Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm
In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper s...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5814879 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552556674416640 |
|---|---|
| author | Shuoben Bi Ruizhuang Xu Aili Liu Luye Wang Lei Wan |
| author_facet | Shuoben Bi Ruizhuang Xu Aili Liu Luye Wang Lei Wan |
| author_sort | Shuoben Bi |
| collection | DOAJ |
| description | In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management. |
| format | Article |
| id | doaj-art-34d94ffde54745b0b4550e93b0962ea7 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-34d94ffde54745b0b4550e93b0962ea72025-02-03T05:58:23ZengWileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/5814879Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering AlgorithmShuoben Bi0Ruizhuang Xu1Aili Liu2Luye Wang3Lei Wan4School of Geographical SciencesSchool of Geographical SciencesSchool of Geographical SciencesSchool of Geographical SciencesSchool of Geographical SciencesIn view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management.http://dx.doi.org/10.1155/2021/5814879 |
| spellingShingle | Shuoben Bi Ruizhuang Xu Aili Liu Luye Wang Lei Wan Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm Journal of Advanced Transportation |
| title | Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm |
| title_full | Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm |
| title_fullStr | Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm |
| title_full_unstemmed | Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm |
| title_short | Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm |
| title_sort | mining taxi pick up hotspots based on grid information entropy clustering algorithm |
| url | http://dx.doi.org/10.1155/2021/5814879 |
| work_keys_str_mv | AT shuobenbi miningtaxipickuphotspotsbasedongridinformationentropyclusteringalgorithm AT ruizhuangxu miningtaxipickuphotspotsbasedongridinformationentropyclusteringalgorithm AT aililiu miningtaxipickuphotspotsbasedongridinformationentropyclusteringalgorithm AT luyewang miningtaxipickuphotspotsbasedongridinformationentropyclusteringalgorithm AT leiwan miningtaxipickuphotspotsbasedongridinformationentropyclusteringalgorithm |