ABSTRACT
In this paper, we provide an efficient mechanism to manage dynamic taxi trip information. Specifically, we have designed a graph storage model, which is more efficient than relational data model, for taxi dispatching problem. Our proposed model is capable of mapping dynamics of the road network in terms of taxi trip information by a set of graph instances or snapshots. G* can effectively handle the complexity and subtlety inherent in dynamic graphs and executes complex queries on large graphs using distributed operators to process graph data in parallel. We extended the G* system using our modeling strategy for transportation network. We carry out our experiments using dynamic taxi trip information in New-York area. The experimental results show the superiority of our proposed system in terms of efficient storage and processing for taxi dispatching problems. This makes the existing taxi information management system more practical and profitable by improving the overall performance.
- Arvind Arasu, Brian Babcock, Shivnath Babu, John Cieslewicz, Mayur Datar, Keith Ito, Rajeev Motwani, Utkarsh Srivastava, and Jennifer Widom. Stream: The Stanford data stream management system. Book chapter, 2004.Google Scholar
- Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, and Jennifer Widom. Models and issues in data stream systems. In Proceedings of the twenty- first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 1--16. ACM, 2002. Google ScholarDigital Library
- Hae Don Chon, Divyakant Agrawal, and Amr El Abbadi. Using space-time grid for efficient management of moving objects. In Proceedings of the 2nd ACM international workshop on Data engineering for wireless and mobile access, pages 59--65. ACM, 2001. Google ScholarDigital Library
- Santani D., Balan R.K., and Woodard J.C. Spatiotemporal efficiency in a taxi dispatch system. In Proc. IEEE. The 6th International Conference on Mobile Systems, Applications, and Services. Breckenridge, Colorado, USA, pages 1--2, June 2008Google Scholar
- Zhiming Ding and Ralf Hartmut Güting. Modeling temporally variable transportation networks. In Database Systems for Advanced Applications, pages 154--168. Springer, 2004.Google ScholarCross Ref
- Joan Feigenbaum, Sampath Kannan, Andrew McGregor, Siddharth Suri, and Jian Zhang. Graph distances in the data-stream model. SIAM Journal on Computing, 38(5):1709--1727, 2008. Google ScholarDigital Library
- S Guze. Graph theory approach to transportation systems design and optimization. 2014.Google Scholar
- Jeong-Hyon Hwang, Jeremy Birnbaum, Alan Labouseur, Paul W Olsen Jr, Sean R Spillane, Jayadevan Vijayan, and Wook-Shin Han. G*: A parallel system for efficiently managing large graphs in the cloud. Memory, pages 1--14, March 2012.Google Scholar
- Luis Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, and Luis Damas. Predicting taxi--passenger demand using streaming data. Intelligent Transportation Systems, IEEE Transactions on, 14(3):1393--1402, 2013. Google ScholarDigital Library
- Luis Moreira-Matias, Joao Moreira, Joao Gama, and Michel Ferreira. On improving operational planning and control in public transportation networks using streaming data: A machine learning approach. In Proc. IEEE ECML PKDD 2014, pages 1--10, Sep 2014.Google Scholar
- Bei Pan, Ugur Demiryurek, Farnoush Banaei-Kashani, and Cyrus Shahabi. Spatiotemporal summarization of traffic data streams. In Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming, pages 4--10. ACM, 2010. Google ScholarDigital Library
- Jussi Rasinmäki. Modelling spatio-temporal environmental data. Environmental Modelling & Software, 18(10):877--886, 2003.Google ScholarCross Ref
- Shashi Shekhar and Sanjay Chawla. Spatial databases. Upper Saddle River, NJ, 2003.Google Scholar
- Susie Stephens, Johan Rung, and Xavier Lopez. Graph data representation in oracle database 10g: Case studies in life sciences. IEEE Data Eng. Bull., 27(4):61--66, 2004.Google Scholar
- Jianwen Su, Haiyan Xu, and Oscar H Ibarra. Moving objects: Logical relationships and queries. In Advances in Spatial and Temporal Databases, pages 3--19. Springer, 2001. Google ScholarDigital Library
- Michalis Vazirgiannis and Ouri Wolfson. A spatiotemporal model and language for moving objects on road networks. In Advances in Spatial and Temporal Databases, pages 20--35. Springer, 2001. Google ScholarDigital Library
- Xianyuan Zhan, Xinwu Qian, and Satish V.Ukkusuri. Measuring the efficiency of urban taxi service system. In Proc. The 3rd International Workshop on Urban Computing (UrbComp 2014), pages 1--9, Aug 2014.Google Scholar
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