Abstract
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.
Original language | English |
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Article number | 3859830 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | Journal of Advanced Transportation |
Volume | 2018 |
DOIs | |
Publication status | Published - 16 May 2018 |
Funding
The authors would like to show great appreciation for support from the National Natural Science Foundation of China (project: Multimode Travel Demand Identification Methods Based on Individual Travel Feature Atlas of Public Transport Commuters (no. 51578028 )) and “Beijing Nova” Program by the Beijing Municipal Science and Technology Commission: Study on the Feature Extraction Method and Demand Mechanism of Public Transport Travel with Multimodes (no. Z171100001117100 ).
Funders | Funder number |
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National Natural Science Foundation of China | 51578028 |
Beijing Municipal Science and Technology Commission |