Short-term Traffic Flow Forecasting Research Articles

Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm

Bounded rationally idea, rather that optimization idea, have result and better performance in decision making theory. Bounded rationality is the idea in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. The emotional theory is an important topic presented in this field. The new methods in the direction of purposeful forecasting issues, which are based on cognitive limitations, are presented in this study. The presented algorithms in this study are emphasizes to rectify the learning the peak points, to increase the forecasting accuracy, to decrease the computational time and comply the multi-object forecasting in the algorithms. The structure of the proposed algorithms is based on approximation of its current estimate according to previously learned estimates. The short term traffic flow forecasting is a real benchmark that has been studied in this area. Traffic flow is a good measure of traffic activity. The time-series data used for fitting the proposed models are obtained from a two lane street I-494 in Minnesota City, USA. The research discuss the strong points of new method based on neurofuzzy and limbic system structure such as Locally Linear Neurofuzzy network (LLNF) and Brain Emotional Learning Based Intelligent Controller (BELBIC) models against classical and other intelligent methods such as Radial Basis Function (RBF), Takagi–Sugeno (T–S) neurofuzzy, and Multi-Layer Perceptron (MLP), and the effect of noise on the performance of the models is also considered. Finally, findings confirmed the significance of structural brain modeling beyond the classical artificial neural networks.

Neural Network Optimized by Genetic Algorithm of Models for Real-Time Forecast of Traffic Flow

Short-term traffic flow forecast plays an important role in transit scheduling. A high-order generalized neural network model is constructed to actualize dynamic forecast on-line and a hybrid genetic algorithm and identical dimension recurrence idea are performed to optimize the structure and shape of neural network dynamically so as to enhance its forecast accuracy. With data collected from Dazhi Str., Harbin as the system input, the experimental result indicates that the average relative error of forecast is 5.53% and the maximum is less than 21%, which proves that the proposed neural network model can satisfy the precision request, accelerate the convergence speed, improve the global generalization ability and possess the practicality in short-term traffic flow forecast.

Gaussian Processes for Short-Term Traffic Volume Forecasting

The accurate modeling and forecasting of traffic flow data such as volume and travel time are critical to intelligent transportation systems. Many forecasting models have been developed for this purpose since the 1970s. Recently kernel-based machine learning methods such as support vector machines (SVMs) have gained special attention in traffic flow modeling and other time series analyses because of their outstanding generalization capability and superior nonlinear approximation. In this study, a novel kernel-based machine learning method, the Gaussian processes (GPs) model, was proposed to perform short-term traffic flow forecasting. This GP model was evaluated and compared with SVMs and autoregressive integrated moving average (ARIMA) models based on four sets of traffic volume data collected from three interstate highways in Seattle, Washington. The comparative results showed that the GP and SVM models consistently outperformed the ARIMA model. This study also showed that because the GP model is formulated in a full Bayesian framework, it can allow for explicit probabilistic interpretation of forecasting outputs. This capacity gives the GP an advantage over SVMs to model and forecast traffic flow.

Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow

This paper presents an adaptive hybrid fuzzy rule-based system (FRBS) approach for the modeling and short-term forecasting of traffic flow in urban arterial networks. Such an approach possesses the advantage of suitably addressing data imprecision and uncertainty, and it enables the incorporation of expert’s knowledge on local traffic conditions within the model structure. The model employs univariate and multivariate data structures and uses a Genetic Algorithm for the offline and online tuning of the FRBS membership functions according to the prevailing traffic conditions. The results obtained from the online application of the proposed FRBS are found to overperform those of the offline application and conventional statistical techniques, when modeling both univariate and multivariate traffic data corresponding to a real signalized urban arterial corridor.

Forecasting Approach for Short-term Traffic Flow based on Principal Component Analysis and Combined Neural Network

Abstract A combination approach based on Principal Component Analysis (PCA) and Combined Neural Network (CNN) is presented for short-term traffic flow forecasting. The historical data of the forecasted traffic volume and interrelated volumes have been processed by PCA. The results of PCA form the input data for CNN. It not only reduces the dimension of input variables and the size of CNN, but also reserves the main information of the original variables and eliminates relativity among them. An example for explanation of validity is given. The forecast results show that this approach is better than the typical Error Back-Propagation neural network (BP NN) with the same data.

Bayesian Time-Series Model for Short-Term Traffic Flow Forecasting

The seasonal autoregressive integrated moving average (SARIMA) model is one of the popular univariate time-series models in the field of short-term traffic flow forecasting. The parameters of the SARIMA model are commonly estimated using classical (maximum likelihood estimate and/or least-squares estimate) methods. In this paper, instead of using classical inference the Bayesian method is employed to estimate the parameters of the SARIMA model considered for modeling. In Bayesian analysis the Markov chain Monte Carlo method is used to solve the posterior integration problem in high dimension. Each of the estimated parameters from the Bayesian method has a probability density function conditional to the observed traffic volumes. The forecasts from the Bayesian model can better match the traffic behavior of extreme peaks and rapid fluctuation. Similar to the estimated parameters, each forecast has a probability density curve with the maximum probable value as the point forecast. Individual probability density curves provide a time-varying prediction interval unlike the constant prediction interval from the classical inference. The time-series data used for fitting the SARIMA model are obtained from a certain junction in the city center of Dublin.

Data Collection Time Intervals for Stochastic Short-Term Traffic Flow Forecasting

The specification of time intervals for data collection is a fundamental determinant of the nature and utility of the resulting traffic condition data streams. In the context of short-term traffic flow forecasting, the establishment of the data collection time interval should play a key role in determining the corresponding appropriate forecasting approach. The data collection time interval provides the forecasting horizon for one-step-ahead forecasting. Nevertheless, the need for more rigorous understanding of the effects of data collection time interval specification within the context of short-term traffic flow forecasting is not well recognized. By contrast, it has been common practice in previous research to select the data collection time interval and forecasting approach without explicit consideration of time interval effects or systematic evaluation of available forecasting methods. A stochastic seasonal autoregressive integrated moving average plus generalized autoregressive conditional heteroscedasticity (SARIMA+GARCH) structure proposed in previous work holds promise in providing accurate point forecasts and reasonable forecasting confidence intervals. In this paper, a spectrum of data collection time intervals is tested with an online forecasting algorithm developed based on the SARIMA+GARCH structure to determine the applicable data collection time intervals for this structure. In this test, both the forecast accuracy and the validity of the forecasting confidence intervals are investigated. This work serves as an important step toward establishing a short-term traffic condition forecasting framework that identifies appropriate forecasting approaches for candidate data collection time intervals based on the functional requirements of specific applications.

Soft-Computing Techniques Applied to Short-Term Traffic Flow Forecasting

Multi Linear Perceptron (MLP) neural networks, Radial Basis Function (RBF) neural networks, and Fuzzy Logic (FL) were used as soft-computing (or artificial intelligent) modelling techniques to come to short-term forecasts of traffic flow. The field data used for modelling was collected through single loop induction detectors on freeways 405 and 22 in Orange County, California. The data consisted of 30-s time bins of traffic flow [vehicles/3Os], occupancy [seconds/3Os], and reliability indicators (value 0-7) that were transformed into 5-min time bins. The paper concludes that the MLP and the RBF(2) - with 1 output - method outperform the FL and the RBF(1)-with 2 outputs method.

Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches

Several univariate and multivariate models have been proposed for performing short-term forecasting of traffic flow. Two different univariate [historical average and ARIMA (autoregressive integrated moving average)] and two multivariate [VARMA (vector autoregressive moving average) and STARIMA (space–time ARIMA)] models are presented and discussed. A comparison of the forecasting performance of these four models is undertaken with data sets from 25 loop detectors located in major arterials in the city of Athens, Greece. The variable under study is the relative velocity, which is the traffic volume divided by the road occupancy. Although the specification of the network’s neighborhood structure for the STARIMA model was relatively simple and can be further refined, the results obtained indicate a comparable forecasting performance for the ARIMA, VARMA, and STARIMA models. The historical average model could not cope with the variability of the data sets at hand.

Comparison of parametric and nonparametric models for traffic flow forecasting

Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to single point short-term traffic flow forecasting. Past research has shown seasonal ARIMA models to deliver results that are statistically superior to basic implementations of nonparametric regression. However, the advantages associated with a data-driven nonparametric forecasting approach motivate further investigation of refined nonparametric forecasting methods. Following this motivation, this research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether nonparametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.