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The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA‐WNN. A comparative analysis of the experimental results shows that the performance of the IGSA‐WNN‐based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model. [ABSTRACT FROM AUTHOR]

Congestion has become highly recognized as a worldwide traffic problem, as traffic demand has grown steadily over the past few decades. Variable speed limits (VSLs) are an intelligent transportation system (ITS) measure that limits mainline flow to mitigate bottleneck congestion. Currently, VSLs have become proactive based on short-term prediction. Proactive VSLs succeed in simulation evaluations, but few have been deployed in the field and their real-world effectiveness has not been proven. Various factors may lead to this limitation, such as the absence of reliable field application software, accuracy of prediction models, and high computation time for proactive control. To address this research gap, this study reports a preliminary VSL test and details its implementation results on Whitemud Drive, Edmonton, Canada. First, based on field traffic measurements before VSL control, recurrent bottleneck locations are identified. Second, the proactive control algorithm is briefly introduced. Then, a software application is designed to realize all necessary functions for VSL field implementation.With all these in hand, the preliminary field test was conducted and the VSL control performance and reliability are evaluated. Finally, the results for before and after VSL control implementation are analyzed in depth. The analysis compares average traffic speed, standard deviation of speed, total travel time, and total travel distance. The results from this study confirm that proactive VSL can relieve recurrent traffic congestion effectively. [ABSTRACT FROM AUTHOR]

Conditions at a freeway bottleneck are predicted with a two-dimensional stochastic differential equation (SDE) model of flow and speed. Stochastic behavior is assumed to be standard Brownian motion. To fully describe conditions observed in the field, it is necessary to couple the SDE model with a stochastic capacity model, thus forming a stochastic capacity-differential equation (SCDE) model. The model contains mechanisms to (1) trigger a breakdown event when the traffic flow reaches or exceeds the freeway capacity, and (2) identify when recovery takes place. Measures of freeway performance are derived from SCDE model predictions. They include breakdown probability, expressed as a function of traffic flow and time-of-day, and recovery time, i.e., the length of time the freeway remains in a congested state before returning to a free-flow state. To achieve dependable model forecasts, it is necessary to impose constraints when fitting the stochastic capacity model. The constraint condition consists of matching the breakdown and prebreakdown flow distributions derived from field data and stochastic capacity model forecasts. Sensitivity analyses are performed to test validity of the model-fitting procedure and to test the dependability of the SCDE model forecasts. An empirical study of recurrent workday freeway congestion on I-93 in Salem, NH is used to demonstrate the approach. The approach demonstrates the need to treat breakdown as a random process. [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods. [ABSTRACT FROM AUTHOR]

Experiments were carried out to measure the noise emission levels in cruise conditions and acceleration/deceleration noise adjustments were made for three types of single vehicles at a reference distance. Integrating vehicle noise emission model and propagation model, the traffic noise at a signal-controlled pedestrian crossing junction was simulated dynamically with real-time traffic flow data provided by microcosmic traffic simulation software. Finally, both the accuracy of simulation results and the practicability of this approach were verified by comparing the simulation results with the measured data. Some prominent characteristics of traffic noise near a signal-controlled pedestrian crossing junction were analyzed as well. VC 2011 Institute of Noise Control Engineering [ABSTRACT FROM AUTHOR]