The document proposes a dynamic approach to optimize taxi sharing. It involves a dual-side taxi searching algorithm to retrieve taxis that can satisfy a user request while minimizing distance. A scheduling algorithm then determines the best taxi. The system architecture partitions the road network into grids and uses timestamps to determine taxi locations and routes. The approach aims to enhance taxi delivery capabilities and satisfy more passenger commute needs while reducing costs and increasing driver profits compared to individual rides.

1. OPTIMIZING TAXI SHARING USING
DYNAMIC APPROACH
MATHIAZHAGAN S
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DYNAMIC APPROACH
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2. Abstract
The significance of taxi ridesharing is often underrated. The potential of
taxi ride sharing is very vast. We focus on solving the taxi ridesharing
problem with dynamic queries and aim to minimize the total distance
significantly.
A dual side taxi searching algorithm is proposed which retrieves the
possible candidate taxis which can satisfy the user’s query.
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3. Abstract
A scheduling algorithm is used to determine the best candidate taxi
which minimizes the additional incurred travel distance.
A general routing algorithm is then used to modify the route of the taxi
accordingly.
 The proposed solution to the dynamic ridesharing problem can
enhance the delivery capability of taxis so as to satisfy the commute of
more people.
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4. Literature survey
Yuan.N. J, Zheng.Y, Zhang.L, Xie.X (2013) “T-Finder: A
Recommender System for Finding Passengers and Vacant Taxis”.
Taxi searching algorithm using a spatio-temporal index to
quickly retrieve candidate taxis that are likely to satisfy a user
query .
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5. Literature survey
D’Orey.P, Fernandes.R (2012) Empirical evaluation of a
dynamic and distributed taxi-sharing system. In IEEE Conf. on
Intelligent Transportation Systems.
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6. Proposed System Architecture
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7. Implementation Modules
1.Data Collection
2.Taxi Searching
3.Taxi Scheduling
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8. Module Description
•Data collection
Quantum Geographical Information System,
openstreet maps is deployed in it and for a certain area.
Using osm2pgrouting protocol the data of the area is
dumped into QGIS and the using Roadgraph plugin the
shortest path can be find using shortest path algorithm.
Dijiktras algorithm is used in finding the shortest path for
a given source and destination, in QGIS, vector option
helps in importing the osm data into the workspace. The
data is stored as .osm format.
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9. Module Description
•Taxi searching
A dual-side taxi searching algorithm is used to determine
the optimal list of taxis which can satisfy the rider’s request.
Since the algorithm bases its approach on both origin and
destination the result set returned is optimal.
The searching and the scheduling are done on the road
network by partitioning the network into grids. Each grid holds a
list holding a timestamp which is needed in order to determine
the location of the taxi and the route.
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10. Dynamic Taxi Searching
𝑔1
𝑔7
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11. Taxi searching
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12. Module Description
•Taxi scheduling
With given set of taxi statuses retrieved for a ride request
by the taxi searching algorithm, the purpose of the taxi
scheduling process is to find status in which satisfies with
minimum travel distance increase. All possible ways of insertion
can be created by reordering the points in the current schedule,
subject to the precedence rule, i.e. any origin point precedes the
corresponding destination point (we refer to this step as the
schedule reordering thereafter), insert origin into the schedule,
insert destination into the schedule.
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13. Hardware Requirements
HARDWARE SPECIFICATION
Hard Disk 80 GB and Above
RAM 2 GB and Above
Processor Pentium IV and Above
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14. Software Requirements
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SOFTWARE VERSION
Windows OS 7 or above
Java Development Kit Java SE 6 or above
Eclipse IDE Juno 4.2 or similar
QGIS 2.8.1
Google Maps API 3 or above
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15. System Implementation
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16. System Implementation
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17. System Implementation
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18. System Implementation
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19. System Implementation
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20. Conclusion
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Dynamic ridesharing application is used for efficient
communication between car owner and ride seeker. It is an
application aimed at reducing fuel consumption and carbon
emission. Our service can enhance the delivery capability of taxis
in a city so as to satisfy the commute of more people. Compared
with the taxi system sending passengers individually, our
ridesharing service saves the total travel distance of taxis when
delivering passengers. Our service can also save the expense of a
taxi user, while increasing the profit of a taxi driver.

21. References
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1.Calvo.R.W, de Luigi.F, Haastrup.P, and Maniezzo.V (2004) “A
distributed geographic information system for the daily
carpooling problem,” Computer Operation Research, pp. 2263-
2278.
2.Desrochers.M, Lenstra.J, Savelsbergh.M, and Soumis.F (1988)
“Vehicle routing with time windows: optimization and
approximation,” Vehicle Routing: Methods and Studies,
Amsterdam, pp. 65–84.

22. References (continued)
3.Yuan.J, Zheng.Y, Zhang.C ,Xie.X and G. Sun (2010) “An
Interactive-Voting based Map Matching Algorithm,” In Proc. of
MDM, pp. 43-52.
4.Yuan.N. J, Zheng.Y, Zhang.L, Xie.X (2013) “T-Finder: A
Recommender System for Finding Passengers and Vacant Taxis”.
IEEE TKDE, pp. 2390-2403.
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23. Thank you
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