Weighing Parameters and Analysis of Congestion using Markov Chain Model
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Abstract
Markov chain (discrete-time Markov chain named after Andrey Markov, is a random process that undergoes transitions from one state to another on a state space. It must possess a property that is usually characterized as "memorylessness": the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property. Here the parameters are land use, road inventory, traffic characteristic and pedestrian. These parameters are enhancing the travel time of the road user because of not reaching the standards. These parameters helps to identify the gap .Linear equation helps to quantile measure of delay and encounter by meeting the needs of the user by standards to provide LOS A.
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References
Constantinos Antoniou .et.al (2007) “Traffic state prediction using Markov chain models” Proceedings of the European Control Conference 2007,Kos, Greece, July 2-5, 2007
Ortiz-Garcia, J. J., S. B. Costello, and M. S. Snaith. Derivation of Transition Probability Matrices for Pavement Deterioration Modeling. Journal of Transportation Engineering, Vol. 132, No. 2, pp. 141-161, February 1, 2006.
Scherer, W. T., and D. M. Glagola. Markovian models for bridge maintenance management. Journal of Transportation Engineering, Vol. 120, No. 1, pp. 37-51, January/February, 1994.
Geroliminis N., Skabardonis A., 2005, “Prediction of arrival profiles and queue lengths along signalized arterials using a Markov decision process” Transportation Research Record, 1934, 116-124.
O.T. Olaleye .et.al (2009) “A Markov Chain Approach to the Dynamics of Vehicular Traffic Characteristics in Abeokuta Metropolis” Research Journals of Applied Sciences, Engineering and Technology 1(3): 160-166, 2009 ISSN: 2040-7467.
A Text Book on MARKOV CHAINS and DECISION PROCESSES for ENGINEERS and MANAGERS by Theodore J. Sheskin.
Whittaker, J., Garside, S., and Lindveld, K. (1997). “Tracking and predicting a network traffic process.” Int. J. Forecast., 13(1), 51–61.
Yu, G., Hu, J. M., Zhang, C. S., Zhuang, L. K., and Song, J. Y. (2003).“Short term traffic flow forecasting based on Markov chain model.”Proc., IEEE Intelligent Vehicles Symp., Columbus, OH, 208–212.
Medhi, J. (2003) Stochastic Models in Queueing Theory. 2nd Edition, Esevier Inc., Berlin.