Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg–Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs

Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks

This paper presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: a) strongly non-linear characteristics are unavoidable in traffic flow data; b) memory space for implementation of short-term traffic flow predictors is limited; c) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; d) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase

Operation properties and δ-equalities of complex fuzzy sets

A complex fuzzy set is a fuzzy set whose membership function takes values in the unit circle in the complex plane. This paper investigates various operation properties and proposes a distance measure for complex fuzzy sets. The distance of two complex fuzzy sets measures the difference between the grades of two complex fuzzy sets as well as that between the phases of the two complex fuzzy sets. This distance measure is then used to define equalities of complex fuzzy sets which coincide with those of fuzzy sets already defined in the literature if complex fuzzy sets reduce to real-valued fuzzy sets. Two complex fuzzy sets are said to be d-equal if the distance between them is less than 1 d. This paper shows how various operations between complex fuzzy sets affect given δ-equalities of complex fuzzy sets. An example application of signal detection demonstrates the utility of the concept of δ-equalities of complex fuzzy sets in practice

An Application of Neural Network and RuleBased System for Network Management: Application Level Problems

The more complex a network becomes, the more reliable and intelligent a network management system must be to consistently monitor the network and detect abnormal situations in a timely manner as they occur. Expert system techniques have been widely accepted to create network management systems. Despite the fact that there are a number of network management systems, most of them deal only with problems at the lower layers of the network hierarchy (the data link, or the network layer). The nature of problems at the application level significantly differs from of those that occur at the lower levels. Lower layer problems are well-understood while problems at the application level are complex, application dependent, and distinct from one another. Consequently, a network management system, in particular a fault management system, used at this level should be able to cope with these difficulties and dependencies. We propose a hybrid system which consists of neural network module and a rule-based system for monitoring and diagnosing problems occur at the application level. The domain name system (DNS) was selected as a testbed application for the prototype system. 1. Expert systems and network management The more complex a network becomes, the more reliable and intelligent a network management system must be to consistently monitor the network and detect abnormal situations in a timely manner as they occur. Expert system techniques have been widely accepted, and applied to create intelligent network management systems. Currently, there are many network management systems available, most of which were implemented by using two Artificial Intelligent (AI) techniques: expert systems and neural networks

Event handling for distributed real-time cyber-physical systems

Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. This paper presents some challenges for developing distributed real-time Cyber-Physical Systems. The focus is on one particular challenge, namely event modelling in distributed real-time CPS. A Web-of-Things based CPS framework for event handling and processing is proposed. To illustrate the application of the proposed framework, a case study for achieving demand response in a smart home is provided

Traffic flow forecasting neural networks based on exponential smoothing method

This paper discusses a neural network development approach based on an exponential smoothing method which aims at enhancing previously used neural networks for traffic flow forecasting. The approach uses the exponential smoothing method to pre-process traffic flow data before implementing on neural networks for training purpose. The pre-processed traffic flow data, which is lesser non-smooth, discontinuous and lumpy than the original traffic flow data, is more suitable to use for neural network training. This neural network development approach was evaluated by forecasting real-time traffic conditions on a section of the freeway in Western Australia. Regarding training errors which indicate capability in fitting traffic flow data, the neural network models developed by the proposed approach was capable to achieve more than 20% of the rate of improvement relative to the neural network developed based on the original traffic flow data. Regarding testing errors which indicate generalization capability for traffic flow forecasting, the neural network models developed by the proposed approach was capable in achieving more than 8% of the rate of improvement relative to the neural networks developed based on the original traffic flow data

Objectsim - The Conceptual Model Of A Generic Parallel & Distributed Computer Architecture Simulator.

This paper describes the design and implementation of OBJECTSIM. OBJECTSIM overcomes the narrow focus of previous architecture simulators by specifying a small number of generic simulation objects (and an associated hierarchy) that are capable of implementing a wide range of target systems. Target systems range from multi-stage interconnection networks (MINs) and ATM and BISDN networks, through to bus and network based multiprocessors. OBJECTSIM also provides trace, execution and stochastic driver mechanisms. To simulate large systems ( > 256 nodes ) OBJECTSIM uses a distributed object oriented simulation kernel. The combination of this kernel with the object hierarchy method makes OBJECTSIM a unique contribution to the field. OBJECTSIM runs on a network of SUN stations