Understanding additional stopover behaviors of car tourists: An analysis of combined route search and mobile GPS travel trajectory data

Yiping Le, Saizo Aoyagi, Kazuki Takahashi

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Car tourists may make some additional stopovers for sightseeing, meal, shopping, or leisure activities which is not originally planned to avoid becoming involved in the traffic congestion occurring on the roads returning home. The objective of this paper is to examine whether this induced stopover behavior exists and to empirically analyze the tendency of additional stopovers by departure time periods for returning home and level of traffic congestions. An analysis of route search data and GPS travel trajectory data collected by mobile car navigation application is conducted. The study results show car tourists tend to have additional stopovers when the departure time for returning home is early, and differences in the tendency of additional stopovers by congestion levels are observed, implying the impact of traffic congestion on additional stopover behaviors to some extent. The study results suggest the possibility of promoting additional stopovers in tourism areas for congestion avoidance by providing predicted near future traffic congestion and surrounding tourism information, which is beneficial to both tourism promotion and traffic congestion mitigation.

本文言語English
ホスト出版物のタイトル4th International Conference on Intelligent Transportation Engineering, ICITE 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ104-108
ページ数5
ISBN(電子版)9781728145532
DOI
出版ステータスPublished - 2019 9
外部発表はい
イベント4th International Conference on Intelligent Transportation Engineering, ICITE 2019 - Singapore, Singapore
継続期間: 2019 9 52019 9 7

出版物シリーズ

名前4th International Conference on Intelligent Transportation Engineering, ICITE 2019

Conference

Conference4th International Conference on Intelligent Transportation Engineering, ICITE 2019
CountrySingapore
CitySingapore
Period19/9/519/9/7

ASJC Scopus subject areas

  • Transportation
  • Artificial Intelligence
  • Management Science and Operations Research
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality

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