Browse > Article
http://dx.doi.org/10.12815/kits.2018.17.4.99

Study on the Development of Congestion Index for Expressway Service Areas Based on Floating Population Big Data  

Kim, Hae (Korea Expressway Corporation)
Lee, Hwan-Pil (Korea Expressway Corporation)
Kwon, Cheolwoo (Dept. of Transportation Eng., Ajou University)
Park, Sungho (Dept. of Transportation Eng., Ajou University)
Park, Sangmin (Dept. of Transportation Eng., Ajou University)
Yun, Ilsoo (Dept. of Transportation Eng., Ajou University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.4, 2018 , pp. 99-111 More about this Journal
Abstract
Service areas in expressways are very important facilities in terms of efficient expressway operation and the convenience of users. It needs a traffic management strategy to inform drivers in advance about congestion in service areas so as to distribute users of service areas. But due to the lack of sensors and data on numbers of people in the service areas, congestion in service areas had not been measured and managed appropriately. In this study, a congestion index for service areas was developed using telecommunication floating population big data. Two alternative indices (i.e., density of service areas and floating population V/c of service areas) were developed. Finally, the floating population V/c of service areas was selected as a congestion index for service areas for reasons of the ease of understanding and comparison.
Keywords
Congestion Index; Service Area; Floating Population; Big Data; V/c;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ministry of Land, Infrastructure and Transport(2017), Press Release About Expansion to 20 Locations of Guide to Service Areas Congestion in This Year.
2 Ministry of the Interior and Safety(2015), Performance-Oriented Design Methods and Standards for Fire Fighting Facilities.
3 Seoul(2013), Public-Private Convergence Big Data and Public Data.
4 Sun J., Wen H., Gao Y. and Hu Z.(2009), "Metropolitan Congestion Performance Measures Based on Mass Floating Car Data," 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, Hainan, China, pp.109-113.
5 Korea Expressway Corporation, www.ex.co.kr, 2016.
6 Choi J. K., Lee H. and Yoo B. S.(2014), "Development of an Algorithm for Estimation of Real-time Subway Platform Congestion using Public Transportation Card Data," Journal of The Korean Society for Railway, pp.1219-1228.
7 Hwang S.(2002), "Development of Urban Congestion Index for Economic congestion Estimation," The Korea Transport Institute.
8 Kim H.(2017a), Development of a Congestion Index for Expressway Service Areas Using Floating Population Big Data, Master Thesis, Ajou University.
9 Kim H. J.(2017b), "Big data about Floating Population and Telecom Opens the age of Transportation Revolution," Monthly KOTI Magazine on Transport, vol. 229, pp.11-15.
10 Kim K. T., Lee I. M., Kwak H. C. and Min J. H.(2015), "Application study of Telecommunication Record Data in Floating Population Estimation," Seoul Studies, vol. 16, no. 3, pp.177-187.
11 Kong X., Xu Z., Shen G., Wang J., Yang Q. and Zhang B.(2015), "Urban Traffic Congestion Estimation and Prediction based on Floating Car Trajectory Data," Future Generation Computer Systems, Vol. 61, pp.97-107.
12 Korea Expressway Corporation(2017), Current Status of Work at Rest Facilities.
13 Korea Institute of Civil Engineering and Building Technology(2014), A Study on Improvement of Classification System of Use of Building.
14 Ministry of Land, Infrastructure and Transport(2013), Highway Capacity Analysis.