Analyzing Stop Time Phase Leading to Congestion Based on Drivers’ Behavior Patterns

Document Type : Research Paper

Authors

1 Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

2 PhD candidate, Imam Khomeini International University, Qazvin, Iran

Abstract

Traffic oscillation, stop and go traffic, is created by different reasons such as: sudden speed drop of leader vehicle. Stop and go traffic commonly is observed in congested freeways results in traffic oscillation. Many theories had been presented to define congestion traffic based on laws of physics such as: thermodynamics and fluid. But, these theories could not explain the complexity of driving responses in different situations of traffic especially in traffic jams. Unfortunately, because trajectories data are very scarce, our understanding of this type of oscillations in congested traffic is still limited. When the leader vehicle of a platoon drops speed, deceleration waves are released from downstream to upstream. Follower vehicles reacts different behavioral reactions based on personal characteristics. In this paper, behavioral patterns of follower driver were classified based on asymmetric microscopic driving behavior theory and traffic hysteresis in NGSIM trajectories. They were four patterns in deceleration phase and two patterns in acceleration phase. Then, two parameters of last deceleration wave leading to congestion, time and space parameters, τ and δ, were calculated based on Newell’s car following model. Time of two phases, stop and congestion phases, were identified based on follower vehicle trajectory. In order to calculate time of two phases, two points were identified: point of receiving stop wave leading to congestion and point of entering to congestion. Artificial neural network models were developed to analyze the relationship between the microscopic parameters and time of two phases. Analysis results present spacing difference of follower between stop and congestion phase based on under reaction-timid pattern and spacing difference of follower between deceleration and congestion phase based on over reaction-timid pattern and spacing of leader vehicle at the wave diffusion point are most effective parameters in stop time leading to congestion.  One of the main practical applications of this paper can be the addressing one of the main problems of micro simulation soft wares (like Aimsun) due to behavioral patterns. 

Keywords

References

-Ahn, S. and Cassidy, M. (2006) “ Freeway traffic oscillations and vehicle lane-changemanoeuvres. In: Heydecker, B., Bell, M., Allsop, R. (Eds.)”, Forthcoming in 17thInternational Symposium on Transportation and Traffic Theory. Elsevier, NewYork.
-Ahn, S., Vadlamani, S. and Laval, J. A. (2011) “A method to account for non-steady state conditions in measuring traffic hysteresis." Transportation Research Part C: Emerging Technologies Vol. 34, pp. 138-147”
-Abdi, A. and Salehikalam, A. (2016) “Analyzing deceleration time lead to congestion based on behavior patterns”, Modares Civil Engineering  Journal - Volume 16, Special Issue, Winter 1395, pp. 91-102.
-Arab Moghadam, M., Pahlavani, P., Naseralavi, S. (2016) “Prediction of car following behavior based on the instantaneous reaction time using an ANFIS-CART based model”, International Journal of Transportation Engineering , Article 4, Volume 4, Issue 2, pp.. 109-126.
-Bilbao-Ubillos, J. (2008) “The costs of urban congestion: estimation of welfare losses arising from congestion on cross-town link roads”, TransportationResearch Part A Vol. 42, No. 8,  pp.1098–11082.
-Chen, D., Laval, J. A.  Zheng, Z. and Ahn,  S. (2012a) “Traffic oscillations: a behavioral car-following model”, Transportation Research Part B, Vol. 46, No. 6, pp. 744-761.
-Chen, D., Laval, J. A., Ahn, S. and Zheng, Z. (2012b) “Microscopic traffic hysteresis in traffic oscillations: A behavioral pespective”, Transportation Research Part B, Vol.43 A. pp.126-141.
-Del Castillo, J. M. (2001) “Propagation of perturbations in dense traffic flow: a model and its implications”, Transportation Research Part B Vol. 35, pp. 367-389.
-Edie, L. C. and Baverez, E. (1967) “Generation and propagation of stop-start traffic waves”, Proceedings of Third International Symposium on the Theory of Traffic Flow. American Elsevier Publishing Co. New York. pp. 26-37.
-Forbes, T. W., Zagorski, H.J.  Holshouser, E. L.  and Deterline, W. A. (1958) “Measurement of driver reactions to tunnel conditions”, Proceedings of Highway Research Board.Vol.37, pp. 345-357.
-Herman, R. and Potts, R. B.  (1961) “Single-lane traffic theory and experiment”, Proceedings of Symposium on the Theory of Traffic Flow (R. Herman Ed.). Elsevier publishing Co. Amsterdam. pp. 120-146.
-Herman, R. and Rothery, R. (1967) “Propagation of disturbances in vehicular platoons”, Proceedings of Third International Symposium on the Theory of Traffic Flow (L.C. Edie, Ed.), American Elsevier publishing Co. New York. pp. 26-37.
-Hongfei, J., Zhicai, J. and Anning, N. (2003) “Develop a car-following model using data collected by ‘five-wheel system”. Proceedings of the IEEE Intelligent Transportation System, Vol. 1, China, pp. 346–351.
-Kim, T. and Zhang, H. M. (2004) “Gap time and stochastic wave propagation”, IEEE Intelligent Transportation Systems Conference, pp. 88-93.
-Koshi, M., Kuwahara, M. and Akahane, H. (1992) “Capacity of sags and tunnels injapanese motorways”, ITE Journal (May issue), pp.17–29.
-Karlaftis, M. G. and Vlahogianni, E. I.  (2011) “Statistics versus neural networks in transportation research: Differences, similarities and some insights”, Transportation Research Part C: Emerging Technologies. Vol. 19, No. 3, pp. 387-399.
-Khodayari, A. and Ghaffari, A. (2011) “ Modify car following model human effects based on locally linear neuro fuzzy”,  Intelligent Vehicles Symposium (IV), 2011 IEEE. pp. 661-666.
-Laval, J. A. and Daganzo, C. F. (2006) “Lane-changing in traffic streams”,  Transportation Research Part B Vol. 40, No. 3,  pp. 251–264.
-Laval, J. A. (2006) “Stochastic processes of moving bottlenecks: Approximate formulas for highway capacity”, Transportation Research Record, pp. 86–91.
-Laval, J. A. and Leclercq, L. (2010) “A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic”, Philosophical Transactions of The Royal Society A. 368, pp. 4519-4541.
-Laval A. J. (2010) “Hysteresis in traffic flow revisited: An improved measurement method, Transportation Research”, Part B. Vol. 45, No 2, pp. 385–391.
-Laval A. J. (2009) “Hysteresis in the fundamental diagram: impact of measurement methods”, 89th Annual Meeting of the Transportation Research Board, Washington, D.C.
-Mauch, M. and Cassidy, M. J. (2002) “Freeway traffic oscillations: observation and predictions”, The 15th International Symposium on Transportation and Traffic Flow Theory.
-Newell, G. F. (1962) “Theories of instability in dense highway traffic”, Journal of the Operations Research Society of Japan Vol. 5, pp.9–54.
-Newell, G. F. (2002)  “A simplified car-following theory: a lower order model”, Transportation Research Part B Vol. 36, pp. 196-205.
-NGSIM. Accessed at: http://ngsim-community.org/
-Orfanou, F., Vlahogianni, E and Karlaftis, M. (2012) “Identifying features of traffic hystersis on freeways,  Transportation Research,  Part B.
-Panwai, S. and  Dia, H. (2007) “ Neural agent car-following models, IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 1, pp. 60–70.
-Trajectory Explorer. Accessed at: http://trafficlab.ce.gatech.edu/tools.html.
-Treiterer, J.  and Myer,  J. A (1974) “The hysteresis phenomenon in traffic flow”,  Proceedings of the Sixth Symposium on Transportation and Traffic Flow Theory. D. J. Buckley (Ed.). pp. 213-219.
-Xiaoliang, Ma. (2006) “A neural-fuzzy framework for modeling car following behavior”, Systems, Man and Cybernetics, 2006. SMC'06. IEEE International Conference on. Vol. 2. IEEE, 2006, pp. 770-776.
-Yeo, H. and Skabardonis, A. (2009) “Understanding stop-and-go traffic in view of asymmetric traffic theory”, Transportation and Traffic Theory 2009: Golden Jubilee, Springer, pp. 99-115.
-Zheng, Z., Ahn, S., Chen, D. and Laval, J. A.  (2011) “Freeway traffic oscillations: Microscopic analysis of formations and propagations using wavelet transform”, Transportation Research Part B, Vol. 45, No. 9, pp. 1378-1388.
-Zheng, Z., Ahn, S., Chen, D. and Laval, J. (2011a) “Applications of wavelet transform for analysis of freeway traffic: bottlenecks, transient traffic, and traffic oscillations. Transportation Research Part B Vol. 45 No. 2,  pp.372–384.
-Zheng, Z., Ahn, S., Chen, D., Laval, J.A. (2011b) “Freeway traffic oscillations: microscopic analysis of formations and propagations using wavelet transform”. The 19th International Symposium on Transportation and Traffic flow Theory, pp.717–731.
-Zhang, H. M. (1999) “A mathematical theory of traffic hysteresis”, Transportation Research Part B, Vol. 33, pp. 1-23.
-Zhang, H. M. and Kim, T. (2005) “A car-following theory for multiphase vehicular traffic flow”, Transportation Research Part B, Vol. 39, pp. 385-399.
-Zheng, J., Suzuki, K. and Fujita., M. (2013) ” Car-following behavior with instaneous driver-vehicle reaction delay: a neural-network-based methodolghy”, Transportation Research Part B, Vol. 36, pp. 339-351.

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