Abstract
The emergence of more complex, data-intensive applications motivates a high demand of effective data modeling for graph databases to support efficient query answering. In this paper, we develop an intuitive graph data model for dynamic taxi ride sharing. We argue that our proposed data model meets the data needs imposed by three fundamental tasks associated with taxi ride sharing. An experiment consisting of a taxi ride sharing simulation with real-world data demonstrates the effectiveness of our modelling approach.
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Notes
- 1.
For simplicity, we assume in this work that each taxi has just one taxi shift.
- 2.
This change time is not considered in some publications on the DTRP even though it has severe implications on ride sharing efficiency, since picking up passengers causes a schedule delay even if the pickup location is on the taxi route.
- 3.
For a better overview, we show the graph model with its nodes and relationships, but do not visualize the properties stored for nodes and relationships.
- 4.
In the literature this term is often used based on travel distance. Road segments, however, can have different travel speeds which leads to the invalidity of the triangle inequality on the road network. The path with the lowest total travel distance between two locations might not necessarily be the shortest path between them.
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Steinmetz, D., Merz, F., Ma, H., Hartmann, S. (2019). A Graph Model for Taxi Ride Sharing Supported by Graph Databases. In: Laender, A., Pernici, B., Lim, EP., de Oliveira, J. (eds) Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11788. Springer, Cham. https://doi.org/10.1007/978-3-030-33223-5_10
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