tailieunhanh - Markov property analysis of the real world driving data and its application

In this study, the real–world driving data of bus system in Hanoi are proved to be a stationary time-series and have the Markov property, based on the analysis of random number series which are the values of instantaneous speed of the bus recorded by the GPS in a whole bus route. | Journal of Science & Technology 126 (2018) 054-058 Markov Property Analysis of the Real-World Driving Data and Its Application Nguyen Thi Yen Lien1,2, Nghiem Trung Dung1,* 1 Hanoi University of Science and Technology University of Transport and Communication Received: March 16, 2017; Accepted: May 25, 2018 2 Abstract There are a number of methods that can be used for the development of a driving cycle. Among them, the Markov chain is a promising approach which has been being widely studied in recent years in many developed countries but still is scarce in Vietnam. In this study, the real–world driving data of bus system in Hanoi are proved to be a stationary time-series and have the Markov property, based on the analysis of random number series which are the values of instantaneous speed of the bus recorded by the GPS in a whole bus route. The typical driving cycle of the bus route was then developed based on this finding. The developed cycle was assessed in the comparison with the on – road driving data. A good conformity of this driving cycle with the real –world driving data was observed. Keywords: Markov chain, stationary series, driving cycle, Hanoi bus, emission factor. 1. Introduction A*driving cycle is a time series of vehicle speeds recorded at successive time points; it is developed from the data collected by driving the testing vehicle on the real road network. The driving cycle provides the basis data for vehicle design and the important index for emission measuring. In recent years, there were several studies on real driving cycle building in Vietnam such as Tong et al (2011) [1] and Tuan Anh et al (2012) [2]. given the present state, the future and past states are independent [7]. The formula is as follows: P ( X n +1 = xn +1 | X 1 = x1 , X 2 = x2 ,., X n = xn ) (1) = P ( X n +1 = xx +1 | X n = xn ) The set of random variables Xn is called the state space of the chain. The conditional probabilities Pij: = P(Xn+1 = j|Xn = i) are called .

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• Journal of Science and Technology
• Markov property analysis
• The real world driving data
• The real–world driving data
• The real –world driving data
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