Intra-urban human mobility and activity transition: evidence from social media check-in data.
Most existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed fro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4019535?pdf=render |
| _version_ | 1818468585608052736 |
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| author | Lun Wu Ye Zhi Zhengwei Sui Yu Liu |
| author_facet | Lun Wu Ye Zhi Zhengwei Sui Yu Liu |
| author_sort | Lun Wu |
| collection | DOAJ |
| description | Most existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed from about 15 million check-in records during a yearlong period in Shanghai, China. The proposed model is activity-based and includes two parts: the transition of travel demands during a specific time period and the movement between locations. For the first part, we find the transition probability between activities varies over time, and then we construct a temporal transition probability matrix to represent the transition probability of travel demands during a time interval. For the second part, we suggest that the travel demands can be divided into two classes, locationally mandatory activity (LMA) and locationally stochastic activity (LSA), according to whether the demand is associated with fixed location or not. By judging the combination of predecessor activity type and successor activity type we determine three trip patterns, each associated with a different decay parameter. To validate the model, we adopt the mechanism of an agent-based model and compare the simulated results with the observed pattern from the displacement distance distribution, the spatio-temporal distribution of activities, and the temporal distribution of travel demand transitions. The results show that the simulated patterns fit the observed data well, indicating that these findings open new directions for combining activity-based analysis with a movement-based approach using social media check-in data. |
| first_indexed | 2024-04-13T21:13:50Z |
| format | Article |
| id | doaj.art-39ce31adfca74b7c8173b20ce8678280 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T21:13:50Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-39ce31adfca74b7c8173b20ce86782802022-12-22T02:29:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0195e9701010.1371/journal.pone.0097010Intra-urban human mobility and activity transition: evidence from social media check-in data.Lun WuYe ZhiZhengwei SuiYu LiuMost existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed from about 15 million check-in records during a yearlong period in Shanghai, China. The proposed model is activity-based and includes two parts: the transition of travel demands during a specific time period and the movement between locations. For the first part, we find the transition probability between activities varies over time, and then we construct a temporal transition probability matrix to represent the transition probability of travel demands during a time interval. For the second part, we suggest that the travel demands can be divided into two classes, locationally mandatory activity (LMA) and locationally stochastic activity (LSA), according to whether the demand is associated with fixed location or not. By judging the combination of predecessor activity type and successor activity type we determine three trip patterns, each associated with a different decay parameter. To validate the model, we adopt the mechanism of an agent-based model and compare the simulated results with the observed pattern from the displacement distance distribution, the spatio-temporal distribution of activities, and the temporal distribution of travel demand transitions. The results show that the simulated patterns fit the observed data well, indicating that these findings open new directions for combining activity-based analysis with a movement-based approach using social media check-in data.http://europepmc.org/articles/PMC4019535?pdf=render |
| spellingShingle | Lun Wu Ye Zhi Zhengwei Sui Yu Liu Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS ONE |
| title | Intra-urban human mobility and activity transition: evidence from social media check-in data. |
| title_full | Intra-urban human mobility and activity transition: evidence from social media check-in data. |
| title_fullStr | Intra-urban human mobility and activity transition: evidence from social media check-in data. |
| title_full_unstemmed | Intra-urban human mobility and activity transition: evidence from social media check-in data. |
| title_short | Intra-urban human mobility and activity transition: evidence from social media check-in data. |
| title_sort | intra urban human mobility and activity transition evidence from social media check in data |
| url | http://europepmc.org/articles/PMC4019535?pdf=render |
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