Improved F-DBSCAN for Trip End Identification Using Mobile Phone Data in Combination with Base Station Density

Trip end identification based on mobile phone data has been widely investigated in recent years. However, the existing studies generally use fixed clustering radii (CR) in trip end clustering algorithms, but ignore the influence of base station (BS) densities on the positioning accuracy of mobile ph...

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Veröffentlicht in:Journal of advanced transportation Jg. 2022; S. 1 - 17
Hauptverfasser: Jiang, Haihang, Yang, Fei, Zhu, Xin, Yao, Zhenxing, Zhou, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Hindawi 30.04.2022
John Wiley & Sons, Inc
Wiley
Schlagworte:
Algorithms
Cell phones
Cellular telephone equipment industry
Cellular telephones
Clustering
Communication
Communications networks
Complexity
Density
Efficiency
Genetic algorithms
Identification
Influence
Methods
Parameter identification
Radio equipment
Specific gravity
Transportation planning
Travel
Travel time
China
ISSN:0197-6729, 2042-3195
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Zusammenfassung:Trip end identification based on mobile phone data has been widely investigated in recent years. However, the existing studies generally use fixed clustering radii (CR) in trip end clustering algorithms, but ignore the influence of base station (BS) densities on the positioning accuracy of mobile phone data. This paper proposes a new two-step method for identifying trip ends: (1) Genetic Algorithm (GA) is utilized to optimize the CRs of DBSCAN under different BS densities. (2) We propose an improved Fast-DBSCAN (F-DBSCAN) for two objectives. One is for improving identification accuracies; the parameter CRs for judging core points can be dynamically adjusted based on the BS density around each mobile phone trace. The other is for reducing time complexity; a fast clustering improvement for the algorithm is proposed. Mobile phone data was collected by real-name volunteers with support from the communication operator. We compare the identification accuracy and time complexity of the proposed method with the existing ones. Results show that the accuracy is raised to 85%, which is approximately 6% higher than the existing methods. Meanwhile, the median running time can be reduced by about 76% by the fast clustering improvement. Especially for noncommuting trip ends, the identification accuracy can be increased by 8%. The average identification errors of travel time and trip end coordinates are reduced by about 12 min and 321 m, respectively.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0197-6729
2042-3195
DOI:10.1155/2022/3099721