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.

Integration of neurocomputing and system simulation for modeling continuous improvement systems in manufacturing

Abstract: The use of a neural network embedded in a larger general-purpose simulation system (GPSS) simulation used to model continuous improvement systems (CIS) policies in a factory setting is described. The neural network is used to accelerate the identification of an effective CIS policy by providing a more realistic simulation framework. The interface between general simulation theory and neural network simulation is examined. Neural networks, when embedded in larger general-purpose simulations, are found to offer the potential for improving on the capabilities of those simulations, in particular manufacturing simulations for continuous improvement of production processes.

Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flows

Abstract: Traffic assignment distributes Origin-Destination (OD) trips in a network, and determines the flow patterns in a traffic network. This research aims at developing simulation-based algorithm for dynamic traffic assignment problems under mixed traffic flow considerations. Four different physical vehicle types are explicitly considered and modeled, including car, bus, motorcycle, and truck. Four different behavioral rules, pre-specified-path driver, user-equilibrium driver, system-optimization driver, and real-time information driver, are considered in the solution procedure. The DTA algorithm consists of an inner loop that incorporates a direction finding mechanism for the search process for System Optimization (SO) and User Equilibrium (UE) classes based on the simulation results of the current iteration, including experienced vehicular trip times and marginal trip times. In order to understand tripmaker acceptance toward route guidance, a survey is conducted to explore possible behavioral classifications and associated percentages. Numerical experiments are conducted in a test network to illustrate the capabilities of the simulation-based DTA procedures, and to observe how system performs under multiple user classes conditions, including multiple user behavior rules and multiple physical vehicle classes.

Synopsis of Traffic Simulation Models

Abstract: Computer simulation modeling is an established tool for assessing traffic operations. Over the past three decades, a variety of traffic simulation models have been developed, and many experiments and applications of these traffic simulation models to imaginary and real traffic operations have been conducted. This paper is intended to review widely used and newly developed models, in terms of modeling mechanisms, characteristics, and applications. Traffic simulation theories and approaches are briefly described. Simulation models developed for different traffic systems are then reviewed, including those for urban networks, freeways and integrated urban street/freeway systems. Important issues on model application are discussed.

Testing and benchmarking of microscopic traffic flow simulation models

Abstract: Microscopic simulation models are becoming increasingly important tools in modelling transport systems. There are a large number of available models used in many countries. The important difficult stage in the development and use of such models is the calibration and validation of the microscopic sub-models describing the traffic flow, such as the car following models for example. The aim of this paper is to present recent progress in calibrating more than a dozen microscopic traffic flow models with very different data sets conducted by DGPS-equipped cars (Differential Global Positioning System), loop detectors and human observers. Different approaches to measure the errors the models produce in comparison to reality are compared. It can be stated that from a microscopic point of view errors of about 15-20% in headwayand travel time-estimation and about 27% in speed-estimation of individual vehicles in the car following process seem to be the minimal reachable level. Furthermore, the larger the simulation horizon is, the smaller the diversity of the analyzed models become in comparison to the diversity in the driver behaviour. Most interesting, no model cold be denoted to be the best and especially highly sophisticated models did not produce better results than very simple ones.