All Issue

2020 Vol.38, Issue 2 Preview Page

Article

A Study on Travel Time Estimation in Diverging Road Sections of Uninterrupted Traffic Flow based on Timestamp Data Analysis

타임스탬프 데이터 분석 기반의 도시부 연속류 도로 교통류 분기지점 통행시간 추정 연구

Sunghoon KIM1
,
Hwapyeong YU2
,
Hwasoo YEO3*
김 성훈1
,
유 화평2
,
여 화수3*
1Research Assistant Professor, Department of Civil & Environmental Engineering, KAIST, Daejeon 34141, Korea
2Ph.D. Student, Department of Civil & Environmental Engineering, KAIST, Daejeon 34141, Korea
3Associate Professor, Department of Civil & Environmental Engineering, KAIST, Daejeon 34141, Korea
1한국과학기술원 건설및환경공학과 연구조교수
2한국과학기술원 건설및환경공학과 박사과정
3한국과학기술원 건설및환경공학과 부교수
* Corresponding Author
30 April 2020. pp. 148-165
PDF XML Citation
Abstract

Various methods using GPS or vehicle detectors have been developed in estimating the travel time of individual road sections, but several problems still exist in terms of estimation accuracy. In particular, at the upstream of diverging road sections such as highway junctions and urban road intersections, traffic flow starts to diverge according to the traveling direction of vehicles. In such a case, the diverged flow behaviors can differ from each other, and differences in travel time towards each direction can be observed even in the same road section. Accordingly, it is necessary to estimate the travel time of a diverging road section by the traveling directions. For this purpose, this study provides a method for estimating the travel time of different traveling direction at the upstream of diverging highway sections using the GPS-based time stamp data. Three sequential steps with a few statistical approaches are provided in this stage: divergence detection in data, classification of diverged data, outlier filtering. In ‘data divergence detection’, a new statistical analysis method that can detect the occurrence of data divergence is provided, and it is analyzed that the method is useful in finding the threshold of determining data divergence. In ‘classification of diverged data’, a statistical approach is presented to classify the data by travel directions, and it is found that our modified method shows superior performance compared to others. In ‘outlier filtering’, a simple moving average is used to remove the data showing abnormal behaviors, but it is analyzed that this approach requires further improvement. The performance of the proposed method is tested using a microscopic simulation program. Through the tests, it is shown that the proposed method has reasonable performance in estimating the travel in the same road section by the travel directions.

Keywords
data classification
diverge bottleneck
intelligent transportation system
travel time estimation
traveler information service

개별 도로구간의 통행시간을 추정함에 있어 GPS 및 차량검지기 등을 활용하는 다양한 방식이 개발 및 적용되어 왔으나, 이를 보다 정확하게 추정함에 있어 몇몇의 문제점들이 산재한다. 특히, 일반 도로의 교차로 및 고속도로의 나들목 또는 분기점의 상류부 도로구간에서는 차량들의 진행 방향별로 교통류가 분기되기 시작되어 각 방향별 교통류의 행태가 상이할 수 있으며, 동일 구간이라도 진행 방향별 통행시간에 큰 차이가 발생할 수 있다. 이에 따라, 해당 구간의 통행시간을 진행 방향별로 분리하여 추정할 필요가 있으며, 이를 위하여 본 연구에서는 GPS 기반의 타임스탬프(Time Stamp) 데이터를 활용하여 고속도로 교통류 분기지점에서의 진행 방향별 통행시간을 추정하는 방법을 제시한다. 제시하는 방법은 ‘데이터 분기 감지’, ‘분기된 데이터 분류’, ‘데이터 이상치 제거’의 순차적인 3개의 단계로 구성된다. ‘데이터 분기 감지’에서는 진행 방향별 통행시간의 차이 발생 여부를 감지할 수 있는 통계적 방법을 제시하며, 해당 방법은 데이터 포인트들의 분기 여부를 판단할 수 있는 임계값을 찾는 데에 유용한 것으로 분석 및 평가된다. ‘분기된 데이터 분류’에서는 통행시간 차이가 감지되었을 때 이를 각 방향별로 데이터를 분류할 수 있는 통계적 접근 방법을 제시하며, 해당 방법 또한 분석을 통하여 데이터 분류에 우수한 성능을 보유한 것으로 평가된다. ‘데이터 이상치 제거’에서는 차량의 주행 중 정차와 같은 비정상적인 행태를 보이는 데이터를 제거하기 위하여 단순이동평균 기반의 방법을 활용하며, 해당 방법은 개선의 필요성이 있는 것으로 평가된다. 시뮬레이션 데이터를 기반으로 제시된 방법에 대하여 전반적인 성능평가를 시행하였으며, 이를 통하여 본 연구의 방법은 동일 도로구간에서의 통행시간을 차량 진행 방향별로 별도 추정함에 있어 합리적인 성능을 보여준다.

키워드
데이터 분류
분기지점 병목현상
여행자 정보제공 서비스
지능형교통체계
통행시간추정
References
1
AIMSUN (2011), AIMSUN Version 7 Users Manual, TTS-Transport Simulation Systems, Barcelona, http://www. aimsun.com.
2
Ban X. G., Herring R., Hao P., Bayen A. M. (2009), Delay Pattern Estimation for Signalized Intersections Using Sampled Travel Times, Transp. Res. Record, 2130, National Academic Sciences, 109-119.
10.3141/2130-14
3
Ban X. J., Li Y. W., Skabardonis A., Margulici J. D. (2010), Performance Evaluation of Travel-Time Estimation Methods for Real-Time Traffic Applications, J. Intell. Transport. Syst., 14(2), Taylor & Francis, 54-67.
10.1080/15472451003719699
4
Bolbol A., Cheng T., Tsapakis I., Chow A. (2012), Sample Size Calculation for Studying Transportation Modes from GPS Data, Procedia Soc. Behav. Sci., 48, Pergamon-Elsevier Science Ltd., 3040-3050.
10.1016/j.sbspro.2012.06.1271
5
Cetin M., List G. F., Zhou Y. J. (2005), Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time, Transp. Res. Record, 1917, National Academic Sciences, 37-44.
10.1177/0361198105191700105
6
Cheng J., Li G., Chen X. H. (2019), Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests, J. Adv. Transp., 2019, Wiley-Hindawi, Article ID 8582761.
10.1155/2019/8582761
7
Gan Q. J., Gomes G., Bayen A. (2017), Estimation of Performance Metrics at Signalized Intersections Using Loop Detector Data and Probe Travel Times, IEEE Trans. Intell. Transp. Syst., 18(11), IEEE-Inst. Electrical Electronics Engineers Inc., 2939-2949.
10.1109/TITS.2017.2666143
8
Jang J. (2016), Outlier Filtering Algorithm for Travel Time Estimation Using Dedicated Short-range Communications Probes on Rural Highways, IET Intell. Transp. Syst., 10(6), Inst. Engineering Technology-IET, 453-460.
10.1049/iet-its.2015.0103
9
Jenelius E., Koutsopoulos H. N. (2013), Travel Time Estimation for Urban Road Networks Using Low Frequency Probe Vehicle data, Transp. Res. Pt. B-Methodol., 53, Pergamon-Elsevier Science Ltd., 64-81.
10.1016/j.trb.2013.03.008
10
Kim S., Tak S., Lee D., Yeo H. (2019), Distributed Model Predictive Approach for Large-Scale Road Network Perimeter Control, Transp. Res. Record, 2673(5), National Academic Sciences, 515-527.
10.1177/0361198119838521
11
Kumar S. V., Vanajakshi L. (2015), Short-term Traffic Flow Prediction Ysing Seasonal ARIMA Model with Limited Input Data, Eur. Transp. Res. Rev., 7(3), SpringerOpen, 1-9.
10.1007/s12544-015-0170-8
12
Lee J., Gu E., Kim H., Jang K. (2017), Long-term Prediction of Bus Travel Time Using Bus Information System Data, J. Korean Soc. Transp., 35(4), Korean Society of Transportation, 348-359.
10.7470/jkst.2017.35.4.348
13
Liu X. L., Lu F., Zhang H. C., Qiu P. Y. (2013), Intersection Delay Estimation from Floating Car Data Via Principal Curves: A Case Study on Beijing's Road Network, Front. Earth Sci., 7(2), Springer, 206-216.
10.1007/s11707-012-0350-y
14
Lu C. R., Dong J. (2018), Estimating Freeway Travel Time and Its Reliability Using Radar Sensor Data, Transportmetrica B-Transp. Dyn., 6(2), Taylor & Francis, 97-114.
10.1080/21680566.2017.1325785
15
Lu X. Y., Lee J., Chen D., Bared J., Dailey D., Shladover S. E. (2014), Freeway Micro-simulation Calibration: Case Study Using Aimsun and VISSIM with Detailed Field Data, Transportation Research Board 93rd Annual Meeting, Washington D.C., USA.
16
Moghaddam S. S., Hellinga B. (2013), Evaluating the Performance of Algorithms for the Detection of Travel Time Outliers, Transp. Res. Record, 2338, National Academic Sciences, 67-77.
10.3141/2338-08
17
Munoz J. C., Daganzo C. F. (2002), The Bottleneck Mechanism of a Freeway Diverge, Transp. Res. Pt. A-Policy Pract., 36(6), Pergamon-Elsevier Science Ltd., 483-505.
10.1016/S0965-8564(01)00017-9
18
Park H., Kim Y. (2017), Determination of the Optimal Aggregation Interval Size of Individual Vehicle Travel Times Collected by DSRC in Interrupted Traffic Flow Section of National Highway, J. Korean Soc. Transp., 35(1), Korean Society of Transportation, 63-78.
10.7470/jkst.2017.35.1.063
19
Park H., Kim Y. (2018), Model for Filtering the Outliers in DSRC Travel Time Data on Interrupted Traffic Flow Sections, KSCE J. Civ. Eng., 22(9), Korean Society of Civil Engineers, 3607-3619.
10.1007/s12205-017-1333-z
20
Rudjanakanoknad J. (2012), Capacity Change Mechanism of a Diverge Bottleneck, Transp. Res. Record, 2278, National Academic Sciences, 21-30.
10.3141/2278-03
21
Shaikh S. A., Kitagawa H. (2014), Continuous Outlier Detection on Uncertain Data Streams, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (IEEE ISSNIP 2014).
10.1109/ISSNIP.2014.6827672
22
Shatnawi I., Yi P., Khlierfat I. (2018), Automated Intersection Delay Estimation Using the Input-output Principle and Turning Movement Data, Int. J. Transp. Sci. Technol., 7, Pergamon-Elsevier Science Ltd., 137-150.
10.1016/j.ijtst.2018.04.001
23
Shi C. Y., Chen B. Y., Li Q. Q. (2017), Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data, ISPRS Int. Geo-Inf., 6, MDPI, 253.
10.3390/ijgi6080253
24
Tam M. L., Lam W. H. K. (2008), Using Automatic Vehicle Identification Data for Travel Time Estimation in Hong Kong, Transportmetrica, 4(3), Taylor & Francis, 179-194.
10.1080/18128600808685688
25
Tang J. J., Zou Y. J., Ash J., Zhang S., Liu F., Wang Y. H. (2016a), Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System, PLoS One, 11(2), Public Library Science, Article e0147263.
10.1371/journal.pone.014726326829639PMC4735499
26
Tang L. L., Kan Z. H., Zhang X., Yang X., Huang F. Z., Li Q. Q. (2016b), Travel Time Estimation at Intersections Based on Low-frequency Spatial-temporal GPS Trajectory Big Data, Cartogr. Geogr. Inf. Sci., 43(5), Taylor & Francis, 417-426.
10.1080/15230406.2015.1130649
27
Wang D. H., Fu F. J., Luo X. Q., Jin S., Ma D. F. (2016), Travel Time Estimation Method for Urban Road Based on Traffic Stream Directions, Transportmetrica A, 12(6), Taylor & Francis, 479-503.
10.1080/23249935.2016.1151964
28
Wang Y., Zheng Y., Xue Y. (2014), Travel Time Estimation of a Path using Sparse Trajectories, Proceeding of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 25-34.
10.1145/2623330.2623656
29
Yang L. X., Zhou X. S. (2017), Optimizing On-time Arrival Probability and Percentile Travel Time for Elementary Path Finding in Time-dependent Transportation Networks: Linear Mixed Integer Programming Reformulations, Transp. Res. Pt. B-Methodol., 96, Pergamon-Elsevier Science Ltd., 68-91.
10.1016/j.trb.2016.11.012
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 38
  • No :2
  • Pages :148-165
  • Received Date : 2020-01-30
  • Revised Date : 2020-03-06
  • Accepted Date : 2020-04-02
  • DOI :https://doi.org/10.7470/jkst.2020.38.2.148
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close