Parallel map matching on massive vehicle GPS data using MapReduce

Jian Huang; Shaoqing Qiao; Haitao Yu; Jinhui Qie; Chunwei Liu

doi:10.1109/HPCC.and.EUC.2013.211

Parallel map matching on massive vehicle GPS data using MapReduce

Jian Huang
, Shaoqing Qiao
, Haitao Yu
, Jinhui Qie
, Chunwei Liu

School of Software

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

The procedure of matching vehicle location data onto road map is very essential for many ITS (Intelligent Transportation System) applications. However, with the boosting deployment of GPS devices in vehicles, the accumulation of huge amount of GPS data caused great challenge on the efficiency and scalability of traditional serial map matching algorithm. In this paper we address the challenge by presenting a novel parallel map matching algorithm to realize high-performance processing of GPS data. The main idea is to adapt the serial map matching algorithm for cloud computing environment by reforming its' data-intensive or I/O-intensive computing stages using MapReduce paradigm. We implemented the algorithm in Hadoop platform and tested its performance by a large GPS dataset exceeds 120 billion GPS records. Experimental results show that our approach is highly efficient and scalable for massive historical GPS data processing.

Original language	English
Title of host publication	Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013
Publisher	IEEE Computer Society
Pages	1498-1503
Number of pages	6
ISBN (Print)	9780769550886
DOIs	https://doi.org/10.1109/HPCC.and.EUC.2013.211
State	Published - 2014
Event	15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013 - Zhangjiajie, Hunan, China Duration: 13 Nov 2013 → 15 Nov 2013

Publication series

Name	Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013

Conference

Conference	15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013
Country/Territory	China
City	Zhangjiajie, Hunan
Period	13/11/13 → 15/11/13

Keywords

Cloud-Commputing
Hadoop
Map Matching
MapReduce

Access to Document

10.1109/HPCC.and.EUC.2013.211

Cite this

Huang, J., Qiao, S., Yu, H., Qie, J., & Liu, C. (2014). Parallel map matching on massive vehicle GPS data using MapReduce. In Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013 (pp. 1498-1503). Article 6832093 (Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013). IEEE Computer Society. https://doi.org/10.1109/HPCC.and.EUC.2013.211

Huang, Jian ; Qiao, Shaoqing ; Yu, Haitao et al. / Parallel map matching on massive vehicle GPS data using MapReduce. Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013. IEEE Computer Society, 2014. pp. 1498-1503 (Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013).

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title = "Parallel map matching on massive vehicle GPS data using MapReduce",

abstract = "The procedure of matching vehicle location data onto road map is very essential for many ITS (Intelligent Transportation System) applications. However, with the boosting deployment of GPS devices in vehicles, the accumulation of huge amount of GPS data caused great challenge on the efficiency and scalability of traditional serial map matching algorithm. In this paper we address the challenge by presenting a novel parallel map matching algorithm to realize high-performance processing of GPS data. The main idea is to adapt the serial map matching algorithm for cloud computing environment by reforming its' data-intensive or I/O-intensive computing stages using MapReduce paradigm. We implemented the algorithm in Hadoop platform and tested its performance by a large GPS dataset exceeds 120 billion GPS records. Experimental results show that our approach is highly efficient and scalable for massive historical GPS data processing.",

keywords = "Cloud-Commputing, Hadoop, Map Matching, MapReduce",

author = "Jian Huang and Shaoqing Qiao and Haitao Yu and Jinhui Qie and Chunwei Liu",

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doi = "10.1109/HPCC.and.EUC.2013.211",

language = "英语",

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booktitle = "Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013",

address = "美国",

note = "15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013 ; Conference date: 13-11-2013 Through 15-11-2013",

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Huang, J, Qiao, S, Yu, H, Qie, J & Liu, C 2014, Parallel map matching on massive vehicle GPS data using MapReduce. in Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013., 6832093, Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013, IEEE Computer Society, pp. 1498-1503, 15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013, Zhangjiajie, Hunan, China, 13/11/13. https://doi.org/10.1109/HPCC.and.EUC.2013.211

Parallel map matching on massive vehicle GPS data using MapReduce. / Huang, Jian; Qiao, Shaoqing; Yu, Haitao et al.
Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013. IEEE Computer Society, 2014. p. 1498-1503 6832093 (Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parallel map matching on massive vehicle GPS data using MapReduce

AU - Huang, Jian

AU - Qiao, Shaoqing

AU - Yu, Haitao

AU - Qie, Jinhui

AU - Liu, Chunwei

PY - 2014

Y1 - 2014

N2 - The procedure of matching vehicle location data onto road map is very essential for many ITS (Intelligent Transportation System) applications. However, with the boosting deployment of GPS devices in vehicles, the accumulation of huge amount of GPS data caused great challenge on the efficiency and scalability of traditional serial map matching algorithm. In this paper we address the challenge by presenting a novel parallel map matching algorithm to realize high-performance processing of GPS data. The main idea is to adapt the serial map matching algorithm for cloud computing environment by reforming its' data-intensive or I/O-intensive computing stages using MapReduce paradigm. We implemented the algorithm in Hadoop platform and tested its performance by a large GPS dataset exceeds 120 billion GPS records. Experimental results show that our approach is highly efficient and scalable for massive historical GPS data processing.

AB - The procedure of matching vehicle location data onto road map is very essential for many ITS (Intelligent Transportation System) applications. However, with the boosting deployment of GPS devices in vehicles, the accumulation of huge amount of GPS data caused great challenge on the efficiency and scalability of traditional serial map matching algorithm. In this paper we address the challenge by presenting a novel parallel map matching algorithm to realize high-performance processing of GPS data. The main idea is to adapt the serial map matching algorithm for cloud computing environment by reforming its' data-intensive or I/O-intensive computing stages using MapReduce paradigm. We implemented the algorithm in Hadoop platform and tested its performance by a large GPS dataset exceeds 120 billion GPS records. Experimental results show that our approach is highly efficient and scalable for massive historical GPS data processing.

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BT - Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013

PB - IEEE Computer Society

T2 - 15th IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2013

Y2 - 13 November 2013 through 15 November 2013

ER -

Huang J, Qiao S, Yu H, Qie J, Liu C. Parallel map matching on massive vehicle GPS data using MapReduce. In Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013. IEEE Computer Society. 2014. p. 1498-1503. 6832093. (Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013). doi: 10.1109/HPCC.and.EUC.2013.211

Parallel map matching on massive vehicle GPS data using MapReduce

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