Latest revision as of 12:13, 5 April 2017

Abstract

The objective of this paper is to analyze the estimation quality of a staged methodology, that allows the estimation of signal timing information like cycle length, green and red time intervals for time-dependent fixed-time controlled and actuated intersections based on low-frequency and sparse floating car data (FCD). The paper exemplifies, based on simulated dataset, the estimation approach, which assumes as a principle condition the daily repetition of similar signal plans, whereby identical daytime and workday periods are aggregated to reach a sufficient trajectory density. The established concept utilizes efficiently small amounts of FCD trajectories that cover typical sampling intervals between 15-45 seconds. The introduced approach considers three processing stages. Firstly, map matching, data decomposition and stop line estimation are required. Secondly, trajectories’ stop line crossing times are calculated, whereby crossing times of all trajectories are iteratively projected by the application of a modulo operation into the time scale of potential cycle lengths. A statistical data analysis is carried out to estimate cycle lengths and daytime slices with similar signal program patterns. Finally, the last stage of the used approach considers the precise estimation of red and green time intervals based on a histogram analysis. The paper conclusively analyzes the signal program estimation quality considering different degrees of saturation and trajectory sample sizes.

Keywords

floating car data ; signal timing estimation ; low frequency

References

1. Anderson and Darling, 1954 T.W. Anderson, D.A. Darling; A test of goodness of fit; Journal of the American statistical Association, 49 (268) (1954), pp. 765–769
2. Axer and Friedrich, 2016 Axer, S., Friedrich, B. (2016). Signal timing estimation based on low frequency floating car data. Paper presented at the 14th World Conference on Transport Research.
3. Axer et al., 2015 Axer, S., Pascucci, F., Friedrich, B. (2015, 30.06.2015). Estimation of traffic signal timing data and total delay for urban intersections based on low frequency floating car data. Paper presented at the Mobil.Tum.
4. Barthauer, 2014 M. Barthauer, B. Friedrich; Evaluation of a Signal State Prediction Algorithm for Car to Infrastructure Applications; Transportation Research Procedia, 3 (2014), pp. 982–991
5. Bester and Varndell, 2002 Bester, C., Varndell, P. (2002). The effect of a leading green phase on the start-up lost time of opposing vehicles. SATC 2002.
6. Branston and van Zuylen, 1978 D. Branston, H. van Zuylen; The estimation of saturation flow, effective green time and passenger car equivalents at traffic signals by multiple linear regression; Transportation Research, 12 (1) (1978), pp. 47–53
7. Braun et al., 2009 R. Braun, F. Busch, C. Kemper, R. Hildebrandt, F. Weichenmeier, C. Menig, R. Preßlein-Lehle; TRAVOLUTION–Netzweite Optimierung der Lichtsignalsteuerung und LSA-Fahrzeug-Kommunikation; Straßenverkehrstechnik, 53 (2009), pp. 365–374
8. Eckhoff et al., 2013 D. Eckhoff, B. Halmos, R. German; Potentials and limitations of green light optimal speed advisory systems; Paper presented at the Vehicular Networking Conference (VNC), 2013 (2013) IEEE
9. Fayazi et al., 2015 S.A. Fayazi, A. Vahidi, G. Mahler, A. Winckler; Traffic signal phase and timing estimation from low-frequency transit bus data; Intelligent Transportation Systems, IEEE Transactions on, 16 (1) (2015), pp. 19–28
10. Kerper et al., 2012 Kerper, M., Wewetzer, C., Sasse, A., Mauve, M. (2012). Learning traffic light phase schedules from velocity profiles in the cloud. Paper presented at the New Technologies, Mobility and Security (NTMS), 2012 5th International Conference on.
11. Liu et al., 2012 Liu, X., Zhu, Y., Li, M., Zhang, Q. (2012). Pova: Traffic light sensing with probe vehicles. Paper presented at the INFOCOM, 2012 Proceedings IEEE.
12. Lou et al., 2009 Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y. (2009). Map-matching for low-sampling-rate GPS trajectories. Paper presented at the Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.
13. Pohlmann and Friedrich, 2010 Pohlmann, T., Friedrich, B. (2010). Online control of signalized networks using the cell transmission model. Paper presented at the Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on.
14. Wand and Jones, 1994 Wand, M.P., Jones, M.C. (1994). Kernel Smoothing : Taylor & Francis.