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Article

AUTOMATIC MULTIREGIME FUNDAMENTAL DIAGRAM CALIBRATION USING LIKELIHOOD ESTIMATION

DOI: 10.7708/ijtte.2017.7(1).06


7 / 1 / 79 - 92 Pages

Author(s)

Saurav Barua - Department of Civil Engineering, University of Information Technology and Sciences, Dhaka, Bangladesh -

Nazmul Haque - Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh -

Anik Das - Department of Civil Engineering, University of Information Technology and Sciences, Dhaka, Bangladesh -

Md Hadiuzzaman - Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh -

Sanjana Hossain - Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh -


Abstract

LIMB (Likelihood Identification for Multi-regime models’ Breakpoint), a new tool, is developed to calibrate various Fundamental diagrams (FDs) under different road geometric and traffic operational conditions. This tool enables to estimate traffic state from real time data and is ready to implement into model based control strategy. Since efficient traffic management or control of transportation system remains big challenge due to the necessity of accurate traffic state estimation; model based control strategy incorporated with LIMB tool can ameliorate the complexity. This research found that the breakpoint of multi-regime FD models obtained from experience were not able to estimate traffic state precisely; therefore LIMB was used to calibrate those models. The investigation also endeavored to develop a guideline which was capable to calibrate suitable FD models for lane- wise traffic conditions. Our proposed technique is independent of speed limits and completely automatic without any threshold inputs. Furthermore, it is comparable with the well-recognized FD automatic calibration technique. The comparative study found a 5% to 8% variation in estimating FD parameters. Later, we investigated several novel single and multi- regime FD models utilizing field traffic data obtained from PeMS website. LIMB adopted likelihood estimation method to identify density at breakpoint in between free flow and congestion states for multi-regime models. It applies least square method to estimate critical density-free flow speed-capacity. The proposed interface is conducive and easily adaptable for transportation practitioners to select the best model based control strategy for smooth and efficient traffic operation.


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Keywords:
LIMB; FD structure; single regime model; multi-regime model; breakpoint;


Acknowledgements:

This research work is supported by the Committee for Advanced Studies and Research (CASR) Grant No. 69) of Bangladesh University of Engineering and Technology (BUET). The contents of this paper reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein.


References:

Aerde, V.M. 1995. Single regime speed-flow-density relationship for congested and uncongested highways. In Proceedings of the Transportation Research Board 74th Annual Meeting, 95080.

 

Daganzo, C.F. 1994. The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research Part B: Methodological 28(4): 269-287.

 

Dervisoglu, G.; Gomes, G.; Kwon, J.; Horowitz, R.; Varaiya, P. 2009. Automatic calibration of the fundamental diagram and empirical observations on capacity. In Proceedings of the Transportation Research Board 88th Annual Meeting, Vol 15.

 

Drake, J.S.; Schofer, J.L.; May, A.D. 1967. A statistical analysis of speed–density hypotheses, Highway Research Record 154:112-117.

 

Drew, D.R. 1968. Traffic Flow Theory and Control. McGraw-Hill Inc., New York. USA. 467 p.

 

Edie, L.C. 1961. Car-following and steady-state theory for non-congested traffic, Operations Research 9(1): 66-76.

 

Greenberg, H. 1959. An analysis of traffic flow. Operations Research 7(1): 79-85.

 

Greenshields, B.D.; Bibbins, J.R.; Channing, W.S.; Miller, H.H. 1934. A study of traffic capacity. In Proceedings of the Fourteenth Annual Meeting of the Highway Research Board 14(1): 448-477.

 

Hegyi, A.; Schutter, A. B.D.; Hellendoorn, H.; Boom, T.V.D. 2002. Optimal coordination of ramp metering and variable speed control-an MPC approach. In Proceedings of the American Control Conference 5: 3600-3605.

 

Jayakrishnan, R.; Wei K.T; Chen, A. 1995. A dynamic traffic assignment model with traffic flow relationships, Transportation Research Part C: Emerging Technologies 3(1): 51-72.

 

Knoop, V.L.; Daamen, W. 2016. Automatic fitting procedure for the fundamental diagram, Transportmetrica B: Transport Dynamics, 1-16.

 

Li, H. 2014. Automatically Generating Empirical Speed-flow Traffic Parameters from Archived Sensor Data. In Proceedings of the 9th International Conference on Traffic and Transportation Studies (ICTTS), 138: 54–66.

 

Lighthill, M.J.; Whitham, G.B. 1955. On kinematic waves. II. A theory of traffic flow on long crowded roads. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 229(1178): 317-345.

 

Lu, X.; Varaiya, P.; Horowitz, R. 2009. Fundamental diagram modelling and analysis based NGSIM data. In Proceedings of the IFAC 12th Symposium on Control in Transportation Systems, 308-315.

 

Lu, X.T.; Skabardonis, A. 2007. Freeway traffic shockwave analysis: exploring the NGSIM trajectory data. In Proceedings of the Transportation Research Board 86th Annual Meeting, 07-3016.

 

May, A.D. 1990. Traffic Flow Fundamentals. Pearson Education. New York. USA. 301 p.

 

Messmer, A.; Markos, P. 1990. METANET: A macroscopic simulation program for motorway networks, Traffic Engineering & Control 31(8): 466-470.

 

Pipes, L.A. 1967. Car-following models and the fundamental diagram of road traffic, Transportation Research 1(1): 21-29.

 

Pompigna, A.; Rupi, F. 2015. Differences between HCM Procedures and Fundamental Diagram Calibration for Operational LOS Assessment on Italian Freeways, Transportation Research Procedia 5(2015): 103-118.

 

Quandt, R.E. 1958. The estimation of the parameters of a linear regression system obeying two separate regimes, Journal of the American Statistical Association 53(284): 873-880.

 

Skabardonis, A.; Dowling, R. 1997. Improved speed flow relationships for planning applications, Transportation Research Record 1572(1997): 18-23.

 

Underwood, R.T. 1961. Speed, Volume, and Density Relationship: Quality and Theory of Traffic Flow, Yale Bureau Highway Traffic, New Haven, Connecticut. 47 p.

 

Zhang, H.M. 1999. A mathematical theory of traffic hysteresis, Transportation Research Part B: Methodological 33(1):1-23.

 

Zhong, R.; Chen, C.; Chow, A.H.F.; Pan, T.; Yuan, F.; He., Z. 2016. Automatic calibration of fundamental diagram for first-order macroscopic freeway traffic models, Journal of Advanced Transportation 50(3): 363-385.


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