Skip to main content

Advertisement

Springer Nature Link
Log in

Utilizing Spatial Big Data platform in evaluating correlations between rental housing car sharing and public transportation

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

Car sharing service for public rental housing in Korea, Happy Car, has increased the mobility for the residents and used as transportation welfare in the era of sharing economy. However, the car sharing service operator for rental housing have needed to improve the services more efficiently and access easily to public transportation but have difficulties in geocoding, analyzing and geovisualizing the large volume of disaggregate data in the services. Therefore, it needs a correlation analysis between rental housing car sharing usage patterns and public transportation accessibility by processing this big volume of data. In this study, we used the sharing car’s GPS data in the 45 rental housing districts in Seoul Metropolitan Area and the transportation card transaction data and analyzed the correlation between car sharing service and the supply level of public transportation. To handle the large volume of spatial location data and the card data during the analysis, we used Spatial Big Data platform to process this spatially referenced Big Data in a parallel distributed way. From the spatial analysis, the level of public transportation supply doesn’t affect the use pattern of sharing car in the rental housing and low income rental housing district households showed the longest moving distance. In terms of analytical functions of the Spatial Big Data platform, spatial computation through the platform such as the buffering analysis used for the accessibility analysis of the public transportation was found to be efficient in repetitive spatial operations of the massive amount of data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The driving distance of car-sharing members following use of the service was found to decrease by 36.7% among users in Japan and 25% among users in Switzerland [8].

  2. https://en.wikipedia.org/wiki/Remix_(book).

  3. Basic Operation (Distance based analysis, Convelx haul, Clip, Intersect, Union, Spatial Join, Buffer, Dissolve), Surface Analysis (Aspect, Slope, Contour, Hillshading), Density Analysis (Point Density, Line Density, Kernel Density), Spatial statistics (Average Nearest Neighbor, High/Low clustering, Incremental Spatial Autocorrelation, Hot Spot Analysis, Spatial Autocorrelation), Network Analysis (Route, Origin–Destination (OD) cost matrix, vehicle routing problem, Find Closest Facilities, Location-Allocation, Service Area).

References

  1. Byun, W. H. (2013). Introduction of Car Sharing in LH rental housing. Daejon: Land and Housing Research Institute.

    Google Scholar 

  2. Kang, P. M., Choi, H. S., & Park, T. J. (2014). Implications of international car-sharing business. Transportation Technology and Policy, 11(4), 72–79.

    Google Scholar 

  3. Choi, S. Y. (2014). About haengbok car, the residential on-site carsharing service. Transportation technology and policy, 11(4), 87–91.

    Google Scholar 

  4. Ko, J. H., Lee, S. J., & Yang, J. Y. (2014). Carsharing program in Seoul: Concept and future directions. Transportation technology and policy, 11(4), 81–86.

    Google Scholar 

  5. Jeong, K. B., Cho, G. B., & Kim, S. W. (2015). The study of availability and factor analysis on car-sharing for sharing economy. Korean Comparative Government Review, 19(3), 105–124.

    Article  Google Scholar 

  6. Hwang, K. Y., & Jeon, H. Y. (2014). Applying sharing economy principle on transport with focus on car sharing practice and research. Journal of Transport Research, 21(1), 35–49.

    Google Scholar 

  7. Cho, K.B. (2015). Study on the constructing collaborative local governance for relationship between the sharing economy and the regional economy: Focusing on the regional car sharing. Ph.D. dissertation paper, Pukyung National University.

  8. Forceware. (2014). Introduction to Spatial Hadoop.

  9. Park, J. S., & Moon, J. H. (2012). Estimation of the demand for car-sharing service. Goyang: The Korea Transportation Institute.

    Google Scholar 

  10. Park, H.Y. (2015). Social cost benefit analysis for carsharing project in Seoul—From an environmental perspective. Master dissertation, Seoul National University.

  11. Kim, S. H. (2014). A study on the car-sharing operation strategies for public transit intermodality. Suwon: Suwon Research Institute.

    Google Scholar 

  12. Martin, E., & Shaheen, S. (2011). The impact of carsharing on public transit and non-motorized transit: An exploration of North American carsharing survey data. Energies, 4(11), 2094-2114. http://www.mdpi.com/1996-1073/4/11/2094/htm.

  13. Choi, H. S., & Park, J. T. (2014). Study on the local factors affecting availability of car-sharig in Seoul. Journal of the Korean Society for Railway, 17(5), 381–389.

    Article  Google Scholar 

  14. Huwer, U. (2004). Public transport and csar-sharing—Benefits and effects of combined services. Transport Policy, 11(1), 77–87.

    Article  Google Scholar 

  15. Shin, M. S., & Bae, S. H. (2012). Study on location decision for cloud transportation system rental station. Journal of Korean Society of Transportation, 30(2), 29–42.

    Article  Google Scholar 

  16. Kim, S. H., Jung, H. J., & Bea, S. H. (2014). Development of a vehicle relocation algorithm for the promotion of one-way car sharing service. Journal of Korean Society of Transportation, 32(3), 239–247.

    Article  Google Scholar 

  17. Kim, S. H., Lee, K. J., & Choi, K. C. (2014). Preferences factors analysis for car-sharing. Journal of Korean Society of Civil Engineers, 34(4), 1241–1249.

    Article  Google Scholar 

  18. Ahn, J. W., Yi, M. S., & Shin, D. B. (2013). Study for Spatial Big Data concept and system building. Journal of Korea Spatial Information Society, 21(5), 43–51.

    Article  Google Scholar 

  19. Kim, M. G., & Koh, J. H. (2016). Recent research trends for geospatial information explored by Twitter data. Spatial Information Research, 24, 65–73.

    Article  Google Scholar 

  20. Cho, N., & Kang, Y. S. (2016). Space-time density of field trip trajectory: exploring spatio-temporal patterns in movement data. Spatial Information Research, 24, 65–73.

    Article  Google Scholar 

Download references

Acknowledgements

This research was conducted with the help of Spatial Big Data project (2014) of Ministry of Land, Infrastructure and Transportation (MOLIT), Republic of Korea.

Author information

Authors and Affiliations

  1. Spatial Information Office, Korea Land and Housing Corporation, Jinju, Republic of Korea

    Junyoug Choi & Jeakak Yoon

Authors
  1. Junyoug Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeakak Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junyoug Choi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choi, J., Yoon, J. Utilizing Spatial Big Data platform in evaluating correlations between rental housing car sharing and public transportation. Spat. Inf. Res. 25, 555–564 (2017). https://doi.org/10.1007/s41324-017-0122-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-017-0122-6

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy