Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data
Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies p...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/9401630 |
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| author | Xiaoxuan Chen Xia Wan Fan Ding Qing Li Charlie McCarthy Yang Cheng Bin Ran |
| author_facet | Xiaoxuan Chen Xia Wan Fan Ding Qing Li Charlie McCarthy Yang Cheng Bin Ran |
| author_sort | Xiaoxuan Chen |
| collection | DOAJ |
| description | Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular probe data preprocessing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (random forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single-feature model, which provides prediction precision around 50%. |
| format | Article |
| id | doaj-art-2512dc5458e64ad78f7ab8d5d3f85f81 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2512dc5458e64ad78f7ab8d5d3f85f812025-08-20T03:54:52ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/94016309401630Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe DataXiaoxuan Chen0Xia Wan1Fan Ding2Qing Li3Charlie McCarthy4Yang Cheng5Bin Ran6Ph.D., Data Scientist, Ford Motor Company, 22000 Michigan Ave, Dearborn, MI 48124, USAPh.D., Data Warehouse Engineer, GlobalFoundries, 400 Stone Break Rd Extension, Malta, NY 12020, USAPh.D., Research Associate, TOPS Laboratory, University of Wisconsin-Madison, 1415 Engineering Drive, Room 1217, Madison, WI 53706, USAPh.D. Machine Learning Researcher, BMW Technology Inc., 540 W Madison St Suite 2400, Chicago, IL 60661, USATraffic Engineering Consultant, TranSmart Technologies Inc., 411 S Wells St, Chicago, IL 60607, USAPh.D., Research Associate, TOPS Laboratory, University of Wisconsin-Madison, 1415 Engineering Drive, Room 1249A, Madison, WI, 53706, USAPh.D., Vilas Distinguished Achievement Professor, TOPS Laboratory, Department of Civil and Environmental Engineering, University of Wisconsin-Madison, USACellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular probe data preprocessing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (random forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single-feature model, which provides prediction precision around 50%.http://dx.doi.org/10.1155/2019/9401630 |
| spellingShingle | Xiaoxuan Chen Xia Wan Fan Ding Qing Li Charlie McCarthy Yang Cheng Bin Ran Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data Journal of Advanced Transportation |
| title | Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data |
| title_full | Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data |
| title_fullStr | Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data |
| title_full_unstemmed | Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data |
| title_short | Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data |
| title_sort | data driven prediction system of dynamic people flow in large urban network using cellular probe data |
| url | http://dx.doi.org/10.1155/2019/9401630 |
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