A markov chain based traffic flow control model for reducing vehicles' CO<sub>2</sub>emissions

Sun, Jun; Li, Chunxiao; Ding, Jie; Jing, Yang; Zhi, Liu

doi:10.1109/icves.2015.7396926

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Markov Chain was broadly use for modeling traffic for different purposes, such as to reduce vehicle emission [13], short-term traffic flow forecasting [14], modeling the multi-traffic signal-control synchronization [15], traffic state prediction [16], travel time estimation [17], etc. However, no one has modeled the motorcycle maneuver using the Markov Chain model.…”

Section: Introductionmentioning

confidence: 99%

Motorcycle Movement Model Based on Markov Chain Process in Mixed Traffic

Mardiati¹,

Trilaksono²,

Gondokaryono³

et al. 2018

IJECE

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<p>Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4th order polynomial.</p>

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