Browsing by Author "Liu, Mingzhe"

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    Theoretical investigation of traffic flow : inhomogeneity induced emergence : a dissertation presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Auckland, New Zealand
    (Massey University, 2010) Liu, Mingzhe
    This research work is focused on understanding the effects of inhomogeneity on traffic flow by theoretical analysis and computer simulations. Traffic has been observed at almost all levels of natural and manmade systems (e.g., from microscopic protein motors to macroscopic objects like cars). For these various traffic, basic and emer- gent phenomena, modelling methods, theoretical analysis and physical meanings are normally concerned. Inhomogeneity like bottlenecks may cause traffic congestions or motor protein crowding. The crowded protein motors may lead to some human diseases. The congested traffic patterns have not been understood well so far. The modelling method in this research is based on totally asymmetric simple exclusion process (TASEP). The following TASEP models are developed: TASEP with single inhomogeneity, TASEP with zoned inhomogeneity, TASEP with junction, TASEP with site sharing and different boundary conditions. These models are motivated by vehicular traffic, pedestrian trafficc, ant traffic, protein motor traffic and/or Internet traffic. Theoretical solutions for the proposed models are obtained and verified by Monte Carlo simulations. These theoretical results can be used as a base for further developments. The emergent properties such as phase transitions, phase separations and spontaneous symmetry breaking are observed and discussed. This study has contributed to a deeper understanding of generic traffic dynamics, particularly, in the presence of inhomogeneity, and has important implications for explanation or guidance of future traffic studies.
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    Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2006) Liu, Mingzhe
    In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way. For urban straight roads, two types of traffic flow, free and car-following flow, have been simulated. A novel five-stage speed-changing CA model is developed to describe free flow. The 1.5-second headway, based on field data, is used to simulate car-following processes, which corrects the headway of 1 second used in all previous CA models. Novel and realistic CA models, based on the Normal Acceptable Space (NAS) method, are proposed to systematically simulate driver behaviour and interactions between drivers to enter single-lane Two-Way Stop-Controlled (TWSC) intersections and roundabouts. The NAS method is based on the two following Gaussian distributions. Distribution of space required for all drivers to enter intersections or roundabouts is assumed to follow a Gaussian distribution, which corresponds to heterogeneity of driver behaviour. While distribution of space required for a single driver to enter an intersection or roundabout is assumed to follow another Gaussian distribution, which corresponds to inconsistency of driver behavior. The effects of passing lanes on single-lane highway traffic are investigated using fine grids CA. Vehicles entering, exiting from and changing lanes on passing lane sections are discussed in detail. In addition, a Genetic Algorithm-based Neural Network (GANN) method is proposed to predict Short-term Traffic Flow (STF) in urban networks, which is expected to be helpful for traffic control. Prediction accuracy and generalization ability of NN are improved by optimizing the number of neurons in the hidden layer and connection weights of NN using genetic operations such as selection, crossover and mutation.