A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events
Accurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we emp...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/3463330 |
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| _version_ | 1850169817901826048 |
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| author | Bao Guo Kaipeng Wang Hu Yang Fan Zhang Pu Wang |
| author_facet | Bao Guo Kaipeng Wang Hu Yang Fan Zhang Pu Wang |
| author_sort | Bao Guo |
| collection | DOAJ |
| description | Accurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we employ the smart card data of 6.5 million subway passengers of the Shenzhen Metro to develop a Markov chain-based individual mobility prediction model (i.e., SCMM) applicable to both ordinary and anomalous passenger flow situations. The proposed SCMM model improves the Markov chain model by incorporating the station-level anomalous passenger flow index and the collective mobility patterns of similar passengers. Compared with the benchmark models, the SCMM model achieves the highest prediction accuracy in both ordinary conditions and large crowding events. Our results highlight the importance of combining an individual’s own historical mobility data with collective mobility data and suggest the appropriate weights of individual and collective information considered in individual mobility modeling. |
| format | Article |
| id | doaj-art-a5cc828095cf475e9178f1feac431bf1 |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a5cc828095cf475e9178f1feac431bf12025-08-20T02:20:38ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/3463330A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding EventsBao Guo0Kaipeng Wang1Hu Yang2Fan Zhang3Pu Wang4School of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringSchool of Traffic and Transportation EngineeringShenzhen Institutes of Advanced TechnologySchool of Traffic and Transportation EngineeringAccurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we employ the smart card data of 6.5 million subway passengers of the Shenzhen Metro to develop a Markov chain-based individual mobility prediction model (i.e., SCMM) applicable to both ordinary and anomalous passenger flow situations. The proposed SCMM model improves the Markov chain model by incorporating the station-level anomalous passenger flow index and the collective mobility patterns of similar passengers. Compared with the benchmark models, the SCMM model achieves the highest prediction accuracy in both ordinary conditions and large crowding events. Our results highlight the importance of combining an individual’s own historical mobility data with collective mobility data and suggest the appropriate weights of individual and collective information considered in individual mobility modeling.http://dx.doi.org/10.1155/2023/3463330 |
| spellingShingle | Bao Guo Kaipeng Wang Hu Yang Fan Zhang Pu Wang A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events Journal of Advanced Transportation |
| title | A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events |
| title_full | A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events |
| title_fullStr | A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events |
| title_full_unstemmed | A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events |
| title_short | A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events |
| title_sort | new individual mobility prediction model applicable to both ordinary conditions and large crowding events |
| url | http://dx.doi.org/10.1155/2023/3463330 |
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