Capacity and Delay Estimation at Signalized Intersections Under Unsaturated Flow Condition Based on Cycle Overflow Probability

Abstract

Capacity at signalized intersections is a basic parameter in urban transport networks. The capacity of a signalized intersection is a function of existing geometric, control, weather, and other conditions. Estimation of capacity at signalized intersections is one of the most important topics in traffic Engineering. If the capacity can directly be measured, the delay at signalized intersections and thus the traffic performance and quality of service can be calculated according to the functional relationship between delay and capacity. Unfortunately, under real world traffic conditions, the capacity cannot easily be measured directly for an existing intersection, especially under unsaturated flow conditions where the flow volume is lower than the capacity. This paper presents a model for estimating the capacity of an existing signalized intersection under unsaturated flow condition based on the cycle overflow probability which can be directly measured by loop detectors at stop lines. The cycle overflow probability is just the proportion of cycles with occupied detector at end of green time phases. Also the flow volume can directly be measured by loop detectors at stop lines. According to the queuing theory, the cycle overflow probability is a function of the degree of saturation, i.e. a function of flow volume and capacity. Thus, by measuring the cycle overflow probability and the flow volume, the capacity can be estimated according to the functional relationship. The proposed model is calibrated to data obtained from loop detectors at different signalized intersections under unsaturated conditions. For validation the model, the real capacities at the same intersections are measured for saturated cycles where the capacity can be considers as the measured volume. The proposed model delivers a useful tool for estimating capacity at signalized intersections under unsaturated conditions. Using the proposed model, the capacity and thus the traffic quality of service at existing signalized intersections can directly be estimated using data from loop detectors at stop lines. The model is theoretically reasonable and easily to use for practitioners. The results of the calibration and validation are very promising.

Keywords

Signalized intersection ; Capacity ; Cycle overflow probability

References

1. Miller, 1978 Miller, A.J. (1978): Signalised intersections - capacity guide. Australian Research Board Bulletin, No. 4, 1968, Reprinted as ARRB Research Report ARR No. 79.
2. Wu, 1996 Wu. N. (1996): Rückstaulängen an Lichtsignalanlagen unter verschiedenen Verkehrsbedingungen. Straßenverkehrstechnik, 5/1996. Kirschbaum Verlag GmbH, Bonn.
3. Wu, 1990 Wu, N. (1990): Wartezeit und Leistungsfähigkeit von Lichtsignalanlagen unter Berücksichtigung von. Instationarität und Teilgebundenheit des Verkehrs (Dissertation). Schriftenreihe des Lehrstuhls. für Verkehrswesen der Ruhr-Universität Bochum, Heft 8.
4. FGSV, 2015 FGSV (2015): Handbuch für die Bemessung von Straßenverkehrsanlagen (HBS 2001). Forschungsgesellschaft für Straßen- und Verkehrswesen (Hrsg.), Nr. 299, FGSV Verlag. GmbH, Köln.
5. TRB, 2010 TRB (2010): Highway Capacity Manual (HCM 2000). TRB, National Research Council, Washington,. D.C.
6. Webster, 1958 Webster, F.V. (1958): Traffic Signal Setting. Road Research Technical Paper, No. 39, HNSO, London.
7. Webster and Cobbe, 1966 Webster, F.V.; Cobbe, B.M. (1966): Traffic Signals. Road Research Technical Paper, No.56 Her. Majestys Stationery Office, London.
8. Akcelik, 1980 Akcelik, R. (1980): Time-dependent expressions for delay, stop rate and queue length at traffic. signals. Australian Road Research Board, No. 361-1.
9. Kimber and Hollis, 1979 Kimber, R.M.; Hollis, E.M. (1979): Traffic queues and delays at road junctions. TRRL Laboratory. Report, LR 909.
10. AIDA, 2000 AIDA (2000): Verkehrszustandsklassifizierung und Störfallerkennung, Systembeschreibung; SSPconsult Beratende Ingenieure GmbH, München.
11. Bernhard and Riedel, 1999 Bernhard, J., Riedel, T. (1999): Erkennung von Stau mit kurzen Schleifendetektoren, Tagungsband der.
12. Heureka, 1999 Heureka, 1999, Forschungsgesellschaft für Straßen- und Verkehrswesen, Köln.
13. Friedrich, 1999 Friedrich, B. (1999): Ein verkehrsadaptives Verfahren zur Steuerung von Lichtsignalanlagen,. Dissertation, Schriftenreihe des Fachgebiets Verkehrstechnik und Verkehrsplanung der. Technischen Universität München, München.
14. Friedrich, 2000 Friedrich, B. (2000): Steuerung von Lichtsignalanlagen: BALANCE – ein neuer Ansatz,. Straßenverkehrstechnik, Heft 7/2000, Kirschbaum Verlag, Bonn.
15. Mück, 2001 Mück, J. (2001): Schätzverfahren für den Verkehrszustand an Lichtsignalanlagen unter Verwendung. halteliniennaher Detektoren. Straßenverkehrstechnik, 11/2001, Kirschbaum Verlag GmbH,. Bonn.
16. Papageorgiou, 1991 Papageorgiou, M. (1991): Optimierung: statische, dynamische, stochastische Verfahren für die. Anwendung; München, Wien: Oldenbourg.
17. Wu, 2004 Wu, N. (2004): Evaluierung und Erweiterung von Verfahren zur Bewertung von verkehrsabhängigen,. zeitlückengesteuerten Lichtsignalanlagen, Arbeitsblätter des Lehrstuhls für Verkehrswesen Nr. 27, Ruhr-Universität Bochum.