A Context-Aware Map Matching Method Based on Supported Degree

Map matching technology is indispensable in the tide of building smart cities. The difficulty degree of matching depends on the matching context (road network density and GPS point quality). However, most existing map matching algorithms use same strategies under different matching contexts, which are hard to balance accuracy and efficiency. Therefore, we propose a new method of map matching, which includes two matching phases: projection matching (Strategy 1) for every GPS points and connectivity matching (Strategy 2) for portions without credible results from the first phase. Thereinto, supported degree is employed to judge the credibility of the projection matching result, which reflect the difficulty degree of matching in each region. In the connectivity matching phase, for matching complex portions, tree structure is creatively adopted, which can represent the connectivity between roads. Besides, we present novel tricks to increase the efficiency, such as considering road segment as the basic element of map matching and simplifying connectivity tree based on limiting-velocity. Finally, to evaluate the performance of this new method, we have compared it with conventional algorithms on the same dataset, which consists of 480,973 GPS points. The proportion of the error road segment length in total trajectory length is used as the criterion to estimate the matching accuracy of the algorithm. When the sampling period is 10s, the algorithm can improve the matching accuracy rate to over 95%. Meanwhile, the running efficiency of this algorithm is obviously better than other algorithms, in sampling period less than 100s.

[1]  Muhammad Tayyab Asif,et al.  Online map-matching based on Hidden Markov model for real-time traffic sensing applications , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[2]  Raymond H. Putra,et al.  Map matching with Hidden Markov Model on sampled road network , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[3]  Chengyang Zhang,et al.  Map-matching for low-sampling-rate GPS trajectories , 2009, GIS.

[4]  Xing Xie,et al.  An Interactive-Voting Based Map Matching Algorithm , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[5]  John Krumm,et al.  Hidden Markov map matching through noise and sparseness , 2009, GIS.

[6]  Feng Lu,et al.  A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data , 2017, IEEE Transactions on Intelligent Transportation Systems.

[7]  Jian Wang,et al.  Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data , 2016, Inf. Sci..

[8]  Simon Washington,et al.  Shortest path and vehicle trajectory aided map-matching for low frequency GPS data , 2015 .

[9]  Chao Chen,et al.  A three-stage online map-matching algorithm by fully using vehicle heading direction , 2018, J. Ambient Intell. Humaniz. Comput..

[10]  Yu-Ling Hsueh,et al.  Map matching for low-sampling-rate GPS trajectories by exploring real-time moving directions , 2018, Inf. Sci..

[11]  Xing Xie,et al.  Reducing Uncertainty of Low-Sampling-Rate Trajectories , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[12]  Zhuo Li,et al.  A novel algorithm of low sampling rate GPS trajectories on map-matching , 2017, EURASIP J. Wirel. Commun. Netw..

[13]  Felipe Maia Galvão França,et al.  Weightless neuro-symbolic GPS trajectory classification , 2018, Neurocomputing.

[14]  Hiroki Yanagisawa An Offline Map Matching via Integer Programming , 2010, 2010 20th International Conference on Pattern Recognition.

[15]  Chao Chen,et al.  TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data , 2018, IEEE Transactions on Intelligent Transportation Systems.

[16]  Lin Sun,et al.  Understanding Taxi Service Strategies From Taxi GPS Traces , 2015, IEEE Transactions on Intelligent Transportation Systems.

[17]  Michel Bierlaire,et al.  A Probabilistic Map Matching Method for Smartphone GPS data , 2013 .