Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction
This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel ro...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/3859830 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849685652805779456 |
|---|---|
| author | Quan Liang Jiancheng Weng Wei Zhou Selene Baez Santamaria Jianming Ma Jian Rong |
| author_facet | Quan Liang Jiancheng Weng Wei Zhou Selene Baez Santamaria Jianming Ma Jian Rong |
| author_sort | Quan Liang |
| collection | DOAJ |
| description | This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger’s behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction. |
| format | Article |
| id | doaj-art-be75b9e0635d494a8535ee2b5d954109 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-be75b9e0635d494a8535ee2b5d9541092025-08-20T03:23:02ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/38598303859830Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph ConstructionQuan Liang0Jiancheng Weng1Wei Zhou2Selene Baez Santamaria3Jianming Ma4Jian Rong5Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaMinistry of Transport of the People’s Republic of China, Beijing 100736, ChinaComputer Science Department, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, NetherlandsTexas Department of Transportation, Austin, TX 78717, USABeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaThis paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger’s behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.http://dx.doi.org/10.1155/2018/3859830 |
| spellingShingle | Quan Liang Jiancheng Weng Wei Zhou Selene Baez Santamaria Jianming Ma Jian Rong Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction Journal of Advanced Transportation |
| title | Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction |
| title_full | Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction |
| title_fullStr | Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction |
| title_full_unstemmed | Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction |
| title_short | Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction |
| title_sort | individual travel behavior modeling of public transport passenger based on graph construction |
| url | http://dx.doi.org/10.1155/2018/3859830 |
| work_keys_str_mv | AT quanliang individualtravelbehaviormodelingofpublictransportpassengerbasedongraphconstruction AT jianchengweng individualtravelbehaviormodelingofpublictransportpassengerbasedongraphconstruction AT weizhou individualtravelbehaviormodelingofpublictransportpassengerbasedongraphconstruction AT selenebaezsantamaria individualtravelbehaviormodelingofpublictransportpassengerbasedongraphconstruction AT jianmingma individualtravelbehaviormodelingofpublictransportpassengerbasedongraphconstruction AT jianrong individualtravelbehaviormodelingofpublictransportpassengerbasedongraphconstruction |