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Degree of Saturation Estimation Using the Average Travel Time at a Signalized Intersection

  • Transportation Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

The rapid development of traffic information technology has enabled the collection of segment and location-based data, in addition to spot data. Therefore, it is now possible to collect data that can represent the condition of a road section, such as travel time. Although the use of travel time is noted in previous studies, they do not consider interruptions to traffic flow, such as those at signalized intersections. This paper proposes algorithms to estimate the degree of saturation (DoS), defined as the ratio of demand flow to capacity, using the average travel time at signalized intersections. The average control delay is first calculated using the average travel time at signalized intersections in a roadway section. The DoS is then determined using an analytic delay model that describes the relationship between average control delay and DoS. At this time, if an initial queue occurs, the delay caused by the initial queue is adjusted. Furthermore, simulation and field test analyses confirm the accuracy of the algorithms, and that the average travel time can estimate DoS. The findings can be used for an improved adaptive signal control system operation and help establish a signal optimization policy for future research.

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References

  • Ahmed K, Abu-Lebdeh G Al-Omari B (2013) Estimation of delay induced by downstream operations. Journal of Transportation Engineering 139(1):8–19, DOI: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000465

    Article  Google Scholar 

  • Akçelik R (1980) Time-dependent expressions for delay, stop rate and queue length at traffic signals. Internal Report AIR 367-1, Australian Road Research Board, Vermont South, Australia

    Google Scholar 

  • Akçelik R, Rouphail NM (1993) Estimation of delays at traffic signals for variable demand conditions. Transportation Research Part B: Methodological 27(2):109–131, DOI: https://doi.org/10.1016/0191-2615(93)90003-S

    Article  Google Scholar 

  • Akgungor AP, Bullen AGR (1999) Analytical delay models for signalized intersections. Proceedings of the 69th ITE annual meeting, August 1–4, Nevada, USA

  • Allsop RE (1972) Delay at a fixed time traffic signal — I: Theoretical analysis. Transportation Science 6(3):260–285, DOI: https://doi.org/10.1287/trsc.6.3.260

    Article  Google Scholar 

  • Bagdatli MEC (2020) Vehicle delay modeling at signalized intersections with gene-expression programming. Journal of Transportation Engineering, Part A: Systems 146(9):04020107, DOI: https://doi.org/10.1061/JTEPBS.0000423

    Google Scholar 

  • Bagdatli MEC, Dokuz AS (2021) Vehicle delay estimation at signalized intersections using machine learning algorithms. Transportation Research Record 2675(9):110–126, DOI: https://doi.org/10.1177/03611981211036874

    Article  Google Scholar 

  • Beckmann M, McGuire CB, Winsten CB (1957) Studies in the economics of transportation. The Economic Journal 67(265): 116–118, DOI: https://doi.org/10.2307/2227641

    Article  Google Scholar 

  • Catling I (1977) A time-dependent approach to junction delays. Traffic Engineering & Control 18:520–526

    Google Scholar 

  • Cheng C, Du Y, Sun L, Ji Y (2016) Review on theoretical delay estimation model for signalized intersections. Transport Reviews 36(4):479–499, DOI: https://doi.org/10.1080/01441647.2015.1091048

    Article  Google Scholar 

  • Dakic I, Stevanovic A (2018) On development of arterial fundamental diagrams based on surrogate density measures from adaptive traffic control systems utilizing stop-line detection. Transportation Research Part C: Emerging Technologies 94:133–150, DOI: https://doi.org/10.1016/j.trpro.2017.05.052

    Article  Google Scholar 

  • Fehon KJ, Moore SE (1982) Dynamic control of a medium sized traffic signal network. Proceedings of the Eleventh Australian Road Research Board Conference, August 23–27, Melbourne, Australia

  • Gartner NH, Messer CJ, Rathi A (1997) Special report 165: Revised monograph on traffic flow theory. Transportation Research Board, Washington, DC, USA

    Google Scholar 

  • Gettman D, Abbas M, Liu H, Skabardonis, A (2014) Operation of traffic signal systems in oversaturated conditions, Volume 2 (No. NCHRP Project 03–90), Transportation Research Board, Washington, DC, USA, DOI: https://doi.org/10.17226/22289

    Book  Google Scholar 

  • Hasiloglu AS, Gokdag M, Karsli N (2014) Comparison an artificial intelligence-based model and other models: Signalized intersection delay estimates. International Journal of Engineering and Innovative Technology 4(3):220–231

    Google Scholar 

  • Hunt PB, Robertson DI, Bretherton RD, Winton RI (1981) SCOOT-a traffic responsive method of coordinating signals (No. LR 1014 Monograph). Transport and Road Research Laboratory, Workingham, Berkshire, UK

    Google Scholar 

  • Hurdle VF (1984) Signalized intersection delay models-a primer for the uninitiated. Transportation Research Record 971(112):96–105

    Google Scholar 

  • Kandarpa R, Sangillo J, Burgess L, Toppen A (2010) Real-time traveler information market assessment white paper. Report No. FHWA-JPO-10-055, United States Department of Transportation, Research and Innovative Technology Administration, Washington, DC, USA

    Google Scholar 

  • Kendall DG (1951) Some problems in the theory of queues. Journal of the Royal Statistical Society: Series B (Methodological) 13(2):151–173, DOI: https://doi.org/10.1111/j.2517-6161.1951.tb00080.x

    MathSciNet  MATH  Google Scholar 

  • Kimber RM, Hollis EM (1979) Traffic queues and delays at road junctions. Report No. LR909 Monograph, Transport and Road Research Laboratory, Workingham, Berkshire, UK

    Google Scholar 

  • Koonce P, Rodegerdts L (2008) Traffic signal timing manual. Report No. FHWA-HOP-08-024, United States Department of Transportation, Federal Highway Administration, Washington, DC, USA

    Google Scholar 

  • Lee SS, Lee SH, Oh YT, Choi KC (2002) Development of degree of saturation estimation models for adaptive signal systems. KSCE Journal of Civil Engineering 6(3):337–345, DOI: https://doi.org/10.1007/BF02829156

    Article  Google Scholar 

  • Ma D, Luo X, Jin S, Wang D, Guo W, Wang F (2017) Lane-based saturation degree estimation for signalized intersections using travel time data. IEEE Intelligent Transportation Systems Magazine 9(3):136–148, DOI: https://doi.org/10.1109/MITS.2017.2709881

    Article  Google Scholar 

  • Miller AJ (1963) Settings for fixed-cycle traffic signals. Journal of the Operational Research Society 14(4):373–386, DOI: https://doi.org/10.1057/jors.1963.61

    Article  Google Scholar 

  • Mori U, Mendiburu A, Álvarez M, Lozano JA (2015) A review of travel time estimation and forecasting for advanced traveller information systems. Transportmetrica A: Transport Science 11(2):119–157, DOI: https://doi.org/10.1080/23249935.2014.932469

    Article  Google Scholar 

  • Murat YS (2006) Comparison of fuzzy logic and artificial neural networks approaches in vehicle delay modeling. Transportation Research Part C: Emerging Technologies 14(5):316–334, DOI: https://doi.org/10.1016/j.trc.2006.08.003

    Article  Google Scholar 

  • Newell GF (1960) Queues for a fixed-cycle traffic light. The Annals of Mathematical Statistics 31(3):589–597, DOI: https://doi.org/10.1214/aoms/1177705787

    Article  MathSciNet  MATH  Google Scholar 

  • Ohno K (1978) Computational algorithm for a fixed cycle traffic signal and new approximate expressions for average delay. Transportation Science 12(1):29–47, DOI: https://doi.org/10.1287/trsc.12.1.29

    Article  Google Scholar 

  • Pascale A, Lam HT, Nair R (2015) Characterization of network traffic processes under adaptive traffic control systems. Transportation Research Procedia 9:205–224, DOI: https://doi.org/10.1016/j.trpro.2015.07.012

    Article  Google Scholar 

  • Prassas ES, Roess PR (2020) The highway capacity manual: A conceptual and research history, Volume 2. Springer International Publishing, Cham, Switzerland

    Book  Google Scholar 

  • Roess RP, Prassas ES, McShane WR (2010) Traffic engineering, 4th edition, Pearson, New York, NY, USA

    Google Scholar 

  • Rouphail NM, Tarko A, Li J (2002) traffic flow at signalized intersections. Traffic flow theory-A state-of-the-art report: Revised monograph on traffic flow theory, chapter 9. Transportation Research Board, Washington, DC, USA

    Google Scholar 

  • Sabra ZA, Gettman D, Henry RD, Nallamothu V, Sabra W (2010) Balancing safety and capacity in an adaptive signal control system —phase 1. Report No. FHWA-HRT-10-038, United States Department of Transportation, Federal Highway Administration, Washington, DC, USA

    Google Scholar 

  • Scemama G (1991) Traffic control practices in urban areas. Recherche Transports Sécurité 6:17–30

    Google Scholar 

  • Shi C, Chen BY, Li Q (2017) Estimation of travel time distributions in urban road networks using low-frequency floating car data. ISPRS International Journal of Geo-Information 6(8):253, DOI: https://doi.org/10.3390/ijgi6080253

    Article  Google Scholar 

  • Stevanovic A (2009) Review of adaptive traffic control principles and deployments in larger cities. Proceedings of the international scientific conference on mobility and transport, May 12–13, Munich, Germany

  • Stevanovic A (2010) Adaptive traffic control systems: Domestic and foreign state of practice. NCHRP Synthesis 403, Project No. 20-5/Topic 40-03, Transportation Research Board, Washington, DC, USA, DOI: https://doi.org/10.17226/14364

    Book  Google Scholar 

  • van Hinsbergen CPIJ, van Lint JWC, Sanders FM (2007) Short term traffic prediction models. Proceedings of the 14th world congress on intelligent transport systems (ITS), October 9–13, Beijing, China

  • van Lint JWC (2008) Online learning solutions for freeway travel time prediction. IEEE Transactions on Intelligent Transportation Systems 9(1):38–47, DOI: https://doi.org/10.1109/TITS.2008.915649

    Article  Google Scholar 

  • Webster FV (1958) Traffic signal settings, Road research laboratory technical paper, No. 39, UK

  • Xu H, Liu H, Tian Z (2010) Control delay at signalized diamond interchanges considering internal queue spillback. Transportation Research Record 2173(1):123–132, DOI: https://doi.org/10.3141/2173-15

    Article  Google Scholar 

  • Yin K, Zhang Y, Wang BX (2011) Modeling delay during heavy traffic for signalized intersections with short left-turn bays. Transportation Research Record 2257(1):103–110, DOI: https://doi.org/10.3141/2257-12

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by Korea Institute of Police Technology (KIPoT) grant funded by the Korean government (KNPA) (No. 092021C29S01000, Development of Traffic Congestion Management System for Urban Network).

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Authors and Affiliations

  1. Traffic Science Institute, Korea Road Traffic Authority, Wonju, 26466, Korea

    Minhyoung Lee

  2. Dept. of Transportation Engineering, University of Seoul, Seoul, 02504, Korea

    Youngchan Kim

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  1. Minhyoung Lee
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Correspondence to Minhyoung Lee.

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Lee, M., Kim, Y. Degree of Saturation Estimation Using the Average Travel Time at a Signalized Intersection. KSCE J Civ Eng 27, 1298–1311 (2023). https://doi.org/10.1007/s12205-023-0312-9

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  • DOI: https://doi.org/10.1007/s12205-023-0312-9

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