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

Short-term traffic speed forecasting based on data recorded at irregular intervals

Recent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term traffic forecasting to make forecasts more reliable, efficient, and accurate. However, most of these methods can only deal with data recorded at regular time intervals, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments.published_or_final_versio

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme

Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.</p

Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model

A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems

Evaluation of Recent Computational Approaches in Short-Term Traffic Forecasting

Part 3: Computational Intelligence and Algorithms; International audience; Computational technologies under the domain of intelligent systems are expected to help the rapidly increasing traffic congestion problem in recent traffic management. Traffic management requires efficient and accurate forecasting models to assist real time traffic control systems. Researchers have proposed various computational approaches, especially in short-term traffic flow forecasting, in order to establish reliable traffic patterns models and generate timely prediction results. Forecasting models should have high accuracy and low computational time to be applied in intelligent traffic management. Therefore, this paper aims to evaluate recent computational modeling approaches utilized in short-term traffic flow forecasting. These approaches are evaluated by real-world data collected on the British freeway (M6) from 1st to 30th November in 2014. The results indicate that neural network model outperforms generalized additive model and autoregressive integrated moving average model on the accuracy of freeway traffic forecasting. Document type: Part of book or chapter of boo

TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan