摘要:

Advance Traffic Management Systems (ATMS) must be able to respond to existing and predicted traffic conditions if they are to address the demands or the 1990's. Artificial intelligence and neural network are promising technologies that provide intelligent, adaptive performance in a variety of application domains. This paper describes two separate neural network systems that have been developed for integration into a ATMS blackboard architecture. The first system is an adaptive traffic signal light controller based upon the Hopfield neural network model, while the second system is a backpropagation model trained to predict urban traffic congestion. Each of these models are presented in detail with results attained utilizing a discrete traffic simulation shown to illustrate their performance.

ISSN:1065-5123    

DOI:10.1080/10248079508903828

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

The goal of an Advanced Traffic Management System (ATMS) is to efficiently manage transportation resources in response to dynamic traffic conditions. The utility of an ATMS will greatly depend upon its ability to adaptively respond to traffic demands, patterns and permutations. Traffic management systems have historically been limited to addressing the control of street signal lights. Algorithmic solutions to this problem have proved to be very restrictive, while expert system solutions have only shown valid results with small signal networks. None of these approaches has addressed the need for management of the overall transportation system of surface streets, interstate highways, public transportation, and emergency vehicle coordination. The volume of traffic combined with the number of streets and intersections an operator control station must monitor clearly dictates the need for computer support. The application of knowledge-based systems and neural networks provides an ATMS with the technology required to control traffic in an intelligent manner. Integrating these technologies with existing transportation methodologies produces a semi-autonomous system capable of reducing operator workloads while maintaining high levels of safety. This paper describes an intelligent traffic management control system called TERMINUS developed to adaptively respond to real-time traffic management problems.

ISSN:1065-5123    

DOI:10.1080/10248079508903829

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

This paper consists of two components. The First component addresses a key AHS infrastructure support issue—providing eight extra connector ramps, in addition to the eight existing highway-to-highway connector ramps for the conventional manual traffic, at a highway-to-highway interchange for accommodating continuous automated driving from one highway to the crossing highway. The proposedstaggered-diamonddesign requires only four, instead of eight, separate structures and hence greatly increases the feasibility of AHS infrastructure support. However, the reduced infrastructure complexity at such interchanges could still constrain considerably the design, operation and evolution of AHS. This is the focus of the second component.

ISSN:1065-5123    

DOI:10.1080/10248079508903830

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

Current research on advanced vehicle control systems (AVCS) emphasizes the regulation of individual vehicles from a vehicle-level point of view. To improve the roadway operational efficiency, however, it is necessary to coordinate the operations of the vehicles on the highway. In this paper, a highway-level control algorithm is proposed which optimizes the traffic flow from a system-level viewpoint. The highway is divided into cells. The density and average speed of vehicles in these cells are assumed to be measured in real-time. Desired vehicle acceleration and lane change commands are assumed to be transmitted through communication devices to the vehicles, which optimize the traffic flow over the whole highway section. The system-wide traffic is optimized by using the downstream traffic status as the “preview” information for upstream traffic regulation. Numerical simulations were performed to validate the effectiveness of the proposed control algorithms.

ISSN:1065-5123    

DOI:10.1080/10248079508903831

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:1065-5123    

DOI:10.1080/10248079508904595

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:1065-5123    

DOI:10.1080/10248079508904596

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:1065-5123    

DOI:10.1080/10248079508904597

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:1065-5123    

DOI:10.1080/10248079508903833

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:1065-5123    

DOI:10.1080/10248079508903834

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:1065-5123    

DOI:10.1080/10248079508903835

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor