Mining spatio-temporal co-location fuzzy congestion patterns from traffic datasets

WANG Xiaoxu; WANG Lizhen; WANG Jialong

doi:10.16511/j.cnki.qhdxxb.2020.25.012

PDF(3388 KB)

Journal of Tsinghua University(Science and Technology) ›› 2020, Vol. 60 ›› Issue (8) : 683-692. DOI: 10.16511/j.cnki.qhdxxb.2020.25.012

SPECIALSECTION: DATABASE

Mining spatio-temporal co-location fuzzy congestion patterns from traffic datasets

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Traffic congestion occurs when the total traffic volume on the road network exceeds the road capacity which disrupts normal traffic flow. The congestion patterns in traffic datasets need to be mined to effectively address urban traffic congestion problems. However, existing research work has failed to reasonably and accurately define "traffic congestion" and ignores the spatio-temporal attributes of the traffic flow data and the fuzziness of the traffic congestion concept. This paper presents the concept of spatio-temporal co-location fuzzy congestion patterns by introducing fuzzy set theory into the definition of traffic congestion to measure the degree of traffic congestion. The algorithm also adds the time attribute to the traditional spatial co-location pattern mining. Two algorithms are then presented for mining spatio-temporal co-location fuzzy congestion patterns. The methods are evaluated using real traffic datasets with the results showing that the methods provide better mining results than the existing methods.

Key words

information processing / spatial data mining / spatio-temporal features / spatio-temporal co-location fuzzy congestion pattern / fuzzy participation index

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Xiaoxu, WANG Lizhen, WANG Jialong. Mining spatio-temporal co-location fuzzy congestion patterns from traffic datasets[J]. Journal of Tsinghua University(Science and Technology). 2020, 60(8): 683-692 https://doi.org/10.16511/j.cnki.qhdxxb.2020.25.012

References

[1] 刘静. 基于状态划分的交通流短时预测方法研究[D]. 北京:北京交通大学, 2007. LIU J. Study on the short-term prediction method of traffic flow based on state division[D]. Beijing:Beijing Jiaotong University, 2007. (in Chinese)
[2] 姜桂艳, 常安德, 牛世峰. 基于车牌识别数据的交通拥堵识别方法[J]. 哈尔滨工业大学学报, 2011, 43(4):131-135. JIANG G Y, CHANG A D, NIU S F. Traffic congestion identification method based on license plate recognition data[J]. Journal of Harbin Institute of Technology, 2011, 43(4):131-135. (in Chinese)
[3] DIKER A C, NASIBOV E. Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPS data[C]//2012 IV International Conference "Problems of Cybernetics and Informatics". Baku, Azerbaijan:IEEE, 2012:1-4.
[4] CHU V W, WONG R K, LIU W, et al. Causal structure discovery for spatio-temporal data[M]//BHOWMICK S S, DYRESON C E, JENSEN C S, et al. Database Systems for Advanced Applications. Cham:Springer, 2014:236-250.
[5] LAKHOUILI A, MEDROMI H, ESSOUFI E H. Urbain traffic congestion estimating using simplified CRONOS model:Algorithm and implementation[J]. International Journal of Computer Techniques, 2015, 2(5):103-110.
[6] LAKHOUILI A, ESSOUFI E H, MEDROMI H. A regulation model of urban traffic congestion:Algorithm and implementation[C]//2015 5th World Congress on Information and Communication Technologies. Marrakech, Morocco:IEEE, 2015:78-82.
[7] 王丽珍, 陈红梅. 空间模式挖掘理论与方法[M]. 北京:科学出版社, 2014. WANG L Z, CHEN H M. Spatial pattern mining[M]. Beijing:Science Press, 2014. (in Chinese)
[8] SHEKHAR S, HUANG Y. Discovering spatial co-location patterns:A summary of results[C]//Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases (SSTD). Berlin, Heidelberg:Springer, 2001:236-256.
[9] YOO J S, SHEKHAR S. A join-less approach for mining spatial co-location patterns[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10):1323-1337.
[10] YANG P Z, WANG L Z, WANG X X. A parallel spatial co-location pattern mining approach based on ordered clique growth[C]//Proceedings of the 23nd International Conference on Database Systems for Advanced Applications. New York, USA:Springer, 2018:734-742.
[11] WANG L Z, BAO X G, ZHOU L H, et al. Mining maximal sub-prevalent co-location patterns[J]. World Wide Web, 2019, 22(5):1971-1997.
[12] WANG L Z, BAO X G, ZHOU L H. Redundancy reduction for prevalent co-location patterns[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(1):142-155.
[13] YANG P Z, WANG L Z, WANG X X, et al. An effective approach on mining co-location patterns from spatial databases with rare features[C]//2019 20th International Conference on Mobile Data Management. Hong Kong, China:IEEE, 2019:53-62.
[14] YANG S S, WANG L Z, BAO X G, et al. A framework for mining spatial high utility co-location patterns[C]//2015 12th International Conference on Fuzzy Systems and Knowledge Discovery. Zhangjiajie, China:IEEE, 2015:631-637.
[15] OUYANG Z P, WANG L Z, WU P P. Spatial co-location pattern discovery from fuzzy objects[J]. International Journal on Artificial Intelligence Tools, 2017, 26(2):1750003.
[16] 吴萍萍, 王丽珍, 周永恒. 带模糊属性的空间Co-location模式挖掘研究[J]. 计算机科学与探索, 2013, 7(4):348-358. WU P P, WANG L Z, ZHOU Y H. Discovering co-location from spatial data sets with fuzzy attributes[J]. Journal of Frontiers of Computer Science & Technology, 2013, 7(4):348-358. (in Chinese)
[17] CELTIC M. Discovering partial spatio-temporal co-occurrence patterns[C]//Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. Fuzhou, China:IEEE, 2011:116-120.
[18] QIAN F, YIN L, HE Q M, et al. Mining spatio-temporal co-location patterns with weighted sliding window[C]//Intelligent Computing and Intelligent Systems. Shanghai, China:IEEE, 2009:181-185.
[19] NGUYEN H, LIU W, CHEN F. Discovering congestion propagation patterns in spatio-temporal traffic data[J]. IEEE Transactions on Big Data, 2017, 3(2):169-180.
[20] HE Y, WANG L Z, FANG Y, et al. Discovering congestion propagation patterns by co-location pattern mining[C]//Proceedings of the Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data. Macau, China:Springer, 2018.