Modelling Traffic Scenarios via Markovian Opinion Dynamics.
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(2023)
摘要
We address the question of whether opinion dynamics models can be exploited in novel scenarios, such as traffic flow on highway lanes. In this paper, we design a Markovian model and compare its predictions with those obtained from the widely recognized Cell Transmission Model (CTM) for the same traffic scenario. We identify potential challenges that may arise and propose strategies to address them. Furthermore, we present a concise demonstration showcasing the predictive capabilities of our proposed model through a small-scale example.
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关键词
Traffic Scenarios,Opinion Dynamics,Traffic Flow,Markov Chain,Maximum Likelihood Estimation,Transition State,Modeling Framework,Discrete-time,Individual Agency,Poisson Process,Network Configuration,Traffic Control,State Trajectories,Arrival Rate,Disturbance Term,Traffic Model,Lane Change,Multiple Trajectories,Vehicle Cells,Discrete-time Markov Chain,One-way Street,Infinite Capacity,Vehicle Density,Traffic Congestion,Time Instants,Constant Density,Transition Probabilities,Control Strategy,Network Of Agents
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