Short-term Traffic Volume Prediction Research Articles

Examining the possibility of short-term prediction of traffic volume in smart city control systems with the use of regression models

This article deals with issues related to the optimization of traffic management in modern cities, the so-called Smart City. In particular, the article presents the process of evolution of the traffic flow prediction model at a selected crossroads in a selected city in Poland - the city of Rzeszów. Rzeszow is an example of a smart city equipped with an extensive system of real-time data collection and processing from multiple road points in the city. The research was aimed at a detailed analysis of the feasibility and degree of fit of different variants of the regression model: linear, polynomial, trigonometric, polynomial-trigonometric, and regression-based Random Forest algorithm. Several studies were carried out evaluating different generations of models, in particular, an analysis was carried out based on which the superiority of the trigonometric model was demonstrated. This model had the best fit and the lowest error rate, which could be a good conclusion for widespread use and implementation in Smart City supervisory systems.

Multidirectional short-term traffic volume prediction based on spatiotemporal networks

Multidirectional short-term traffic volume prediction based on spatiotemporal networks

The short-term prediction of daily traffic volume for rural roads using shallow and deep learning networks: ANN and LSTM

The short-term prediction of daily traffic volume for rural roads using shallow and deep learning networks: ANN and LSTM

Hybrid CNN and LSTM Model (HCLM) for Short-Term Traffic Volume Prediction

Hybrid CNN and LSTM Model (HCLM) for Short-Term Traffic Volume Prediction

Short-Time Traffic Forecasting in Tourist Service Areas Based on a CNN and GRU Neural Network

The continuous development of highway construction projects has prompted the function of service areas to be improved day by day. A traditional service area gradually transforms from a single traffic service mode to a complex traffic service mode. The continuous enrichment and perfection of the service area’s function makes the surrounding highway network more attractive, which leads to a sudden increase in highway traffic volume in a short period of time. In order to better improve the service level of a tourist service area by predicting the short-term traffic volume of the toll station around the tourist service area, this paper proposes a model combining a convolutional neural network and a gated recurrent unit (CNN plus GRU) to solve the problem of short-term traffic volume prediction. The data from 17 toll stations of the Yu’an Expressway in Guizhou Province were selected for the experiment to test the prediction effect of the CNN plus GRU-based model. The experimental results show that the prediction accuracy, the MAE and RMSE, are 1.8101 and 2.7021, respectively, for the toll stations with lower traffic volumes, and 3.820 and 5.172, respectively, for the toll stations with higher traffic volumes. Compared with a single model, the model’s prediction accuracy is improved, to different degrees. Therefore, the use of a convolutional neural network operation is better when the total traffic volume is low, considering the algorithm’s time and error. When using the combined convolutional neural network and gated recurrent unit model and when the total traffic volume is high, the algorithm error is significantly reduced and the prediction results are better.

Data Imputation for Detected Traffic Volume of Freeway Using Regression of Multilayer Perceptron

Traffic volume data are the important part of research and application of intelligent transportation systems (ITS). However, data loss often happens due to various factors in the real world, which may cause large deviations in prediction or bad accuracy of optimizations. Imputation is a valid way to handle missing values. A multilayer perceptron-multivariate imputation of chain equation (MLP-MICE) regression imputation method optimized by the limit-memory-BFGS algorithm is proposed, considering the temporal and spatial characteristics of traffic volume. Also, 32 groups of simulated imputation experiments based on the detected traffic volume of road sections in the Guangdong freeway system are conducted, which take the scenarios of continuous missing and jumped missing into account. The results of the experiments show that the MLP-MICE can optimize the imputation performance in the missing value of traffic volume with the MAPE of imputation results from 6.38% to 30%. Meanwhile, the proposed model has higher imputation accuracy for the traffic volume data with a lower degree of mutation. Lastly, the performance of the proposed model of imputation in short-term traffic volume prediction is discussed using the support vector machine. The results of it show that the MAPE of prediction under the proposed model is much lower than all-zero imputation. Therefore, the proposed model in this study is positive on improving the accuracy of traffic volume prediction and intelligent traffic control and management.

Spatiotemporal attention mechanism-based multistep traffic volume prediction model for highway toll stations

As the fundamental part of other Intelligent Transportation Systems (ITS) applications, short-term traffic volume prediction plays an important role in various intelligent transportation tasks, such as traffic management, traffic signal control and route planning. Although Neural-network-based traffic prediction methods can produce good results, most of the models can’t be explained in an intuitive way. In this paper, we not only proposed a model that increase the short-term prediction accuracy of the traffic volume, but also improved the interpretability of the model by analyzing the internal attention score learnt by the model. we propose a spatiotemporal attention mechanism-based multistep traffic volume prediction model (SAMM). Inside the model, an LSTM-based Encoder-Decoder network with a hybrid attention mechanism is introduced, which consists of spatial attention and temporal attention. In the first level, the local and global spatial attention mechanisms considering the micro traffic evolution and macro pattern similarity, respectively, are applied to capture and amplify the features from the highly correlated entrance stations. In the second level, a temporal attention mechanism is employed to amplify the features from the time steps captured as contributing more to the future exit volume. Considering the time-dependent characteristics and the continuity of the recent evolutionary traffic volume trend, the timestamp features and historical exit volume series of target stations are included as the external inputs. An experiment is conducted using data from the highway toll collection system of Guangdong Province, China. By extracting and analyzing the weights of the spatial and temporal attention layers, the contributions of the intermediate parameters are revealed and explained with knowledge acquired by historical statistics. The results show that the proposed model outperforms the state-of-the-art model by 29.51% in terms of MSE, 13.93% in terms of MAE, and 5.69% in terms of MAPE. The effectiveness of the Encoder-Decoder framework and the attention mechanism are also verified.

The Short-Term Prediction of Daily Traffic Volume for Rural Roads Using Shallow and Deep Learning Networks: Ann and Lstm

The Short-Term Prediction of Daily Traffic Volume for Rural Roads Using Shallow and Deep Learning Networks: Ann and Lstm

Short-term Traffic Volume Prediction for the Merging Roads by Artificial Neural Network

Ana arterlerinde trafik sıkışıklığı yaşanan şehirlerin ikincil derecedeki yollarında trafik hacim tahminlerinin yapılması kritik konulardan biridir. Ekonomiden farklı mühendislik uygulamalarına kadar birçok alanda (satış tahminleri, endüstriyel süreç kontrolü, müşteri araştırmaları, veri doğrulama, risk yönetimi, hedef pazarlama ve inşaat mühendisliği gibi) kullanılan yapay sinir ağı (YSA) bu konuda umut verici bir çözüm olabilir. Geçmiş trafik verilerine dayanarak daha yüksek bir tahmin doğruluğu sağlayan YSA, son 30 yıldır ulaştırma mühendisliği alanındaki uygulamalarda çok popüler hale geldi. Bu makaledeki temel amaç, İstanbul'un Boğaz Köprülerinden birine katılan bir bağlantı yolunun kısa dönem trafik hacmini YSA'nın üç farklı uygulamasıyla tahmin etmektir. Bunlar İleri Besleme Geri Yayılımı (FFBP), Genelleştirilmiş Regresyon Sinir Ağı (GRNN) ve Radyal Tabanlı Fonksiyon (RBF) idi. Daha sonra elde edilen sonuçlar birbirleriyle ve Çoklu Doğrusal Regresyon (MLR) yönteminin sonuçları ile karşılaştırıldı.

Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction

Accurate traffic volume prediction can help traffic managers to control traffic well,and can also provide convenient travel routes for passengers. In order to better describe the non-stationary, complex and spatial correlation of traffic volume, the traffic prediction model is proposed based on wavelet denoising and phase space reconstruction (WD-PSR-GA-BP). Pure traffic volume is firstly preprocessed by wavelet denoising method. Subsequently, the prediction model is built by BP neural network, which is optimized by genetic algorithm. In addition, one-dimensional traffic volume is mapped to high-dimensional space by the theory of phase space reconstruction. The inputsof the prediction model are obtained by the reconstructed traffic volume datasets. In order to verify the effectiveness of the proposed model, two groups of datasets and different models are studied in the experiment of Section 4. The experimental results show that the proposed model is superior to all other competitors in terms of MAPE, RMSE, and MAE.

An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning

Short-term traffic volume prediction, which can assist road users in choosing appropriate routes and reducing travel time cost, is a significant topic of intelligent transportation system. To overcome the error magnification phenomena of traditional combination methods and to improve prediction performance, this paper proposes an improved Bayesian combination model with deep learning (IBCM-DL) for traffic flow prediction. First, an IBCM framework is established based on the new BCM framework proposed by Wang. Then, correlation analysis is used to analyze the relevance between the historical traffic flow and the traffic flow within the current interval. Three sub-predictors including the gated recurrent unit neural network (GRUNN), autoregressive integrated moving average (ARIMA), and radial basis function neural network (RBFNN) are incorporated into the IBCM framework to take advantage of each method. The real-world traffic volume data captured by microwave sensors located on the expressways of Beijing was used to validate the proposed model in multiple scenarios. The overall results illustrate that the IBCM-DL model outperforms the other state-of-the-art methods in terms of accuracy and stability.

A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems

The prediction of short-term volatile traffic becomes increasingly critical for efficient traffic engineering in intelligent transportation systems. Accurate forecast results can assist in traffic management and pedestrian route selection, which will help alleviate the huge congestion problem in the system. This paper presents a novel hybrid DTMGP model to accurately forecast the volume of passenger flows multi-step ahead with the comprehensive consideration of factors from temporal, origin-destination spatial, and frequency and self-similarity perspectives. We first apply discrete wavelet transform to decompose the traffic volume series into an appropriation component and several detailed components. Then we propose a more efficient tracking model to forecast the appropriation component and a novel Gaussian process model to forecast the detailed components. The forecasting performance is evaluated with real-time passenger flow data in Chongqing, China. Simulation results demonstrate that our hybrid model can achieve on average 20%–50% accuracy improvement, especially during rush hours.

Short-term traffic volume prediction by ensemble learning in concept drifting environments

Because of the rapid changes in traffic conditions caused by various circumstances, such as road construction and traffic jams, the distribution of the traffic volume data changes over time. The performances of traditional traffic volume prediction methods, with fixed model types and parameter settings, suffer from gradual degradation during these concept drift processes. In this paper, a novel incremental regression framework under the concept drifting environment is proposed, with ensemble learning as the major solution for updating the distribution representation. First, we transform the regression problem of traffic volume forecasting into a binary classification problem. Second, loss functions for incremental and ensemble learning are constructed based on this transformation. Finally, the incremental learning of the regression function is formulated as stepwise updating of the decision hyperplane. The experimental results show that our method is more stable and accurate than the existing incremental and ensemble regression methods.

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the model parameters, but rather focus on generating the prediction intervals (PIs) that can minimize a multi-objective function which considers two criteria, reliability and interval sharpness. The improved PSO-ELM models are developed for an hourly border crossing traffic dataset and compared to: (1) the original PSO-ELMs; (2) two state of the art models proposed by Zhang et al. (2014) and Guo et al. (2014) separately; and (3) the traditional ARMA and Kalman filter models. The results show that the improved PSO-ELM can always keep the mean PI length the lowest, and guarantee that the PI coverage probability is higher than the corresponding PI nominal confidence, regardless of the confidence level assumed. The study also probes the reasons that led to a few points being not covered by the PIs of PSO-ELMs. Finally, the study proposes a comprehensive optimization framework to make staffing plans for border crossing authority based on bounds of PIs and point predictions. The results show that for holidays, the staffing plans based on PI upper bounds generated much lower total system costs, and that those plans derived from PI upper bounds of the improved PSO-ELM models, are capable of producing the lowest average waiting times at the border. For a weekday or a typical Monday, the workforce plans based on point predictions from Zhang et al. (2014) and Guo et al. (2014) models generated the smallest system costs with low border crossing delays. Moreover, for both holiday and normal Monday scenarios, if the border crossing authority lacked the required staff to implement the plans based on PI upper bounds or point predictions, the staffing plans based on PI lower bounds from the improved PSO-ELMs performed the best, with an acceptable level of service and total system costs close to the point prediction plans.

A k Nearest Neighbor based Local Linear Wavelet Neural Network Model for On-line Short-term Traffic Volume Prediction

This paper presents a forecasting method called k nearest neighbor based local linear wavelet neural network (kNN-LLWNN) for the on-line, short-term prediction of five-minute traffic volumes at westbound of Interstate 64 in Hampton Road in Virginia. The method is based on combining k nearest neighbor (k-NN), with local linear wavelet neural network (LLWNN). The idea is to apply k-NN method to form the training dataset for LLWNN instead of taking the whole historical dataset for training. The proposed model is compared with k-NN, LLWNN and support vector regression (SVR) separately from prediction accuracy and running time two aspects. Experiments are conducted to decide the most appropriate parameters for the four models for the verification dataset. For the test dataset, the study's findings appear to confirm the hypothesis that, kNN-LLWNN performs comparable with LLWNN and SVR, and its running time is much lower than LLWNN and SVR because of the introduction of k-NN.

Short-Term Traffic Volume Prediction for Sustainable Transportation in an Urban Area

AbstractAccurate short-term traffic volume prediction is essential for the realization of sustainable transportation as providing traffic information is widely known as an effective way to alleviate congestion. In practice, short-term traffic predictions require a relatively low computation cost to perform calculations in a timely manner and should be tolerant to noise. Traffic measurements of variable quality also arise from sensor failures and missing data. There is no optimal prediction model so far fulfilling these challenges. This paper proposes a so-called absorbing Markov chain (AMC) model that utilizes historical traffic database in a single time series to carry out predictions. This model can predict the short-term traffic volume of road links and determine the rate in which traffic eases once congestion has occurred. This paper uses two sets of measured traffic volume data collected from the city of Enschede, Netherlands, for the training and testing of the model, respectively. The main advantag...

Short-Term Forecasting of Traffic Volume

Although several methods for short-term forecasting of traffic volume have recently been developed, the literature lacks studies that focus on how to choose the appropriate prediction method on the basis of the statistical characteristics of the data set. This study first diagnosed the predictability of four traffic volume data sets on the basis of various statistical measures, including (a) complexity analysis methods, such as the delay time and embedding dimension method and the approximate entropy method; (b) nonlinearity analysis methods, such as the time reversibility of surrogate data; and (c) long-range dependency analysis techniques, such as the Hurst exponent. After the data sets were diagnosed, three models for short-term prediction of traffic volume were applied: (a) seasonal autoregressive integrated moving average (SARIMA), (b) k nearest neighbor (k-NN), and (c) support vector regression (SVR). The results from the statistical data diagnosis methods were then correlated to the performance results of the three prediction methods on the four data sets to determine the means for choosing the appropriate prediction method. The results revealed that SVR was more suitable for nonlinear data sets, while SARIMA and k-NN were more appropriate for linear data sets. The data diagnosis results were also used to devise a selection process for the parameters of the prediction models, such as the length of the training data set for SARIMA and SVR, the average number of nearest neighbors for k-NN, and the input vector length for k-NN and SVR.

Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM) is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN). The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

Forecasting of Short-Term Freeway Volume with v-Support Vector Machines

Predictions for short-term traffic volume provide important inputs for traveler information and traffic management. Traffic volumes in the near future are often estimated based on historical volumes. Because of the complicated nonlinear relationship between historical and future traffic volume data, many previous studies used neural networks to predict short-term traffic volumes. In this research, a v-support vector machine (v-SVM) model, which has the particular strength of overcoming local minima and overfitting common to neural network models, is proposed for short-term traffic volume prediction. The v-SVM model is compared with a widely used multilayer feed-forward neural network (MLFNN) model using four data sets collected from three interstate freeways. Testing results show that for both one-step and two-step forecasting, the v-SVM model outperforms the MLFNN model for all data sets in terms of mean absolute percentage error and root-mean-square error. Key issues in applying both models are also discussed in this article.

Short-Term Prediction of Traffic Volume in Urban Arterials

This paper attempts to develop time-series models for forecasting traffic volume in urban arterials. The Box-Jenkins approach is used to estimate the time-series models. A 1-min data set representing traffic volume on five major urban arterials were available to estimate time-series models. The Box-Jenkins autoregressive integrated moving average (ARIMA) model of order (0, 1, 1) turned out to be the most adequate model in reproducing all original time series. The developed model is easy to understand and implement. Further, the model is computationally tractable, and only requires the storage of the last forecasted error and current traffic observation.