Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

Method and system for traffic simulation of road network

Abstract: According to one embodiment, a system is disclosed, which uses road parameters defining the road network and model parameters used as initial-value parameters, thereby performing traffic simulation by the microsimulation method. The system includes a traffic simulator and a display controller. The traffic simulator performs traffic simulation to predict a traffic condition on an object road of a road network. The display controller controls a display unit, displaying the result of the simulation. More precisely, the display controller displays a dynamic image showing the traffic condition of vehicles running on the road network, on the screen of the display unit, and changes the image in terms of pattern, in accordance with a display instruction.

Urban Network Traffic Simulation:TRAF-NETSIM Program

Abstract: The network simulation program NETSIM is now a component model of the integrated traffic simulation system TRAF. The current version of the model is therefore referred to as the TRAF-NETSIM program. As part of an effort aimed at developing, maintaining, and supporting the TRAF simulation system, TRAF-NETSIM has been extensively modified over the past five years. Four new features have been added to the TRAF-NETSIM simulation program: actuated controller logic, identical traffic streams, conditional turning movements, and signal transition. Several major modifications have also been made to the simulation logic to resolve the problems encountered during the testing of the simulation program; to enhance the logic to represent complex decision processes; and to enhance and extend the input-output capabilities, user-interaction, and the computational efficiency of the program. These new features and the modifications incorporated in the TRAF-NETSIM simulation model are described in this paper.

Optimal traffic networks topology: A complex networks perspective

Abstract: We investigate and analyse an optimal traffic network structure for resisting traffic congestion with different volumes of traffic. For this aim, we introduce a cost function and user-equilibrium assignment (UE) which ensures the flow balance on traffic systems. Our finding is that an optimal network is strongly dependent on the total system flow. And the random network is most desirable when the system flow is small. But for the larger volume of traffic, the network with power-law degree distribution is the optimal one. Further study indicates, for scale-free networks, that the degree distribution exponent has large effects on the congestion of traffic network. Therefore, the volume of traffic and characteristic of network determine the optimal network structure so as to minimize the side-effect produced by traffic congestion.

Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management

Abstract: Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management We present a stochastic traffic engineering framework for optimizing bandwidth provisioning and route selection in networks. Traffic demands are uncertain and specified by probability distributions, and the objective is to maximize a risk-adjusted measure of network revenue that is generated by serving demands. Considerable attention is given to the appropriate measure of risk in the network model. We also advance risk-mitigation strategies. The optimization model, which is based on mean-risk analysis, enables a service provider to maximize a combined measure of mean revenue and revenue risk. The framework is intended for off-line traffic engineering, which takes a centralized view of network topology, link capacity and demand. We obtain conditions under which the optimization problem is an instance of convex programming. We study the properties of the solution and show that it asymptotically meets the stochastic efficiency criterion.In our numerical investigations we illustrate the impact of demand uncertainty on various aspects of the optimally traffic engineered solutions. The service provider's tolerance to risk is shown to have a strong influence on the traffic engineering and revenue management decisions. We develop the efficient frontier, which is the set of Pareto optimal pairs of mean revenue and revenue risk, to aid the service provider in selecting its operating point.

Neural network based online traffic signal controller design with reinforcement training

Abstract: Traffic congestion leads to problems like delays, decreasing flow rate, and higher fuel consumption. Consequently, keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper, a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions.