Journal of Korean Society of Transportation (대한교통학회지)

Korean Society of Transportation (대한교통학회)
권호 표지
DOI QR코드

DOI QR Code

Exploring the Temporal Relationship Between Traffic Information Web/Mobile Application Access and Actual Traffic Volume on Expressways

웹/모바일-어플리케이션 접속 지표와 TCS 교통량의 상관관계 연구

  • RYU, Ingon (Department of Transportation Systems Engineering, Ajou University) ;
  • LEE, Jaeyoung (Center for Advanced Transportation Systems Simulation, University of Central Florida) ;
  • CHOI, Keechoo (Department of Transportation Systems Engineering, Ajou University) ;
  • KIM, Junghwa (TOD-based Sustainable City Transportation Research Center, Ajou University) ;
  • AHN, Soonwook (Traffic Information Center, Korea Expressway Corporation)
  • 류인곤 (아주대학교 교통시스템공학과) ;
  • 이재영 (중앙플로리다대학교 첨단교통시뮬레이션연구센터) ;
  • 최기주 (아주대학교 교통시스템공학과) ;
  • 김정화 (아주대학교 TOD기반 도시교통연구센터) ;
  • 안순욱 (한국도로공사 교통센터)
  • Received : 2015.05.21
  • Accepted : 2015.11.27
  • Published : 2016.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

In the recent years, the internet has become accessible without limitation of time and location to anyone with smartphones. It resulted in more convenient travel information access both on the pre-trip and en-route phase. The main objective of this study is to conduct a stationary test for traffic information web/mobile application access indexes from TCS (Toll Collection System); and analyzing the relationship between the web/mobile application access indexes and actual traffic volume on expressways, in order to analyze searching behavior of expressway related travel information. The key findings of this study are as follows: first, the results of ADF-test and PP-test confirm that the web/mobile application access indexes by time periods satisfy stationary conditions even without log or differential transformation. Second, the Pearson correlation test showed that there is a strong and positive correlation between the web/mobile application access indexes and expressway entry and exit traffic volume. In contrast, truck entry traffic volume from TCS has no significant correlation with the web/mobile application access indexes. Third, the time gap relationship between time-series variables (i.e., concurrent, leading and lagging) was analyzed by cross-correlation tests. The results indicated that the mobile application access leads web access, and the number of mobile application execution is concurrent with all web access indexes. Lastly, there was no web/mobile application access indexes leading expressway entry traffic volumes on expressways, and the highest correlation was observed between webpage view/visitor/new visitor/repeat visitor/application execution counts and expressway entry volume with a lag of one hour. It is expected that specific individual travel behavior can be predicted such as route conversion time and ratio if the data are subdivided by time periods and areas and utilizing traffic information users' location.

최근 스마트폰의 빠른 보급으로 누구나 언제 어디서든 자유로운 네트워크 접속이 가능해졌다. 이는 통행 전은 물론 통행 중 교통정보 검색이 매우 편리해졌음을 의미한다. 고속도로 교통정보 탐색 행태의 기반이 되는 상관성 분석을 위하여, 웹과 모바일-앱의 접속 지표에 대한 정상성 여부를 검증하고, TCS 교통량과의 상관관계를 실증적으로 분석하는 것이 본 연구의 목적이다. 그 결과 첫째, 시간대별 웹/모바일-앱의 접속 지표에 대한 ADF-검정, PP-검정 결과, 로그변환이나 차분변환 없이도 시계열의 정상성 조건을 만족하는 것으로 나타났다. 둘째, 고속도로 진출입 교통량과의 피어슨 상관계수를 검토한 결과, 웹/모바일-앱의 모든 접속 지표는 뚜렷한 양적 상관관계를 보였다. 단, 트럭의 TCS 진입 교통량은 상관관계가 거의 없는 것으로 나타났다. 셋째, 시계열 변수 사이에 존재하는 발생시간의 시차 관계(동행성, 선행성, 후행성)를 규명하기 위해 교차분석을 수행한 결과, 모바일 이용자는 모든 웹 접속 지표보다 선행하고 있었으며, 모바일 실행횟수는 모든 웹 접속 지표와 동행함을 발견하였다. 넷째, 고속도로의 진입 교통량에 선행하는 웹/모바일-앱 접속 지표는 존재하지 않았으며, 웹 페이지뷰/방문자/신규방문자/재방문자, 모바일 실행횟수는 오히려 고속도로 진입 총 교통량과 비교시 1시간의 후행 시차에서 상관관계가 가장 높게 나타났다. 향후 분석의 공간적 범위와 시간적 범위를 세분화하고 교통정보 이용자의 위치정보를 활용할 수 있다면, 경로 전환 시점/비율과 같은 개별 통행행태까지도 예측할 수 있게 될 것으로 판단된다.

Keywords

References

  1. Bin M. Y., Jeong Y. M. (2008), A Study for Web-based Traffic Information Search and Utilization, Gyeonggi Research Institute, Suwon, South Korea.
  2. Byeon H. S. (2014), The Evaluation of Web Site Service Quality for Transportation Information, J. Korean Soc. Transp., 32(1), Korean Society of Transportation, 39-49. https://doi.org/10.7470/jkst.2014.32.1.039
  3. Chei D. H., Lee C. H., Lee S. Y., Baek J. W. (2014), Correlation Analysis Between Railroad and Incheon Int'l Airport Passengers, Proceedings of Korean Society for Railway, 496-501.
  4. Choi J. M., Lee S. J., Cho C. S. (2011), Data Mining of Advanced Traffic Information, The Korea Transport Institute, Sejong, South Korea.
  5. Han Z. H., Kim J. Y. (2006), Analysis of Cointegration Between Housing Price and Employment Activity According to Johansen Test, Journal of the Korean Offical Statistics, 11(1), Statistics Korea, 1-22.
  6. Kim J. H., Kwon D. Y., Jeong S. R. (2014), Trend of Big Data Application and Technology, Proceedings of Korean Society for Internet Information, 15(1), Korean Society for Internet Information, 171-172.
  7. Kim S. H., Baek S. K., Han D. H. (2010), Relationship Between Expressway Traffic Volume and Economic Growth Rate, Transportation Technology and Policy, 7(4), Korean Society of Transportation, 75-79.
  8. Lee J. M., Park S. S. (2005), An Estimation for Highway Trip Demand Functions Based upon Time Series Analysis, J. Korean Soc. Transp., 23(7), Korean Society of Transportation, 7-15.
  9. Marine-Roig E. (2014), A Webometric Analysis of Travel Blogs and Review Hosting: The Case of Catalonia, Journal of Travel & Tourism Marketing, 31(3), 381-396. https://doi.org/10.1080/10548408.2013.877413
  10. Nelson C. R., Plosser C. R. (1982), Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications, Journal of Monetary Economics, 10, 139-162. https://doi.org/10.1016/0304-3932(82)90012-5
  11. Oh D. S., Oh Y. T., Jo S. G., Hong E. J. (2009), An Analysis on Service Usage of Traffic Information on the Expressway, Journal of Korea Institute of Intelligent Transport Systems, 8(5), 12-25.
  12. Park H. S., Kim B. H., Kim Y. C. (2012), An Analysis on Service Usage of Traffic Information on the National Highway, Proceedings of Korean Society of Civil Engineers, 43-47.
  13. Park J. S. (2007), An Analysis of Behavioral Relationship Between Transit and Mobile Phone Users, Seoul University, Doctoral Dissertation.
  14. Pereira F. C., Rodrigues F., Ben-Akiva M. (2014), Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios, Journal of Intelligent Transportation Systems, (ahead-of-print), 1-16.
  15. Ryu I. G., Lee J. Y., Park G. C., Choi K. C., Hwang J. M. (2015), Analysis of Highway Traffic Indices Using Internet Search Data, J. Korean Soc. Transp., 33(1), Korean Society of Transportation, 14-28. https://doi.org/10.7470/jkst.2015.33.1.14
  16. Shin K. W., Choi K. C. (2014), Analyzing the Relationship Between Precipitation and Transit Ridership Through a Seemingly Unrelated Regression Model, J. Korean Soc. Transp., 32(2), Korean Society of Transportation, 83-92. https://doi.org/10.7470/jkst.2014.32.2.083
  17. Tsirimpa A. (2015), Modeling the Impact of Traffic Information Acquisition From Mobile Devices During the Primary Tour of the Day, Journal of Intelligent Transportation Systems, (ahead-of-print), 1-9.
  18. Tsirimpa A., Polydoropoulou A. (2015), The Impact of Traffic Information Acquisition on the Traffic Conditions of the Athens Greater Area, Transportation Systems and Engineering: Concepts, Methodologies, Tools, and Applications, 174.
  19. AC Transit article: http://abc7news.com/traffic/ac-transit-partners-with-google-maps-to-give-riders-real-time-bus-data/840473
  20. Arlington Transportation: http://transportation.arlingtonva.us/key-performance-measures/managing-effectively/website-traffic/
  21. Korea Expressway Co. OASIS: http://data.ex.co.kr
  22. Washington State Department of Transportation : http://www.wsdot.wa.gov/funding/partners/projects/webadvertising.htm