- Email: [email protected]
Intelligent control of vehicle dynamic systems by artificial neural network
Recent doctoral dissertation Systems (ATIS) and Advanced Traffic Management Systems (ATMS) environment. The proposed system has two components: route assignment and traffic simulation. Three simulation methodologies are integrated into the traffic simulation component. They are the macro-particle traffic simulation model (MPSM), the modified MPSM (M-MPSM). and the microscopic (MICRO) model. The route assignment component follows an integrated assignment-simulation framework which dynamically assigns both guided and unguided vehicles to the network in both spatial and temporal dimensions. It uses a learning process in which the new route assignment uses information gained from previous iterations in the computation of new paths. During each iteration, guided vehicles follow routes according to time-dependent shortest paths while unguided vehicles follow static shortest paths. To satisfy real-time computational requirements, the proposed system has been developed on the Connection Machine, CM-Z. a massively parallel computer. Extensive numerical examples were carried out to analyze the computation and application aspects. Results indicate that parallel computing architectures offer promising platforms for real-time operations and that the proposed system is capable of analyzing the network performance under various ATMS scenarios.
Intelligent control of vehicle dynamic systems by artificial neural network. Kim, Hoyong, Ph.D. North Carolina State University. 1995. 134 pp. Director: Paul I. Ro. Order Number DA9537037 In this study, two artificial neural network controls for a vehicle four wheel steering system, a sliding mode control combined with an artificial neural network. and an unsupervised learning control are proposed. In the first control scheme, the neural network estimates known or even unknown dynamics such that the control parameters of the sliding mode can be adaptively adjusted. The adaptive capability of neural network minimizes the necessary switching gain of the discontinuous control to compensate for the uncertainties. The second control scheme, a neural controller using unsupervised learning, is able to compensate for the unknown uncertainties, resulting in the robust control of nonlinear vehicle dynamics,
INtelligent TRaffic Evaluator for Prompt Incident Diagno& in a Multi Media environment:INTREPID MM. Author: Suttayamully, Somprasong, Ph.D. The Ohio State University, 1995. 284 pp. Adviser: Fabian C. Hadipriono. Order Number DA9534075 Incident-related congestion on freeways costs the nation millions of dollars a year in lost productivity, property damage, and personal injuries. The situation on rural freeways is even worse than that on urban freeways because the resources required for appropriate incident response are not always nearby. In addition, high-technology equipment,
63
such as incident detection systems and closedcircuit television, is not available to detect and verify incidents on rural freeways in a timely manner. To provide quick and suitable responses, a knowledge-based system for incident management is needed. The INtelligent TRaffic Evaluator for Prompt Incident Diagnosis (INTREPID) was developed as an intelligent system to assist a dispatcher to appropriately manage an incident. Unlike other systems, INTREPID allows users to directly enter key information gathered from eyewitnesses to obtain responses from the proper agencies and to request the proper equipment without delay.
An investigation of the analytical capability of object-oriented programming in transport modelling. Ton, That T. Ph.D. University of’ Nw South Wales (Australia), 1995. This dissertation investigates the potential capabilities of object-oriented programming for transportation modeling. There are three original contributions from this research. The first is the establishment of an object-oriented conceptual framework for representing theoretical transportation models. This framework uses three key concepts: the system approach, the object-oriented approach, and software reusability. The second is the development of TRANSOOP. a reusable object-oriented software library for transport modeling. TRANSOOP contains 33.000 lines of C++ programming code implementing 130 basic models. identified from eight supporting domains: land use. transportation, traffic, spatial geometry, environmental impact assessment, mathematics, statistics and computing utility. The third contribution is the development of an evaluation methodology for assessing the effectiveness of a software library like TRANSOOP to support the development of new transportation modeling applications. TRANSCOOP support was successfully applied to five case study applications in transportation engineering.
Modelling the day-today dynamics of driver route choice and traffic control. Sorah, Harun al-Rasyid, Ph.D. The University of‘ Leeds. 1995. Equilibrium models of traffic networks predict an ‘ultimate’ long-term average state but neglect sources of variability and are behaviorally limited. In contrast, this study develops a day-to-day dynamic model (D-Day) based explicitly on the notions of variability. The model includes a microscopic model of individuals’ route choice behavior and a macroscopic traffic model that enables a day-to-day evolution of the driver and traffic system environment to be simulated over time, and generates the whole range of possible state of network performance and route choice. D-Day has been tested over a range of network configurations from a simple two-link network to realistic large-scale networks and in particular to
Intelligent control of vehicle dynamic systems by artificial neural network. Kim, Hoyong, Ph.D. North Carolina State University. 1995. 134 pp. Director: Paul I. Ro. Order Number DA9537037 In this study, two artificial neural network controls for a vehicle four wheel steering system, a sliding mode control combined with an artificial neural network. and an unsupervised learning control are proposed. In the first control scheme, the neural network estimates known or even unknown dynamics such that the control parameters of the sliding mode can be adaptively adjusted. The adaptive capability of neural network minimizes the necessary switching gain of the discontinuous control to compensate for the uncertainties. The second control scheme, a neural controller using unsupervised learning, is able to compensate for the unknown uncertainties, resulting in the robust control of nonlinear vehicle dynamics,
INtelligent TRaffic Evaluator for Prompt Incident Diagno& in a Multi Media environment:INTREPID MM. Author: Suttayamully, Somprasong, Ph.D. The Ohio State University, 1995. 284 pp. Adviser: Fabian C. Hadipriono. Order Number DA9534075 Incident-related congestion on freeways costs the nation millions of dollars a year in lost productivity, property damage, and personal injuries. The situation on rural freeways is even worse than that on urban freeways because the resources required for appropriate incident response are not always nearby. In addition, high-technology equipment,
63
such as incident detection systems and closedcircuit television, is not available to detect and verify incidents on rural freeways in a timely manner. To provide quick and suitable responses, a knowledge-based system for incident management is needed. The INtelligent TRaffic Evaluator for Prompt Incident Diagnosis (INTREPID) was developed as an intelligent system to assist a dispatcher to appropriately manage an incident. Unlike other systems, INTREPID allows users to directly enter key information gathered from eyewitnesses to obtain responses from the proper agencies and to request the proper equipment without delay.
An investigation of the analytical capability of object-oriented programming in transport modelling. Ton, That T. Ph.D. University of’ Nw South Wales (Australia), 1995. This dissertation investigates the potential capabilities of object-oriented programming for transportation modeling. There are three original contributions from this research. The first is the establishment of an object-oriented conceptual framework for representing theoretical transportation models. This framework uses three key concepts: the system approach, the object-oriented approach, and software reusability. The second is the development of TRANSOOP. a reusable object-oriented software library for transport modeling. TRANSOOP contains 33.000 lines of C++ programming code implementing 130 basic models. identified from eight supporting domains: land use. transportation, traffic, spatial geometry, environmental impact assessment, mathematics, statistics and computing utility. The third contribution is the development of an evaluation methodology for assessing the effectiveness of a software library like TRANSOOP to support the development of new transportation modeling applications. TRANSCOOP support was successfully applied to five case study applications in transportation engineering.
Modelling the day-today dynamics of driver route choice and traffic control. Sorah, Harun al-Rasyid, Ph.D. The University of‘ Leeds. 1995. Equilibrium models of traffic networks predict an ‘ultimate’ long-term average state but neglect sources of variability and are behaviorally limited. In contrast, this study develops a day-to-day dynamic model (D-Day) based explicitly on the notions of variability. The model includes a microscopic model of individuals’ route choice behavior and a macroscopic traffic model that enables a day-to-day evolution of the driver and traffic system environment to be simulated over time, and generates the whole range of possible state of network performance and route choice. D-Day has been tested over a range of network configurations from a simple two-link network to realistic large-scale networks and in particular to