Download - Dynamic ridesharing
OPTIMIZING TAXI SHARING USING DYNAMIC APPROACH
MATHIAZHAGAN S
10-Apr-15 OPTIMIZING TAXI SHARING USING DYNAMIC APPROACH 1
AbstractThe 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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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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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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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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Proposed System Architecture
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Implementation Modules
1.Data Collection
2.Taxi Searching
3.Taxi Scheduling
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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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Module Description
• Taxi searchingA 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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Dynamic Taxi Searching
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Taxi searching
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Module Description
• Taxi schedulingWith 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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Hardware Requirements
HARDWARE SPECIFICATION
Hard Disk 80 GB and Above
RAM 2 GB and Above
Processor Pentium IV and Above
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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
OPTIMIZING TAXI SHARING USING DYNAMIC APPROACH
System Implementation
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System Implementation
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System Implementation
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System Implementation
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System Implementation
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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.
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.
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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Thank you
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