Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network

Abstract

A radial basis function (RBF) neural network has recently been applied to time-series forecasting. The test results of an RBF neural network in forecasting short-term freeway traffic volumes are provided. Real observations of freeway traffic volumes from the San Antonio TransGuide System have been used in these experiments. For comparison of forecasting performances, Taylor series, exponential smoothing method (ESM), double exponential smoothing method, and backpropagation neural network were also designed and tested. The RBF neural network model provided the best performance and required less computational time than BPN. It seems that RBF and ESM can be a viable forecasting routine for advanced traffic management systems. There are some tradeoffs between RBF and ESM. Although the performance of ESM is inferior to RBF, the former does not need a complicated training process or historic database, and vice versa. However, even in the best performance case, 35 percent of the forecast traffic volumes showed 10 percent or more percentage errors. This means that we cannot heavily depend on the forecast traffic volumes as long as we are utilizing the models tested. Further work is needed to provide a more reliable traffic forecasting model.

Modelling the impact of traffic incidents on travel time reliability

“…This result is consistent with previous studies (Park et al, 1998;Tavassoli Hojati et al, 2011;Chung, 2012). In addition, weekdays (from Monday through Friday) and weekends (Saturday and Sunday), public holidays and school holidays have different traffic patterns and incident characteristics.…”

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

“…Since early 1980s, univariate time series models, mainly Box-Jenkins Auto-Regressive Integrated Moving Average (ARIMA) [3] and Holt-Winters Exponential Smoothing (ES) models [15], [22], have been widely used in traffic prediction. In the last decade, Neural Network (NNet) models also has been extensively used in forecasting of various traffic parameters, including speed [23], [10], travel time [21], and traffic flow [19], [17]. Nowadays, ARIMA, ES and NNet models are used as benchmarking methods for short-term traffic prediction [17], [16].…”

“…In the last decade, Neural Network (NNet) models also has been extensively used in forecasting of various traffic parameters, including speed [23], [10], travel time [21], and traffic flow [19], [17]. Nowadays, ARIMA, ES and NNet models are used as benchmarking methods for short-term traffic prediction [17], [16]. However, these approaches consider traffic flow as a simple time-series data and ignore phenomenons that particularly happen to traffic data.…”

Spatiotemporal Traffic State Prediction Based on Discriminatively Pre-trained Deep Neural Networks

“…Young-Seon et al proposed a short-term traffic flow prediction algorithm combining the online-based SVR with the weighted learning method for short-term traffic flow predictions [1]. ANN is considered one of the best tools to model highly nonlinear relationships between inputs and outputs, and many papers have adopted different ANN models, such as the Bayesian neural network [11] and radial basis function neural network [12], for predicting traffic flow. For more information, Vlahogianni et al [13] provide a good review of the proposed techniques as well as the challenges of short-term prediction.…”