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Social Costs Estimation to Evaluate Urban Trip Activity - An application of student housing and social costs analysis for urban planning -
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  • Journal title : Journal of KIBIM
  • Volume 6, Issue 2,  2016, pp.19-28
  • Publisher : Korean Institute of Building Information Modeling
  • DOI : 10.13161/kibim.2016.6.2.019
 Title & Authors
Social Costs Estimation to Evaluate Urban Trip Activity - An application of student housing and social costs analysis for urban planning -
Shin, Dongyoun; Song, Yu-Mi; Kim, Sung-Ah;
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 Abstract
Social costs analysis seeks to reveal the environmental effects of transportation policy. It delivers a sense of the effects of the public`s daily travel and the costs that are or would be incurred from individual trips. Moreover, the accumulated total number of trips will uncover the effects of travel on society. This article shows the quantitative analysis of the economic outcomes of travel using social costs estimation methods. In order to support urban planning tasks, this research implemented analysis tool for social costs estimation by travel behavior. For a case study, a jave based application which can convert people`s trip data into social costs is developed. the application used for simulating student-housing effects by estimating social costs changes. The analysis included the attributes, building scale and locational changes of the student housing as well as transforms of the students` trips.
 Keywords
Urban Sensing;Sensor Network;Social Costs;Urban Planning;Smart City;Behavior Information Modeling;
 Language
Korean
 Cited by
 References
1.
An, Y., Mylopoulos, J., Borgida, A. (2006). Building semantic mappings from databases to ontologies. in the 21st National Conference on Artificial Intelligence, AAAI Press.

2.
Brunner, P. H., Rechberger, H. (2004). Practical handbook of material flow analysis, The International Journal of Life Cycle Assessment, 9(5), pp. 337-338. crossref(new window)

3.
Cohen, J., Cohen, P., West, S. G., Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.

4.
Eastman, C., Eastman, C. M., Teicholz, P., Sacks, R. (2011). BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. John Wiley & Sons.

5.
Gilchrist, A., Allouche, E. N. (2005). Quantification of social costs associated with construction projects: stateof-the-art review, Tunnelling and underground space technology, 20(1), pp. 89-104. crossref(new window)

6.
Isikdag, U., Zlatanova, S. (2009). Towards defining a framework for automatic generation of buildings in CityGML using building Information Models, 3D Geo-Information Sciences: Springer, pp. 79-96.

7.
Kim, S. A., Shin, D., Choe, Y., Seibert, T., Walz, S. P. (2012). Integrated energy monitoring and visualization system for Smart Green City development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform, Automation in Construction, 22, pp. 51-59. crossref(new window)

8.
Litman, T. (1994). Transportation cost analysis: techniques, estimates and implications.

9.
Maibach, M., Schreyer, C., Sutter, D., Van Essen, H., Boon, B., Smokers, R., Schroten, A., Doll, C., Pawlowska, B., Bak, M. (2008). Handbook on estimation of external costs in the transport sector, CE Delft Solutions for environment, economy and technology www.ce.nl.

10.
Nam, T., Pardo, T. A. (2011). Conceptualizing smart city with dimensions of technology, people, and institutions. Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times: ACM, 282-291.

11.
O'Connell, P. L. (2005). Korea's high-tech utopia, where everything is observed, New York Times, 5.

12.
Okutani, I., Stephanedes, Y. J. (1984). Dynamic prediction of traffic volume through Kalman filtering theory, Transportation Research Part B: Methodological, 18(1), pp. 1-11.

13.
Shin, D., Aliaga, D., Tuncer, B., Arisona, S. M., Kim, S., Zcnd, D., Schmitt, G. (2015). Urban sensing: Using Journal 28 of KIBIM Vol.6, No.2 (2016) smartphones for transportation mode classification, Computers, Environment and Urban Systems, 53, pp. 76-86. crossref(new window)

14.
D., Schmitt, G. (2015). Urban sensing: Using smartphones for transportation mode classification, Computers, Environment and Urban Systems, 53, pp. 76-86. crossref(new window)

15.
Scott, A. (2002). Global city-regions: trends, theory, policy. Oxford University Press.