
each
objects
to most
similar
center
Update
the
cluster
means
Update
the
cluster
means
reassignreassign

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K-Medoid Algorithm.
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18. The k-Nearest Neighbor
Algorithm
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19. Mathematical Model
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20. Applications
• This system can be used to any taxi agency to improve its business.
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21. Conclusion
• This system implements an architecture in which Spatial-temporal
analysis is the main advantage which is not existing at all in any taxi
booking system. In simulation part, we are plotting graphs which are
helpful for taxi driver to see his revenue and through which he gets his
profitable location. We use grid based clustering algorithm for
clustering maps and k-means algorithm for spatial-temporal analysis.
We adopt greedy strategy that adjust only one factor at a time until our
system reaches optimal goal i.e. maximize revenue.
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22. References
• Yu-Ling Hsueh, Ren-Hung Hwang, and Yu-Ting Chen, ”An Effective Taxi
Recom-mender System Based on a Spatiotemporal Factor Analysis Model” ,
International conference on computing ,Networking and communication ,Mobile
Computing and Vehicle Communication Symposium,2014,pp.429-433
• Nicholas Jing Yuan,Yu Zheng,Liuhang Zhang, and Xing Xie, ”T-Finder: A Recom-
mender System for Finding Passengers and Vacant Taxis” ,IEEE
TRANSACTIONSON KNOWLEDGE AND DATA ENGINEERING, VOL. 25,
NO. 10, OCTOBER 2013 ,pp 2390-2403
• Ye Ding, Siyuan Liu, Jiansu Pu, Lionel M. Ni, ”HUNTS: A Trajectory
Recommendation System for Effective and Efficient Hunting of Taxi Passengers” ,
2013 IEEE 14th International Conference on Mobile Data Management ,pp 107-
116
• L. Moreira-Matias, R. Fernandes, J. Gama, M. Ferreira, J. o. Mendes-Moreira, and
L. Damas, An online recommendation system for the taxi stand choice problem
(poster), in VNC, 2012, pp. 173180.
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