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2016 Vol.34, Issue 2
Mobile Source Emissions Estimates for Intra-zonal Travel Using Space Syntax Analysis

공간 구문론을 이용한 존내 자동차 배출량 추정 모형

Kyu Jin LEE1*
,
Keechoo CHOI2
이 규진1*
,
최 기주2
1TOD-based Sustainable City Transportation Research Center, Ajou University
2Department of Transportation System Engineering, Ajou University
1아주대학교 TOD기반 지속가능 도시교통연구센터
2아주대학교 교통시스템공학과
*교신저자. *Corresponding Author. 
April 2016. pp. 107-122
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Abstract

This study aims to develop a framework to estimate mobile source emissions with the macroscopic travel demand model including enhanced estimates of intra-zonal travel emissions using Space Syntax analysis. It is acknowledged that "the land-use and transportation interaction model explains the influence of urban structure on accessibility and mobility pattern". Based upon this theory, the estimation model of intra-zonal travel emissions is presented with the models of total travel distance, total travel demand, and average travel speed of intra-zonal trips. Thess statistical models include several spatial indices derived from the Space Syntax analysis. It explains that urban spatial structure is a critical factor for intra-zonal travel emissions, which is lower in compact zone with smaller portion of land area, lower sprawl indicator, and more grid-type of road network. Also the suggested framework is applied in the evaluation of the effectiveness of bicycle lane project in Suwon, Korea. The estimated emissions including intra-zonal travel is as double as the results only with inter-zonal demands, which shows better performance of the suggested framework for more realistic outcomes. This framework is applicable to the estimation of mobile source emissions in nation-wide and the assessment of transportation-environment policies in regional level.

Keywords
interaction of urban land-use and transportation
intra-zonal emission
intra-zonal travel distance
intra-zonal trips
mobile emission
space syntax

이 연구는 교통수요모형에 기반한 자동차 배출량 산정 시 존내 배출량이 누락되는 문제를 보완하기 위해, 교통수요모형과 결합하여 활용될 수 있는 존내 배출량 산정 모형을 정립하는데 목적이 있다. 특히 “도시-교통의 상호작용에 기안하여, 도시공간구조는 접근도에 영향을 미치고, 이는 사람들의 통행행태에 영향을 미친다.”는 이론에 근거하여, 본 연구는 도시 도로의 구조적인 특성을 정량화할 수 있는 공간 구문론(Space Syntax) 이론 등을 활용하여 존내 통행거리, 존내 통행량, 존내 평균 통행속도, 그리고 이들을 조합한 존내 배출량 추정 모형을 구축하였다.연구 결과, 도시 공간 구조는 존내 배출 특성에도 영향을 미치는 것으로 나타났으며, 특히 압축도시이고, 대지 면적이 좁고, 난개발 지수가 낮고, 격자형 도시일수록, 존내 자동차 배출량은 적게 배출되는 것으로 나타났다. 지역의 공간적 특성을 반영하여 구축된 본 연구 모형은 기존 모형에 비해 약 44-46% 높은 설명력을 보였다. 이를 수원시 자전거 도로 개선사업에 적용하여 배출 저감량을 산정한 결과, 존간 배출 저감량만 고려한 기존 결과와 비교해 약 2배 높은 배출 저감량이 산정되었다. 본 연구결과는 국가 배출량 산정에의 활용 및 다양한 교통-환경 정책의 효과 평가에도 유용하게 활용될 수 있을 것으로 기대된다.

키워드
도시-교통 상호작용
존내 배출량
존내 통행거리
존내 통행량
자동차 배출량
공간 구문론
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.
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.
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.
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.
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.
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
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 34
  • No :2
  • Pages :107-122
  • Received Date : 2015-07-27
  • Revised Date : 2015-09-30
  • Accepted Date : 2016-02-25
  • DOI :https://doi.org/10.7470/jkst.2016.34.2.107
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
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