Short-term Traffic Prediction Model Research Articles

A Novel Short-Term Traffic Prediction Model Based on SVD and ARIMA With Blockchain in Industrial Internet of Things

With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of non-linearity, non-stationary and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the Autoregressive Integrated Moving Average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real datasets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-art baselines.

Traffic Occupancy Prediction Using a Nonlinear Autoregressive Exogenous Neural Network

The main aim of the intelligent transportation systems is the ability to accurately predict traffic characteristics like traffic occupancy, speed, flow and accident based on historic and real time data collected by these systems in transportation networks. The main challenge of a huge quantity of traffic data collected automatically, stored and processed by these systems is the way of handling and extracting the required traffic data to formulate the prediction traffic characteristic model. In this research, the required traffic data of a specified road link in UK are extracted from the big raw data of the SCOOT system by designing C++ extractor program. In addition, short term traffic prediction models are created by using deep learning technique NARX neural network to find accurate and exact traffic occupancy. Three scenarios of time interval which are 10 minutes, 20 minutes and 30 minutes are considered for analyzing the prediction accuracy. The results showed that the prediction models for the 30 minutes interval scenario have very good accuracy in estimating the future traffic occupancy compared to another scenarios of time intervals. In addition, the testing and validation study showed that the prediction models for 30 minutes intervals for particular road link yield better accuracy than 10 minutes and 20 minutes intervals.

Deep Learning Methods in Short-Term Traffic Prediction: A Survey

Nowadays, traffic congestion has become a serious problem that plagues the development of many cities aroundthe world and the travel and life of urban residents. Compared with the costly and long implementation cyclemeasures such as the promotion of public transportation construction, vehicle restriction, road reconstruction, etc., traffic prediction is the lowest cost and best means to solve traffic congestion. Relevant departmentscan give early warnings on congested road sections based on the results of traffic prediction, rationalize thedistribution of police forces, and solve the traffic congestion problem. At the same time, due to the increasingreal-time requirements of current traffic prediction, short-term traffic prediction has become a subject of widespread concern and research. Currently, the most widely used model for short-term traffic prediction are deeplearning models. This survey studied the relevant literature on the use of deep learning models to solve shortterm traffic prediction problem in the top journals of transportation in recent years, summarized the currentcommonly used traffic datasets, the mainstream deep learning models and their applications in this field. Finally, the challenges and future development trends of deep learning models applied in this field are discussed.

Short-Term Traffic Flow Prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing

The paper aims to explore the performance of short-term traffic flow prediction of the 5G (5th Generation Mobile Communication Technology) Internet of Vehicles (IoV) based on edge computing (EC) for the smart city and to further improve the intelligence of the smart city. Aiming at the current emergency of traffic congestion and road congestion, the present work adds EC to the current vehicle network, and integrates a deep convolution random forest neural network (DCRFNN). Additionally, it implements a model for short-term traffic flow prediction of a 5G vehicle network based on EC and deep learning (DL), and analyzes its performance by simulation. The results reveal that the proposed algorithm has a lower average delay cost, and the average unloading utility is stable at approximately 70%. In the prediction performance analysis, the recognition accuracy of the proposed algorithm reaches 98.06%. It is at least 1.14% higher than that of the advanced convolution neural network (CNN) algorithm proposed by other scholars and achieves a faster convergence rate. Therefore, the constructed short-term traffic prediction model implemented has a high-quality prediction performance while also ensuring a better unloading performance. The results can provide an experimental basis for traffic flow prediction and intelligent development of the smart city.

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 min respectively.

A joint temporal-spatial ensemble model for short-term traffic prediction

In this paper, we address the problem of short-term traffic flow prediction since accurate prediction of short-term traffic flow facilitates timely traffic management and rapid response. We advocate deep machine learning approach and propose a novel ensemble model, named ALLSCP, that considers both temporal and spatial characteristics of traffic conditions. Specifically, we consider (1) short-, medium- and long-term temporal traffic evolution, (2) global and local spatial traffic patterns and (3) the correlation of temporal-spatial features in our predictions. We use real-world traffic data from two locations (i.e., Los Angeles and London) with frequent fluctuations (due to proneness to traffic accidents and/or congestion) to train and test our model. For each location, we consider road segments with and without junctions (i.e., linear vs intersection). We compare our model against well-known existing machine/deep learning prediction models. Our results indicate that our ALLSCP model consistently achieves the most accurate predictions (≈96% accuracy both on linear and intersection roadways) when compared against existing models in the literature. In addition, we conducted ablation experiments to further gain insights into the contributions of individual constituent models of our ensemble ALLSCP model. Our results indicate that ALLSCP achieves the best results and is also robust against emergent traffic situations.

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

Dynamic Modification Neural Network Model for Short-term Traffic Prediction

Short-term precise prediction of network traffic can effectively help operators reasonably allocate network resources, improve network service quality and scheduling efficiency. Our model proposed in this paper uses dynamic parameters to adjust the predicted value and refit the predicted sequence for short-term traffic prediction which is called Dynamic Modification Neural Network (DMNN) model. The architecture of our model consists of a prediction module, an adjustment module and a series re-fitting module. The prediction module learns time closeness of the traffic data before the predicted point. The adjustment module takes dynamic period characteristics of data into account and generates adjusted value by linear discrete dynamic parameters. The series re-fitting module refits predicted series from the first two modules with a new hybrid loss function. In this paper, we take different neural networks in the prediction module, such as Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Long Short-Term Memory (ConvLSTM) and Convolutional Bi-directional Long Short-Term Memory (ConvBiLSTM). We evaluate the performance of the proposed model by using two real-world datasets for short-term traffic flow prediction. Moreover, Experimental results show that the proposed model with dynamic modification has much higher accuracy than other models and decreases the prediction value error and time skew of the inflection points.

IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data. As one of the effective imputation approaches, generative adversarial networks (GANs) are implicit generative models that can be used for data imputation, which is formulated as an unsupervised learning problem. This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation. IGANI imputes data in two steps and maintains the invertibility of the generative imputer, which will be shown to be a sufficient condition for the convergence of the proposed GAN-based imputation. The performance of our proposed method is evaluated on (1) the imputation of traffic speed data collected in the city of Guangzhou in China, and the training of short-term traffic prediction models using imputed data, and (2) the imputation of multi-variable traffic data of highways in Portland-Vancouver metropolitan region which includes volume, occupancy, and speed with different missing rates for each of them. It is shown that our proposed algorithm mostly produces more accurate results compared to those of previous GAN-based imputation architectures.

Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks

Big data from toll stations provides reliable and accurate origin-destination (OD) pair information of expressway networks. However, although the short-term traffic prediction model based on big data is being constantly improved, the volatility and nonlinearity of peak traffic flow restricts the accuracy of the prediction results. Therefore, this research attempts to solve this problem through three contributions, firstly, proposing the use the Pauta criterion from statistics as the standard for defining the anomaly criteria of expressway traffic flows. Through comparison with the common local outlier factor (LOF) method, the rationality and advantages of the Pauta criterion were expounded. Secondly, adding week attributes to data, and splitting the data based on the similarity characteristics of traffic flow time series in order to improve the accuracy and efficiency of data input. Thirdly, by introducing empirical mode decomposition (EMD) to decompose the signal before autoregressive integrated moving average (ARIMA) model training is carried out. The first two contributions are for efficiency, the third is to deal with the volatility and nonlinearity of the abnormal peak training data. Finally, the model is analyzed, based on the expressway toll data of the Jiangsu Province. The results show that the EMD-ARIMA model has more advantages than the ARIMA model when dealing with fluctuating data.

On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis

Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence.

Short-term traffic prediction model for urban transportation using structure pattern and regression: an Indian context

The paper presents a model for predicting short term traffic flow using structure pattern and regression. The relationship between traffic flow on the current section and upstream stations are used for predicting the short-term traffic flow and is exposed by the traffic flow structure pattern. The structure pattern of the traffic flow can be pinched from freeway toll data based on which, a new prediction model has been proposed. Locally weighted learning is used to predict the next instance’s traffic flow of current section by considering the traffic flow of current section and the next station entrance flow. This learning model deploys both linear and non-linear models to fit the nearby points and then applies those values to forecast the values of the query points. Experimental results illustrates that the short term prediction method based on structure pattern and regression is an effective approach for traffic flow prediction, this is applicable especially during the abnormal traffic states. The proposed system outperforms the conventional systems in terms of accuracy and proves to facilitate in conserving energy. The proposed work benefits the travelers by rerouting and saves fuel consumption.

Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns

Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction.

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.

Short-Term Traffic Prediction for Edge Computing-Enhanced Autonomous and Connected Cars

Autonomous and connected cars (ACCs), together with edge computing (EC), have been recognized as a promising solution to achieve green intelligent transportation for smart cities. This paper aims to address short-term traffic prediction, a fundamental enabler for the success of ACC applications, under the European Telecommunications Standards Institute multi-access EC (MEC) architecture that exhibits constraints different from conventional cloud computing. First, a data-centric experiment platform is designed and implemented to facilitate traffic prediction algorithm development. This paper further proposes a novel short-term traffic prediction model that integrates a traffic light model and a vehicle velocity model, considering limited computing resources of MEC servers. We note that the effects of traffic lights are complicated and have not been rigorously examined in most, if not all, of the related work. This work models the queueing time when a driver arrives at a road intersection and faces a red light. Moreover, to forecast the vehicle velocity, we propose a novel low-complexity semiparametric prediction model considering periodic features and spatial/temporal correlations of dynamic road events. The experiment results demonstrate that our vehicle-velocity prediction model achieves almost equivalent accuracy to the well-known Long Short-Term Memory Neural Network model, requiring much lower computational complexity. Our experiment results also confirm that since our integrated model pays attention to the traffic-light effects and the individual driver behavior, it can more effectively capture the real-time changes of traffic conditions.

A Spatial-Temporal Hybrid Model for Short-Term Traffic Prediction

Accurate and timely short-term traffic prediction is important for Intelligent Transportation System (ITS) to solve the traffic problem. This paper presents a hybrid model called SpAE-LSTM. This model considers the temporal and spatial features of traffic flow and it consists of sparse autoencoder and long short-term memory (LSTM) network based on memory units. Sparse autoencoder extracts the spatial features within the spatial-temporal matrix via full connected layers. It cooperates with the LSTM network to capture the spatial-temporal features of traffic flow evolution and make prediction. To validate the performance of the SpAE-LSTM, we implement it on the real-world traffic data from Qingyang District of Chengdu city, China, and compare it with advanced traffic prediction models, such as models only based on LSTM or SAE. The results demonstrate that the proposed model reduces the mean absolute percent error by more than 15%. The robustness of the proposed model is also validated and the mean absolute percent error on more than 86% road segments is below 20%. This research provides strong evidence suggesting that the proposed SpAE-LSTM effectively captures the spatial-temporal features of the traffic flow and achieves promising results.

Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks

Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.

Predictive Control for Distributed Smart Street Light Network

With the advent of smart city that embedded with smart technology, namely, smart streetlight, in urban development, the quality of living for citizens has been vastly improved. TALiSMaN is one of the promising smart streetlight schemes to date, however, it possesses certain limitation that led to network congestion and packet dropped during peak road traffic periods. Traffic prediction is vital in network management, especially for real-time decision-making and latency-sensitive application. With that in mind, this paper analyses three real-time short-term traffic prediction models, specifically simple moving average, exponential moving average and weighted moving average to be embedded onto TALiSMaN, that aim to ease network congestion. Additionally, the paper proposes traffic categorisation and packet propagation control mechanism that uses historical road traffic data to manage the network from overload. In this paper, we evaluate the performance of these models with TALiSMaN in simulated environment and compare them with TALiSMaN without traffic prediction model. Overall, weighted moving average showed promising results in reducing the packet dropped while capable of maintaining the usefulness of the streetlight when compared to TALiSMaN scheme, especially during rush hour.

Local traffic patterns extraction with network-wide consistency in large urban networks

Abstract With the increasing availability of traffic data in large urban areas, there is an opportunity to infer more sophisticated traffic patterns and trends which till now has been difficult to obtain and understand. The inferred traffic patterns can then serve as input into short-term traffic prediction models or to predict traffic demand in the network for deployment of traffic management policies. However, the network-wide consistency, the interaction between variables and the difficulty during forecasting in choosing a pattern at early AM peak hour require robust, but flexible enough pattern extraction method. A wide range of researchers tackle some of these points, but it is difficult to find the one that fulfills all of them. Thus, this paper presents traffic flow pattern extraction methodology to identify daily traffic flow profiles consistent among all the detectors as a patterns based on two-phase methodology consisting of decision tree algorithm, Pathmox, and 2-step iterative clustering. The first phase uses qualitative variables (i.e., holidays, large events, weather, day-of-week) to capture meaningful and robust patterns in a tree-based configuration. The second phase consists of two steps that aim to reduce still high variability in some patterns non-attributable to the qualitative factors already exploited in the first phase. The advantages of this approach are (1) its capacity to break down variability in patterns due to both known and unknown factors; (2) it does not rely on specific network settings and (3) network-wide scale consistent patterns are identified. The traffic pattern extraction method is evaluated with real traffic flow data collected in period of one year on a motorway network M4 and M7 in Sydney, Australia. Results show that the proposed method extracts more identifiable patterns and more efficiently captures trends in the data with almost non-overlapping conditional variability bands compared to spatial-clustering approach based on k-means.

A short-term traffic prediction model in the vehicular cyber–physical systems

The advances in Cyber–Physical Systems (CPS), vehicular networks and Intelligent Transportation System (ITS) boost a growing interest in the design, development and deployment of Vehicular Cyber–Physical Systems (VCPS) for some emerging applications. As one of the key application for realizing traffic guidance, the traffic prediction could provide better route planning for people and accuracy decision basis for traffic managements. In practice, short-term traffic information has the characteristics of real-time, incompleteness, non-linearity and non-stationary, and few proposed methods could successfully implement this forecasting. In this paper, we proposed a fuzzy Markov prediction model which can estimate the short-term traffic conditions in VCPS in urban environment. First, we selected a real-time GPS dataset in the Shanghai Transport Grid Project as our data source for traffic prediction and pre-process this raw dataset to make it consistent with the practical case. Next, we combine the fuzzy theory with Markov progress in the prediction model, and use the continuous three-step average method to reduce the errors caused by the one-step transition. Finally, we choose the speed and traffic flow to express the metrics of traffic state and use the fuzzy reasoning rules to give out the determined traffic state. The simulation results show that our proposed model can be precisely used for the short-term traffic prediction in urban environment.