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
This paper describes a location specific cell transmission model of freeway traffic based on the observed variability of fundamental diagrams both along and across freeway segments. This model extends the original cel...This paper describes a location specific cell transmission model of freeway traffic based on the observed variability of fundamental diagrams both along and across freeway segments. This model extends the original cell transmission model （CTM） mechanism by defining various shapes of fundamental diagrams to reproduce more complex traffic phenomena, including capacity drops, lane-by-lane variations, nonho- mogeneous wave propagation velocities, and temporal lags. A field test on a Canadian freeway was used to demonstrate the validity of the location specific CTM. The simulated spatio-temporal evolutions of traffic flow show that the model can be used to describe the traffic dynamics near bottlenecks more precisely than the original model.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.
基金Supported in part by the National Key Basic Research and Devel-opment (973) Program of China (No. 2006CB705506)the National Natural Science Foundation of China (No. 50708055)+1 种基金the Key Technologies Research & Development Program of the Eleventh Five-Year Plan of China (No. 2007BAK35B06)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education
文摘This paper describes a location specific cell transmission model of freeway traffic based on the observed variability of fundamental diagrams both along and across freeway segments. This model extends the original cell transmission model （CTM） mechanism by defining various shapes of fundamental diagrams to reproduce more complex traffic phenomena, including capacity drops, lane-by-lane variations, nonho- mogeneous wave propagation velocities, and temporal lags. A field test on a Canadian freeway was used to demonstrate the validity of the location specific CTM. The simulated spatio-temporal evolutions of traffic flow show that the model can be used to describe the traffic dynamics near bottlenecks more precisely than the original model.
