01764172

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https://annualmeeting.myt...nlineProgram/Details/15713

With the rapid development of tourism around the world, it has become of vital importance to improve the services provided to tourists to ensure the convenience and satisfaction of their travel. The optimal travel route identification and recommendation is an important part of a tourist’s plan to make the tour healthier and improve the tourist’s satisfaction and well-being, as the tourist may not be familiar with the attractions of the city to be visited. In this paper, the authors propose a novel research framework to help tourists make an optimal route recommendation by analyzing the historical mobile signaling data. First, the authors collect the mobile signaling data generated by tourists, and then crawl the city attraction location data to obtain tourists’ travel sequence. Then, the authors propose to employ a frequent pattern mining method to mine the popular attractions and the frequent travel sequence among attractions, then identify a tourism area, which is comprised of a cluster of attractions that are more likely to be visited on a single tour. Next, to ensure the reasonability of the recommended travel route, the authors adopt an Ant Colony Optimization (ACO) algorithm to find the optimal travel route among the popular attractions. Finally, an empirical study is conducted to verify the feasibility and applicability of the proposed research framework and approaches using the data from Xiamen, Fujian Province. The results of this empirical study indicate that the proposed approaches have significant potential for identifying optimal travel routes from mobile signaling data.

This paper was sponsored by TRB committee AED50 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

TRBAM-21-01998

English

20p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; Maps; References (34) ; Tables

Crowdsourcing; Data collection; Leisure time; Optimization; Route choice; Smartphones; Tourism; Travel behavior

Fujian (China); Xiamen (China)

Data and Information Technology; Safety and Human Factors; Transportation (General)

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-01998

TRIS, TRB, ATRI

Dec 23 2020 11:21AM