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CLC number: TP399; U495

On-line Access: 2016-12-13

Received: 2016-01-19

Revision Accepted: 2016-04-11

Crosschecked: 2016-11-08

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Citations:  Bibtex RefMan EndNote GB/T7714


Aftab Ahmed Chandio

http://orcid.org/0000-0002- 5752-0520

Fan Zhang


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.12 P.1305-1319


Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories

Author(s):  Aftab Ahmed Chandio, Nikos Tziritas, Fan Zhang, Ling Yin, Cheng-Zhong Xu

Affiliation(s):  Shenzhen Institutes of Advanced Technology, Chinese of , Shenzhen 518055, China; more

Corresponding email(s):   chandio.aftab@usindh.edu.pk, zhangfan@siat.ac.cn

Key Words:  Map-matching, GPS trajectories, Tuning-based, Cloud computing, Bulk synchronous parallel

Aftab Ahmed Chandio, Nikos Tziritas, Fan Zhang, Ling Yin, Cheng-Zhong Xu. Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(12): 1305-1319.

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Smart cities have given a significant impetus to manage traffic and use transport networks in an intelligent way. For the above reason, intelligent transportation systems (ITSs) and location-based services (LBSs) have become an interesting research area over the last years. Due to the rapid increase of data volume within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A large part of data within the transportation domain is produced in the form of Global Positioning System (GPS) data. Such a kind of data is usually infrequent and noisy and achieving the quality of real-time transport applications based on GPS is a difficult task. The map-matching process, which is responsible for the accurate alignment of observed GPS positions onto a road network, plays a pivotal role in many ITS applications. Regarding accuracy, the performance of a map-matching strategy is based on the shortest path between two consecutive observed GPS positions. On the other extreme, processing shortest path queries (SPQs) incurs high computational cost. Current map-matching techniques are approached with a fixed number of parameters, i.e., the number of candidate points (NCP) and error circle radius (ECR), which may lead to uncertainty when identifying road segments and either low-accurate results or a large number of SPQs. Moreover, due to the sampling error, GPS data with a high-sampling period (i.e., less than 10 s) typically contains extraneous datum, which also incurs an extra number of SPQs. Due to the high computation cost incurred by SPQs, current map-matching strategies are not suitable for real-time processing. In this paper, we propose real-time map-matching (called RT-MM), which is a fully adaptive map-matching strategy based on cloud to address the key challenge of SPQs in a map-matching process for real-time GPS trajectories. The evaluation of our approach against state-of-the-art approaches is performed through simulations based on both synthetic and real-world datasets.

The paper discussed a work of map matching process with contribution of the cloud computing and other strategies to improve the accuracy and efficiency. Indeed this is an important technical issue in practical application of map matching, especially the real time spatial data processing. This is a useful alternative to map matching problem. The paper is well written with useful illustration.


概要:智慧城市为智能交通管理和交通网络智能应用的发展提供了巨大推动力。近来,智能交通系统(Intelligent transportation systems, ITSs)和移动位置服务(Location-based services, LBSs)也成为了研究领域的热点。交通领域数据量在快速不断增长,云计算在巨量数据的存储、接入、管理和处理方面有着巨大作用。交通领域相当比例的数据为GPS数据,此类数据具有非频繁、含噪声等特性,这使得维护基于GPS的实时交通软件的服务质量较为困难。在诸多智能交通系统应用中,地图匹配处理起着将GPS观测点准确排列于路网中的关键作用。考虑到准确性时,地图匹配策略的性能由两个连续的GPS观测点间的最短路径决定;另一方面,处理最短路径查询(Processing shortest path queries,SPQs)耗费着较高计算量。现有的地图匹配技术采用固定参数(固定的候选点数量,固定的误差圆半径)的办法,这可能导致确认线路分段时产生不确定性,也可导致低精度结果(或需进行大量SPQ处理以保证精度)。此外,由于采样错误的存在,较高采样时间(大于10 s)内的GPS数据常含有冗余数据,这也导致需要额外的SPQ处理。由于SPQ处理导致的高运算量问题,现有的地图匹配策略并不能实现实时应用。在本文中,我们提出一种实时地图匹配方法(Real-time map-matching, RT-MM)。该方法以云计算为基础,是一种全自适应地图匹配策略,能够应对实时GPS轨迹地图匹配中SPQ处理的关键问题。本研究还通过基于虚拟数据和实际数据的仿真,对所述方法与现有方法的性能进行了比较。

关键词:地图匹配;GPS轨迹;可调节;云计算;块同步并行计算(Bulk synchronous parallel, BSP)

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Brakatsoulas, S., Pfoser, D., Salas, R., et al., 2005. On map-matching vehicle tracking data. Proc. 31st Int. Conf. on Very Large Data Bases, p.853-864.

[2]Chandio, A.A., Zhang, F., Memon, T.D., 2014. Study on LBS for characterization and analysis of big data benchmarks. Mehran Univ. Res. J. Eng. Technol., 33(4):432-440.

[3]Chandio, A.A., Tziritas, N., Xu, C.Z., 2015a. Big-data processing techniques and their challenges in transport domain. ZTE Commun., 13(1):50-59.

[4]Chandio, A.A., Tziritas, N., Zhang, F., et al., 2015b. An approach for map-matching strategy of gps-trajectories based on the locality of road networks. 2nd Int. Conf. on Internet of Vehicles, p.234-246.

[5]Chen, B.Y., Yuan, H., Li, Q., et al., 2014. Map-matching algorithm for large-scale low-frequency floating car data. Int. J. Geograph. Inform. Sci., 28(1):22-38.

[6]Chen, C., Liu, Z., Lin, W.H., et al., 2013. Distributed modeling in a MapReduce framework for data-driven traffic flow forecasting. IEEE Trans. Intell. Transp. Syst., 14(1): 22-33.

[7]Dean, J., Ghemawat, S., 2008. MapReduce: simplified data processing on large clusters. Commun. ACM, 51(1): 107-113.

[8]Dijkstra, E.W., 1959. A note on two problems in connexion with graphs. Numer. Math., 1(1):269-271.

[9]Fang, S.K., Zimmermann, R., 2011. EnAcq: energy-efficient GPS trajectory data acquisition based on improved map matching. Proc. 19th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, p.221- 230.

[10]Goh, C.Y., Dauwels, J., Mitrovic, N., et al., 2012. Online map-matching based on hidden Markov model for real- time traffic sensing applications. 15th Int. IEEE Conf. on Intelligent Transportation Systems, p.776-781.

[11]Gonzalez, H., Han, J., Li, X., et al., 2007. Adaptive fastest path computation on a road network: a traffic mining approach. 33rd Int. Conf. on Very Large Data Bases, p.794-805.

[12]Greenfeld, J.S., 2002. Matching GPS observations to locations on a digital map. Proc. 81st Annual Meeting of the Transportation Research Board, p.1-13.

[13]He, Z.C., She, X.W., Zhuang, L.J., et al., 2013. On-line map-matching framework for floating car data with low sampling rate in urban road networks. IET Intell. Transp. Syst., 7(4):404-414.

[14]Hu, H., Lee, D.L., Lee, V., 2006. Distance indexing on road networks. Proc. 32nd Int. Conf. on Very Large Data Bases, p.894-905.

[15]Hummel, B., Tischler, K., 2005. Robust, GPS-only map matching: exploiting vehicle position history, driving restriction information and road network topology in a statistical framework. GIS Research UK Conf., p.68-77.

[16]Kajdanowicz, T., Kazienko, P., Indyk, W., 2014. Parallel processing of large graphs. Fut. Gener. Comput. Syst., 32: 324-337.

[17]Kolahdouzan, M., Shahabi, C., 2004. Voronoi-based K nearest neighbor search for spatial network databases. Proc. 30th Int. Conf. on Very Large Data Bases, p.840-851.

[18]Kühne, R., Schäfer, R., Mikat, J., et al., 2003. New approaches for traffic management in metropolitan areas. Proc. IFAC CTS Symp.

[19]Li, Q., Zhang, T., Yu, Y., 2011a. Using cloud computing to process intensive floating car data for urban traffic surveillance. Int. J. Geograph. Inform. Sci., 25(8):1303- 1322.

[20]Li, X., Han, J., Lee, J.G., et al., 2007. Traffic density-based discovery of hot routes in road networks. Int. Symp. on Spatial and Temporal Databases, p.441-459.

[21]Li, Z.J., Chen, C., Wang, K., 2011b. Cloud computing for agent-based urban transportation systems. IEEE Intell. Syst., 26(1):73-79.

[22]Liu, K., Li, Y., He, F., et al., 2012. Effective map-matching on the most simplified road network. Proc. 20th Int. Conf. on Advances in Geographic Information Systems, p.609-612.

[23]Lou, Y., Zhang, C., Zheng, Y., et al., 2009. Map-matching for low-sampling-rate GPS trajectories. Proc. 17th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, p.352-361.

[24]Malewicz, G., Austern, M.H., Bik, A.J., et al., 2010. Pregel: a system for large-scale graph processing. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.135-146.

[25]Newson, P., Krumm, J., 2009. Hidden Markov map matching through noise and sparseness. Proc. 17th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, p.336-343.

[26]Pink, O., Hummel, B., 2008. A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. Proc. 11th Int. IEEE Conf. on Intelligent Transprotation Systems, p.862-867.

[27]Quddus, M.A., Ochieng, W.Y., Noland, R.B., 2007. Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transp. Res. Part C, 15(5):312-328.

[28]Seo, S., Yoon, E.J., Kim, J., et al., 2010. HAMA: an efficient matrix computation with the MapReduce framework. IEEE 2nd Int. Conf. on Cloud Computing Technology and Science, p.721-726.

[29]Tang, W., Ren, D., Lan, Z., et al., 2013. Toward balanced and sustainable job scheduling for production supercomputers. Parall. Comput., 39(12):753-768.

[30]Thomsen, J.R., Yiu, M.L., Jensen, C.S., 2012. Effective caching of shortest paths for location-based services. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.313-324.

[31]Tiwari, S., Kaushik, S., 2013. Scalable method for k optimal meeting points (k-omp) computation in the road network databases. Int. Workshop on Databases in Networked Information Systems, p.277-292.

[32]Wang, J.Z., Wang, Z.J., 2013. Architecture design of urban intelligent transportation using cloud computing. Adv. Mater. Res., 605-607:2549-2552.

[33]Wang, S., 2010. A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Ann. Assoc. Am. Geograph., 100(3):535-557.

[34]Wang, S., Liu, Y., 2009. TeraGrid GIScience gateway: bridging cyberinfrastructure and GIScience. Int. J. Geograph. Inform. Sci., 23(5):631-656.

[35]Wang, Z.Y., Du, Y., Wang, G., et al., 2008. A quick map- matching algorithm by using grid-based selecting. Int. Workshop on Education Technology and Training and Geoscience and Remote Sensing, p.306-311.

[36]Wenk, C., Salas, R., Pfoser, D., 2006. Addressing the need for map-matching speed: localizing global curve-matching algorithms. 18th Int. Conf. on Scientific and Statistical Database Management, p.379-388.

[37]Yin, G.G., Xu, C.Z., Wang, L.Y., 2003. Optimal remapping in dynamic bulk synchronous computations via a stochastic control approach. IEEE Trans. Parall. Distr. Syst., 14(1): 51-62.

[38]Yuan, J., Zheng, Y., Zhang, C., et al., 2010. An interactive- voting based map matching algorithm. 11th Int. Conf. on Mobile Data Management, p.43-52.

[39]Zheng, Y., Wang, L., Zhang, R., et al., 2008. GeoLife: managing and understanding your past life over maps. 9th Int. Conf. on Mobile Data Management, p.211-212.

[40]Zheng, Y., Capra, L., Wolfson, O., et al., 2014. Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., 5(3):38.

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