Spatiotemporal Traffic Database Construction with Google Real-time Traffic Information and Spatiotemporal Congestion Pattern Analysis: A Case Study of Montgomery County,  Maryland,  U.S.A.

Sanghoon Ji; Jeongchang Seong; Stanescu Ana; Chul Sue Hwang; Yubin Lee

doi:10.22776/kgs.2021.56.3.265

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2021 Vol.56, Issue 3 Preview Page Next Page

Research Article

Spatiotemporal Traffic Database Construction with Google Real-time Traffic Information and Spatiotemporal Congestion Pattern Analysis: A Case Study of Montgomery County, Maryland, U.S.A. 구글 실시간 교통 정보를 활용한 시공간 자료 구축 및 정체 패턴 분석 - 미국 메릴랜드주 몽고메리카운티 사례 -

30 June 2021. pp. 265-276

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Abstract

The floating car data (FCD) method uses real-time vehicle locations to estimate traffic conditions. The use of FCD products for research can be limited by their costs and accessibility mainly because such datasets are collected by private companies. In this study, we propose a research method that utilizes FCD-based real-time traffic data from Google Maps. We constructed a real-time traffic map-based database (RTMDB) by compiling time-series real-time traffic map images. Using the RTMDB, we analyzed spatiotemporal congestion patterns in Montgomery County, Maryland, U.S.A. To check the reliability of RTMDB, a comparison analysis was conducted with traffic sensor data collected in the field. Results show that RTMDB is closely correlated to the collected data from traffic speed monitoring sensors. We also analyzed the congestion patterns depending on road types and AM/PM rush-hours.

Keywords

Spatio-temporal data

Real-time traffic map

Congestion pattern analysis

Floating-car data (FCD)

Intelligent transportation systems(ITS)

실시간 차량 위치정보를 활용하는 Floating car data (FCD) 방식으로 수집된 교통 정보는 소유권이 주로 민간기업에 있기 때문에 연구를 위한 활용 시 비용 및 접근성에 제약이 있다. 이 연구에서는 그러한 제약이 없는 FCD기반의 실시간 교통 정보 지도(Real-time traffic map, RTM)를 활용하는 연구 방법을 제시한다. 구체적으로, RTM자료를 시계열적으로 수집한 후, 실시간 교통 지도 데이터 베이스(Real-time traffic map-based database, RTMDB)을 구축하였으며, 이를 활용하여 미국 메릴랜드 주 몽고메리 카운티 내 정체 패턴의 시공간적 특성을 분석하였다. 연구 지역 도로에 설치된 센서에서 수집되는 교통 정보와의 비교분석을 통해 RTMDB의 실효성을 검증하였고, 통행 속도 감지 센서의 관측 결과와 밀접한 상관관계를 확인하였다. 그리고 도로 유형별 정체 패턴 차이와 출퇴근 시간대별 정체 패턴 분포의 차이를 분석하였다.

키워드

시공간 정보

실시간 교통 지도

정체 패턴 분석

차량 흐름 자료(Floating Car Data)

지능형 교통 관리 체계

References

박재홍･오철･김태형･주신혜, 2010, “GPS 프로브 차량 속도자료를 이용한 고속도로 사고 위험구간 추출기법,” 한국ITS학회논문지, 9(3), 73-84.

서울시 교통정보 시스템, 속도 정보, https://topis.seoul.go.kr/refRoom/openRefRoom_1.do

최기주･장정아･정재영･심상우, 2004, “교통정보제공을 위한 GPS/GIS기반의 실시간 혼잡지표개발,” 대한지형공간정보, 12(4), 53-60.

Bian, C., Yuan, C., Kuang, W. and Wu, D., 2016, “Evaluation, classification, and influential factors analysis of traffic congestion in chinese cities using the online map data,” Mathematical Problems in Engineering, 2016, 1-10. 10.1155/2016/1693729

De Fabritiis, C., Ragona, R. and Valenti, G., 2008, “Traffic estimation and prediction based on real time floating car data,” Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, 197-203. 10.1109/ITSC.2008.4732534

García-Ramírez, Y., 2020, “Developing a traffic congestion model based on google traffic data: a case study in Ecuador,” Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems, 137-144. 10.5220/0009594501370144

Google, Arterial trafic available on Google Maps, 2009, https://googleblog.blogspot.com.tr/2009/08/bright-side-of-sitting-in-traffic.html

Google, Google Maps is turning 15! Celebrate with a new look and features, 2020, https://blog.google/products/maps/maps-15th-birthday

Google, Google Maps Platform Coverage Details, 2021, https://developers.google.com/maps/coverage

Google, View places, traffic, terrain, biking, and transit, 2019, https://support.google.com/maps/answer/3092439?hl=en&visit_id=1-636415200196506079-2363811973&rd=2

He, Z., Zheng, L., Chen, P. and Guan, W., 2017, “Mapping to cells: a simple method to extract traffic dynamics from probe vehicle data,” Computer-Aided Civil and Infrastructure Engineering, 32(3), 252-267. 10.1111/mice.12251

Herrera, J. C., Work, D. B., Herring, R., Ban, X., Jacobson, Q. and Bayen, A. M., 2010, “Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment,” Transportation Research Part C: Emerging Technologies, 18(4), 568-583. 10.1016/j.trc.2009.10.006

Jiang, Z., McCord, M. R., and Goel, P. K., 2006, “Improved AADT estimation by combining information in image- and ground-based traffic data,” Journal of Transportation Engineering, 132(7), 523-530. 10.1061/(ASCE)0733-947X(2006)132:7(523)

Leduc, G., 2008, Road Traffic Data: Collection Methods and Applications.

Liu, X. and Ban, Y., 2013, “Uncovering Spatio-temporal cluster patterns using massive floating car data,” ISPRS International Journal of Geo-Information, 2(2), 371-384. 10.3390/ijgi2020371

Mahapatra, S. and Pujara, K. R., 2018, 2018 Maryland State Highway Mobility Report, MDOTSHA, USA.

Mirzahossein, H., Gholampour, I., Sedghi, M. and Zhu, L., 2021, “How realistic is static traffic assignment? Analyzing automatic number-plate recognition data and image processing of real-time traffic maps for investigation,” Transportation Research Interdisciplinary Perspectives, 9(100320), 1-11. 10.1016/j.trip.2021.100320

Nanthawichit, C., Nakatsuji, T. and Suzuki, H., 2003, “Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway,” Transportation Research Record, 1855(1), 49-59. 10.3141/1855-06

Schäfer, R.-P., Thiessenhusen, K.-U. and Wagner, P., 2002, “A traffic Information system by means of Read-time Floating-Car Data,” Proceedings of ITS World Congress 2002, Chicago, IL.

Seong, J. C., Kassa, H. and Choi, D., 2011, “Modeling and mapping traffic-congested corridors for statewide decision support,” Advances in Cartography and GIScience, 2, 449-456. 10.1007/978-3-642-19214-2_30

Song, J., Zhao, C., Zhong, S., Nielsen, T. A. S. and Prishchepov, A. V., 2019, “Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques,” Computers, Environment and Urban Systems, 77, 101364. 10.1016/j.compenvurbsys.2019.101364

van den Haak, P., Bakri, T., van Katwijk, R., Emde, M., Agricola, N. and Snelder, M., 2018, “Validation of google floating car data for applications in traffic management,” TRB 97th Annual Meeting Compendium of Papers, Washington DC.

Young, S., 2007, “Real-time traffic operations data using vehicle probe technology,” 2007 Mid-Continent Transportation Research Symposium, Iowa.

Zhang, S., Li, S., Li, X., and Yao, Y., 2020, “Representation of traffic congestion data for urban road traffic networks based on pooling operations,” Algorithms, 13(4), 84. 10.3390/a13040084

Information

Publisher :The Korean Geographical Society
Publisher(Ko) :대한지리학회
Journal Title :Journal of the Korean Geographical Society
Journal Title(Ko) :대한지리학회지
Volume : 56
No :3
Pages :265-276
Received Date : 2021-06-03
Revised Date : 2021-06-24
Accepted Date : 2021-06-28
DOI :https://doi.org/10.22776/kgs.2021.56.3.265

Journal of the Korean Geographical Society ISSN:1225-6633(Print) 대한지리학회지

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