Discovering the Graph-Based Flow Patterns of Car Tourists Using License Plate Data: A Case Study in Shenzhen, China
Identifying flow patterns from massive trajectories of car tourists is considered a promising way to improve the management of tourism traffic. Previous researches have mainly focused on tourist movements at the macro-scale, such as inbound, domestic, and urban tourism using flow maps. Compared with...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/4795830 |
| Summary: | Identifying flow patterns from massive trajectories of car tourists is considered a promising way to improve the management of tourism traffic. Previous researches have mainly focused on tourist movements at the macro-scale, such as inbound, domestic, and urban tourism using flow maps. Compared with modeling the flow patterns of tourists at the macro-scale, modeling tourist flow at the microscale is more complicated. This paper takes Dapeng Island located in Shenzhen as the study area and uses the car recognition devices to collect traffic flow. Firstly, car tourists are separated from the mixed traffic flow after analyzing the spatial-temporal characteristics of tourists and residents. Next, daily graphs of tourist movements between road segments and tourist attractions are constructed. Finally, a frequent subgraph mining algorithm is used to extract the flow patterns of car tourists. The experimental results show that (1) car tourists have obvious preferences in the selection of trip time and tourist attractions; (2) the intercity tourists tend to take multidestination trips rather than a single destination trip in the same type of attractions; (3) car tourists are inclined to park their cars in an easy-to-access place, even if the attractions visited are changed. The main contribution of this paper is to present a new method for discovering the flow patterns of car tourists hidden in massive amounts of license plate data. |
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| ISSN: | 0197-6729 2042-3195 |