Short-term Traffic Flow Forecasting Research Articles

An adaptive composite time series forecasting model for short-term traffic flow

Short-term traffic flow forecasting is a hot issue in the field of intelligent transportation. The research field of traffic forecasting has evolved greatly in past decades. With the rapid development of deep learning and neural networks, a series of effective methods have been proposed to address the short-term traffic flow forecasting problem, which makes it possible to examine and forecast traffic situations more accurately than ever. Different from linear based methods, deep learning based methods achieve traffic flow forecasting by exploring the complex nonlinear relationships in traffic flow. Most existing methods always use a single framework for feature extraction and forecasting only. These approaches treat all traffic flow equally and consider them contain same attribute. However, the traffic flow from different time spots or roads may contain distinct attributes information (such as congested and uncongested). A simple single framework usually ignore the different attributes embedded in different distributions of data. This would decrease the accuracy of traffic forecasting. To tackle these issues, we propose an adaptive composite framework, named Long-Short-Combination (LSC). In the proposed method, two data forecasting modules(L and S) are designed for short-term traffic flow with different attributes respectively. Furthermore, we also integrate an attribute forecasting module (C) to forecast the traffic attributes for each time point in future time series. The proposed framework has been assessed on real-world datasets. The experimental results demonstrate that the proposed model has excellent forecasting performance.

Short-term traffic flow prediction based on hybrid decomposition optimization and deep extreme learning machine

Precise and robust short-term traffic flow forecasting is vital to reduce carbon emissions and alleviate traffic congestion. However, developing a traffic flow prediction model that is both precise and robust is exceedingly difficult, owing to the nonlinear and non-stationary characteristics of traffic flow. This study proposes a novel hybrid model (CPQDELM) in pursuit of this objective. This model attains satisfactory prediction performance by integrating the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), dung beetle optimization algorithm based on quantum inspiration and multistrategy improvement(QMDBO), and deep extreme learning machine (DELM). The proposed model employs CEEMDAN and PE for data decomposition and reorganization and then uses the QMDBO algorithm to optimize the DELM parameters. Experiments are conducted in this study to validate the efficacy of the QMDBO algorithm and the CPQDELM hybrid model, respectively. First, the effectiveness of the improvement strategy proposed in this study is verified by comparing the proposed QMDBO algorithm with six other algorithms through experiments on the CEC2021 test function. Secondly, using two real-world datasets, comparing the CPQDELM hybrid model and twelve baseline models. The results indicate that the model proposed in this study performs better than the current methodologies. The RMSE, MAE, and MAPE of the model proposed in this study are all decreased by 27.24 %, 25.97 %, and 33.56 %, respectively, when compared to the conventional DELM, using the S321k51 dataset of mountain scenic highway as an example.

GSA-KELM-KF: A Hybrid Model for Short-Term Traffic Flow Forecasting

Short-term traffic flow forecasting, an essential enabler for intelligent transportation systems, is a fundamental and challenging task for dramatically changing traffic flow over time. In this paper, we present a gravitational search optimized kernel extreme learning machine, named GSA-KELM, to avoid manually traversing all possible parameters to improve the potential performance. Furthermore, with the interference of heavy-tailed impulse noise, the performance of KELM may be seriously deteriorated. Based on the Kalman filter that cleverly combines observed data and estimated data to perform the closed-loop management of errors and limit the errors within a certain range, we propose a combined model, termed GSA-KELM-KF. The experimental results of two real-world datasets demonstrate that GSA-KELM-KF outperforms the state-of-the-art parametric and non-parametric models.

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction

Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods.

Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model

The increase of road traffic in large cities during the last years has produced that long and short-term traffic flow forecasting is a critical need for the authorities. The availability of good traffic flow prediction methods is a must to make informed decisions concerning (punctual) traffic congestions. Previous work has shown that the accuracy of these methods decreases if we consider urban traffic and long-term predictions. In this paper we present a hybrid model, combining a Convolutional Neural Network and a Bidirectional Long–Short-Term Memory network, and apply it to long-term traffic flow prediction in urban routes. This model combines the capability of CNN to extract hidden valuable features from the input model and the capability of BiLSTM to understand the temporal context. In order to assess the usefulness of our model, we considered four streets of the city of Madrid with different characteristics and compared the results of our proposed model with the ones obtained by eight widely used baseline models. The results show that our hybrid model outperforms the baseline models with respect to three metrics commonly used in regression: mean absolute error, root mean squared error and accuracy.

Formula omitted]-LSTM: An error distribution free deep learning for short-term traffic flow forecasting

Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term memory (LSTM) network is considered qualified to capture the long-term temporal dependencies in traffic flow. However, the training of LSTM networks is often guided by the mean square error (MSE) criterion. Such criterion depends on a strong assumption that the errors between the traffic flow and its predictions are Gaussian independent identically distributed. In this regard, the forecasting performance is seriously deteriorated by non-Gaussian noises inside the traffic flow sequences. To address this issue, we relax the assumption of the prediction errors to arbitrary distribution by a negative guided mixed correntropy criterion. Then, we formulate a robust loss function by the negative guided mixed correntropy criterion. We subsequently equip the loss function in an LSTM network, termed Δfree-LSTM, for short-term traffic flow forecasting. Extensive experiments on four benchmark datasets demonstrate that the Δfree-LSTM network outperforms the traditional parametric and nonparametric models, as well as state-of-the-art LSTM family models. The source code is available athttps://github.com/541764418/Delta-free-LSTM.

Выбор и оценка ARIMA-модели для прогнозирования пассажиропотока

Short-term forecasting of transport traffic flow is one of the main areas of research in the field of intelligent transport systems. In recent years, a large number of studies on traffic forecasting have been conducted. However, it is difficult to pick the most appropriate traffic forecasting method for a particular application. Naive methods are widely used due to their low computational costs and simplicity of implementation. But the accuracy of these methods is usually quite low. Clustering methods average out the traffic variables within a certain group of days based on similar traffic models. Macroscopic simulation models take into account only such global variables of the road network as traffic density, average speeds and flows. The advantage of nonparametric models lies in the fact that they enable us to model complex, dynamic and nonlinear processes occurring in road traffic. One of the forecasting options for passenger traffic is the forecasting on the basis of time series processing, which allows obtaining rather reliable results. Currently, considerable attention is given to the spatiotemporal forecasting. ARIMA processes are flexible linear models which can accurately describe a wide range of various time series behavior, including medium-term peaks and troughs. Despite the fact that these basic models are quite simple to describe, their evaluation requires extensive computer calculations. This paper investigates the problem of seasonality modeling when studying the time distribution of passenger traffic flow in road transport. The basic information for predicting passenger traffic flow is the data obtained during the daily parsing of the online service for searching traveling car companions (edem.rf (едем.рф)) for 12 months (February 2022 - January 2023).

A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting

Credible and accurate traffic flow forecasting is critical for deploying intelligent traffic management systems. Nevertheless, it remains challenging to develop a robust and efficient forecasting model due to the nonlinear characteristics and inherent stochastic traffic flow. Aiming at the nonlinear relationship in the traffic flow for different scenarios, we proposed a two-stage hybrid extreme learning model for short-term traffic flow forecasting. In the first stage, the particle swarm optimization algorithm is employed for determining the initial population distribution of the gravitational search algorithm to improve the efficiency of the global optimal value search. In the second stage, the results of the previous stage, rather than the network structure parameters randomly generated by the extreme learning machine, are used to train the hybrid forecasting model in a data-driven fashion. We evaluated the trained model on four real-world benchmark datasets from highways A1, A2, A4, and A8 connecting the Amsterdam ring road. The RMSEs of the proposed model are 288.03, 204.09, 220.52, and 163.92, respectively, and the MAPEs of the proposed model are 11.53%, 10.16%, 11.67%, and 12.02%, respectively. Experimental results demonstrate the superior performance of our proposed model.

Short-Term Traffic Flow Prediction with Structure-Optimized Deep Belief Network

This paper proposes a structure-optimized deep belief network method for short-term traffic flow forecast, which is used to solve the problems of too simple training data in deep learning short-term traffic flow forecast and random selection of model structure construction parameters. We constructed a deep belief network short-term traffic flow forecast model that can simultaneously train three types of traffic data related to the predicted node traffic volume, enhance the spatiotemporal correlation of predictions, and overcome the shortcomings of too single training data. At the same time, we optimize the short-term traffic flow prediction model structure of the deep belief network; and use the T-PSO algorithm to optimize the hidden layer structure parameters of the prediction model. It can avoid the decrease in the practicability of the model caused by the random selection of the model structure construction parameters. The experimental results show that the method of structure-optimized deep belief network is feasible and effective, and the prediction accuracy is better than the classic deep learning prediction model.

Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenging task due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task, namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways’ main lanes. The time resolution of the dataset was set to 15 min, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-the-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyses for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during midday periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

Prediction model for short‐term traffic flow based on a K‐means‐gated recurrent unit combination

Short-term forecasting of traffic flow is an indispensable part of easing traffic pressure. Considering that different traffic flow patterns will affect the short-term traffic flow prediction results, a combined method based on the K-means clustering algorithm and gated recurrent unit (GRU) is proposed to build a short-term traffic flow prediction model to overcome the above problems. The K-means algorithm is used to cluster historical traffic flow data to establish different traffic flow pattern libraries. The K-nearest neighbour (KNN) classification algorithm is used to determine the historical traffic flow pattern most similar to the traffic flow change trend of the date to be predicted. All historical traffic flow data in this category is used training samples to make targeted predictions. The traffic flow data of performance measurement system (PeMS) in California, USA is used to verify the performance of the proposed model. Compared with the GRU network, stacked auto encoders (SAEs), random forest (RF), and support vector machine regression (SVR), the results show that the proposed combination model K-means-GRU considers the diversity of traffic flow patterns and improves the prediction accuracy, it can better solve the short-term traffic flow prediction problem.

Short, Mid and Long Term Traffic Flow Forecasting and Data Visualization Tool

İstanbul içerisinde her geçen gün artan insan ve araç sayısı beraberinde ciddi bir trafik yoğunluğu getirmektedir. Oluşan bu araç yoğunluğunun giderilmesi veya kontrol edilebilmesi için eldeki verilerin iyi yorumlanması gerekmektedir. İstanbul il sınırları içerisinde ölçümleri yapılan tüm hız verileri İstanbul Büyükşehir Belediyesi tarafından kayıt altına alınmaktadır. Fakat bu verilerin sayılar üzerinden yorumlanması oldukça güçtür. Görselleştirme, sayısal verilerden anlam çıkarılması ve yorum yapılabilmesi için sıkça başvurulan bir yöntemdir. Bu çalışmada trafik yoğunluğunu analiz ederek görselleştiren, kısa, orta ve uzun vadede hız tahmini yapan bir araç geliştirilmiştir. Analiz kısmında İstanbul sınırları içerisinde birçok noktadaki sensörden alınan hız verileri belli bölge, zaman ve lokasyona göre özelleştirilmiş ve kullanıcıya talepleri doğrultusunda görsel bir içerik sunulmuştur. İçerik hazırlanırken ilgili bilgiler harita üzerinde işaretlenmiş veya sayısal grafiklerden yararlanılmıştır. Trafik tahmini yapabilmek için regresyon ve temel istatistiksel veriye dayalı üç farklı yöntem denenmiş ve elde edilen sonuçlar karşılaştırılmıştır. Geliştirilen sistem günün farklı zaman dilimleri için çıkarılan regresyon modellerini kullanarak %21.8 Ortalama Mutlak Yüzde Hatası ve 8.5 Ortalama Mutlak Hata ile 1 hafta sonraya kadar trafik akışı tahmini yapabilmektedir.

GSA‐ELM: A hybrid learning model for short‐term traffic flow forecasting

Accurate and timely short-term traffic flow forecasting is an essential component for intelligent traffic management systems. However, developing an effective and robust forecasting model is challenging due to the inherent randomness and nonlinear characteristic of the traffic flow. In this paper, a gravitational search algorithm optimized extreme learning machine, termed GSA-ELM, is proposed to unlock the potential performance for short-term traffic flow forecasting. The extreme learning machine avoids the tedious backpropagation by analytically determining the optimal solution. The gravitational search algorithm globally searches the optimal parameters for the extreme learning machine. The forecasting performance of the GSA-ELM is evaluated on four benchmark datasets by comparing several state-of-the-art models. The four benchmark datasets are real-world traffic flow data from highways A1, A2, A4, A8 near the ring road of Amsterdam. The MAPEs of the GSA-ELM model are 11.69 % , 10.25 % , 11.72 % and 12.05 % on four benchmark datasets, respectively, whereas the RMSEs of the GSA-ELM model are 287.89, 203.04, 221.39 and 163.24 , respectively. The experimental results demonstrate the superior performance of the proposed model.

Attention meets long short-term memory: A deep learning network for traffic flow forecasting

Accurate forecasting of future traffic flow has a wide range of applications, which is a fundamental component of intelligent transportation systems. However, timely and accurate traffic forecasting remains an open challenge due to the high nonlinearity and volatility of traffic flow data. Canonical long short-term memory (LSTM) networks are easily drawn to focus on min-to-min fluctuations rather than the long term dependencies of the traffic flow evolution. To address this issue, we propose to introduce an attention mechanism to the long short-term memory network for short-term traffic flow forecasting. The attention mechanism helps the network model to assign different weights to different inputs, focus on critical and important information, and make accurate predictions. Extensive experiments on four benchmark data sets show that the LSTM network equipped with an attention mechanism has superior performance compared with commonly used and state-of-the-art models.

Performance analysis of LSTM model with multi-step ahead strategies for a short-term traffic flow prediction

In this study, the effect of direct and recursive multi-step forecasting strategies on the short-term traffic flow forecast performance of the Long Short-Term Memory (LSTM) model is investigated. To increase the reliability of the results, analyses are carried out with various traffic flow data sets. In addition, databases are clustered using the k-means++ algorithm to reduce the number of experiments. Analyses are performed for different time periods. Thus, the contribution of strategies to LSTM was examined in detail. The results of the recursive based strategy performances are not satisfactory. However, different versions of the direct strategy performed better at different time periods. This research makes an important contribution to clarifying the compatibility of LSTM and forecasting strategies. Thus, more efficient traffic flow prediction models will be developed and systems such as Intelligent Transportation System (ITS) will work more efficiently. A practical implication for researchers that forecasting strategies should be selected based on time periods.

A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting

Accurate short-term traffic flow modeling is an essential prerequisite to analyze and control traffic flow. Canonical data-driven methods are a large account of parameters that may be underfitted with limited training samples, yet they cannot adaptively boost their understanding of the spatiotemporal dependencies of the traffic flow. The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. To address these issues, we propose an easy-to-implement yet effective boosting model based on extreme gradient boosting and enhance it by wavelet denoising for short-term traffic flow forecasting. The discrete wavelet denoising is employed to preprocess the noisy traffic flow data. Then, the denoised training datasets are reconstructed to train the extreme gradient boosting model. These two components are integrated seamlessly in a unified framework, and the whole framework can retain the features in the data as much as possible. Our model can precisely capture the hidden spatial dependency in the data. Extensive experiments are conducted on four benchmark datasets compared with frequently used models. The results demonstrate that the proposed model can precisely capture the hidden spatial dependency of the traffic flow data and achieve superior performance.

Short-term Traffic Flow Forecast Based on DE-RBF Fussion Model

The road traffic system is a time-varying, complex nonlinear system. Real-time and accurate road short-term traffic flow prediction is the key to realizing the traffic flow guidance system. In order to improve the prediction accuracy of short-term traffic flow, this paper proposes an algorithm based on the fusion model of differential evolution algorithm (DE) and radial basis function (RBF). This method takes the fitness function as the measurement standard, and uses the DE algorithm to optimize the RBF parameters to obtain the optimal short-term traffic flow prediction value. Through MATLAB simulation experiments, a relatively accurate prediction of the short-term traffic flow of the DE-RBF fusion model is realized. The mean square error (MSE) and the average absolute error percentage of actual and predicted values (MAPE) analysis index are introduced as the evaluation index of the prediction model. After comparing with the two prediction network models of radial basis function (RBF) and wavelet function (WNN), the results show that the DE-RBF fusion model proposed in this paper is effective and feasible for short-term traffic flow prediction.

Explanatory prediction of traffic congestion propagation mode: A self-attention based approach

Short-term traffic flow forecasting, an important component of intelligent transportation systems (ITS), is a challenging research direction as forecasting itself is affected by a series of complex factors. As more and more attention is paid to the data itself, deep learning methods have attained mainstream popularity for accomplishing traffic flow prediction tasks. In recent years, the attention mechanism has been widely used in various fields thanks to its excellent result interpretation ability and its capability to improve the performance of neural network models. In terms of time series data prediction, LSTM has demonstrated its powerful time feature extraction capability. Because of its ability to efficiently and quickly extract spatial–temporal features, CNN is often used in combination with LSTM and attention mechanisms to obtain accurate traffic flow prediction forecast results. In this paper, we propose a short-term traffic flow prediction model based on self-attention, and test the performance of the model experimentally with real data. The model can achieve the best prediction results compared with other classical models. In addition, the temporal and spatial features extracted by the model have certain physical characteristics making results easier to interpret.

A hybrid approach for short-term traffic flow forecasting based on similarity identification

Short-term traffic flow forecasting is a key component of intelligent transportation system, yet difficult to be forecasted reliably, and accurately. A novel hybrid forecasting model is proposed by combining three predictors, namely, the autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN) and support vector regression (SVR). First, it is assumed that all previous intervals can have influence on the predicted interval and then the entropy-based gray relation analysis method is applied to analyze the correlation and determine the length of time constrain window. Second, an improved Euclidean distance is employed to identify the similarity. Furthermore, the rank-exponent method is utilized to rank the results according to the similarity and fuse the predicted values of the predictors. Finally, a numerical experiment is implemented, which indicates that the performance of forecasting results is superior to the conventional ones.