Study on the Application of Markov Chains in Transportation

With the increasing demand for efficient traffic management driven by urbanization, predicting traffic patterns has become critical. This paper looks at the application of Markov chains in the field of transportation, focusing on their ability to model complex and dynamic traffic systems. Three Markov-based models are discussed: homogeneous Markov chains, Grey Markov chains, and Markov-based queueing models. Based on these three models, this paper provided the relevant applications, including pedestrian crash prediction, urban rail passenger flow forecasting, and bus stop zone length optimization. It is shown by the results that Markov chains are effective in these models. To elaborate, homogeneous Markov chains help identify high-risk areas for pedestrian crashes, which provides a targeted measurement for the traffic management departments. On the other hand, GM-Markov models improve the accuracy of passenger flow forecasting. Moreover, queueing models optimize bus stop efficiency, making transportation systems more reliable. These findings show that using Markov chains in transportation could lead to a better traffic system. As a result, cities can manage their transportation networks more effectively.