A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN

Traffic Flow Analysis Based on Queuing Models

One of the most important issues in the plant layout design especially in mass production organizations with high inter-plant logistics is‘material flow and inter-plant traffic analysis and its effects on the production capabilities or pauses in production lines. In this paper the inter-plant traffic analysis issue on the basis of single channel queue model (M/M/1) is analyzed in a carmaker c...

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation mod...

Advancing Traffic Flow Theory Using Empirical Microscopic Data

An improved opposition-based Crow Search Algorithm for Data Clustering

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...

Calibration of microscopic traffic-flow models using multiple data sources.

Parameter identification of microscopic driving models is a difficult task. This is caused by the fact that parameters--such as reaction time, sensitivity to stimuli, etc.--are generally not directly observable from common traffic data, but also due to the lack of reliable statistical estimation techniques. This contribution puts forward a new approach to identifying parameters of car-following...