This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model us...This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model uses the cell transmission model to calculate the macroscopic variables of the vehicle transmission, and the maximum information principle to examine the velocity distribution in each cell. The velocity distribution based on maximum information principle is solved by the Lagrange multiplier method. The advantage of the proposed model is that it can simultaneously calculate the hydrodynamic variables and velocity distribution at the cell level. An example shows how the proposed model works. The proposed model is a hybrid traffic simulation model, which can be used to understand the self-organization phenomena in traffic flows and predict the traffic evolution.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.71071024)the Hunan Provincial Natural Science Foundation(Grant No.12JJ2025)
文摘This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model uses the cell transmission model to calculate the macroscopic variables of the vehicle transmission, and the maximum information principle to examine the velocity distribution in each cell. The velocity distribution based on maximum information principle is solved by the Lagrange multiplier method. The advantage of the proposed model is that it can simultaneously calculate the hydrodynamic variables and velocity distribution at the cell level. An example shows how the proposed model works. The proposed model is a hybrid traffic simulation model, which can be used to understand the self-organization phenomena in traffic flows and predict the traffic evolution.
