Short-term forecasting of traffic parameters such as flow and occupancy is an essential element of modern Intelligent Transportation Systems research and practice. Although many different methodologies have been used for short-term predictions, literature suggests neural networks as one of the best alternatives for modeling and predicting traffic parameters. However, because of limited knowledge regarding a network’s optimal structure given a specific dataset, researchers have to rely on time consuming and questionably efficient rules-of-thumb when developing them. This paper extends past research by providing an advanced, genetic algorithm based, multilayered structural optimization strategy that can assist both in the proper representation of traffic flow data with temporal and spatial characteristics as well as in the selection of the appropriate neural network structure. Further, it evaluates the performance of the developed network by applying it to both univariate and multivariate traffic flow data from an urban signalized arterial. The results show that the capabilities of a simple static neural network, with genetically optimized step size, momentum and number of hidden units, are very satisfactory when modeling both univariate and multivariate traffic data.

Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach

Short‐term traffic forecasting: Overview of objectives and methods

Transit operations are interrupted frequently by stochastic variations in traffic and ridership conditions that deteriorate schedule or headway adherence and thus lengthen passenger wait times. Providing passengers with accurate vehicle arrival information through advanced traveler information systems is vital to reducing wait time. Two artificial neural networks (ANNs), trained by link-based and stop-based data, are applied to predict transit arrival times. To improve prediction accuracy, both are integrated with an adaptive algorithm to adapt to the prediction error in real time. The bus arrival times predicted by the ANNs are assessed with the microscopic simulation model CORSIM, which has been calibrated and validated with real-world data collected from route number 39 of the New Jersey Transit Corporation. Results show that the enhanced ANNs outperform the ones without integration of the adaptive algorithm.

Dynamic Bus Arrival Time Prediction with Artificial Neural Networks

One of the major requirements of advanced traveler information systems (ATISs) is a mechanism to estimate link travel times. This article examines the use of an artificial neural network (ANN) for predicting freeway link travel times for one through five time periods into the future. Actual freeway link travel times from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system were used as a test bed. It was found that when predicting one or two time periods into the future, the ANN model that only considered previous travel times from the target link gave the best results. However, when predicting three to five time periods into the future, the ANN model that employed travel times from upstream and downstream links in addition to the target link gave superior results. The ANN model also gave the best overall results compared with existing link travel time forecasting techniques.

Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results, of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and real-time profile. It was found that the SNN gave the best overall results.

Spectral Basis Neural Networks for Real-Time Travel Time Forecasting

To capture the complex nature of intersection queue dynamics, this study has explored the use of neural network models with data from extensive simulation experiments. The proposed models, although lacking in mathematical elegance, are capable of providing the acceptable prediction accuracy (more than 90%) at 3 time-steps ahead. As each time-step is as short as 3 s, the resulting information on queue evolution is sufficiently detailed for both responsive signal control and intersection operations. To accommodate the differences in available surveillance systems, this study has also investigated the most suitable neural network structure for each proposed queue model with extensive exploratory analyses.

Predicting intersection queue with neural network models

To capture the complex nature of intersection queue dynamics, this study has explored the use of neural network models with data from extensive simulation experiments. The proposed models, although lacking in mathematical elegance, are capable of providing the acceptable prediction accuracy (more than 90 percent) at 3 time-steps ahead. As each time-step is as short as 3 s, the resulting information on queue evolution is sufficiently detailed for both responsive signal control and intersection operations. To accommodate the differences in available surveillance systems, this study has also investigated the most suitable neural network structure for each proposed queue model with extensive exploratory analyses.

Chih-Chiang Su

Papers

Predicting intersection queue with neural network models

Predicting intersection queue with neural network models