Construction of a Short-Term Traffic Flow Prediction Model Based on Improved LSTM and Performance Evaluation Across Multiple Time Granularities

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

Construction of traffic state vector using mutual information for short-term traffic flow prediction

Accurate selection of spatial-temporal features is key to the short-term traffic flow prediction model outputting higher quality results, which can effectively reduce the difficulty of constructing the prediction model. The spatial-temporal feature selection of most existing short-term traffic flow prediction models mainly relies on empirical knowledge methods and lacks interpretability. The proposed short-term traffic flow prediction network, named STFP-FGFS, utilizes a filter-genetic feature selection method to better explain the results of short-term traffic flow predictions. It consists of three stages: initial generation of temporal-spatial feature set, filtering, and feature optimization, as well as the predicted model. The initial spatial features are generated based on effective travel time, target time granularity, and vehicle type; that is, original spatial features are replaced by standardized spatial features. Four widely used feature selection methods for short-term traffic flow prediction are applied and compared, evaluating three experimental targets and four types of time granularity using four evaluation indexes. The results show that the STFP-FGFS proposed method has overall superior performance, good interpretability, and readability for selected spatial-temporal features.

In recent years, various types of Intelligent Transportation Systems(ITSs) have produced a large amount of structured traffic flow data. Using these structured data and deep learning models to predict urban short-term traffic flow is an important focus of current research. In order to solve the problem of low accuracy of Long-Short Term Memory(LSTM) model in urban traffic flow prediction, this paper proposes an LSTM model based on attention mechanism. The proposed model can learn the importance of each past value to the current value from the long sequence of traffic data at the past moment, which makes it possible to extract more valuable features. Constructed a dataset using the traffic data in the core section of Wuhan for experiments, and the performance of the improved model is compared with the original LSTM model. The results show that when the input data sequence increases from 16 to 64, the MAPE of our proposed model is reduced by 3.76%, while the MAPE of the LSTM model is reduced by 1.51%, which proves the effectiveness of our proposed method.

This study proposes a short-term traffic flow prediction model that combines community detection-based federated learning with a graph convolutional network (GCN) to alleviate the time-consuming training, higher communication costs, and data privacy risks of global GCNs as the amount of data increases. The federated community GCN (FCGCN) can achieve timely, accurate, and safe traffic state predictions in the era of big traffic data, which is critical for the efficient operation of intelligent transportation systems. The FCGCN prediction process has four steps: dividing the local subnetwork with community detection, local training based on the global parameters, uploading the local model parameters, and constructing a global model prediction based on the aggregated parameters. Numerical results on the PeMS04 and PeMS08 datasets show that the FCGCN outperforms four benchmark models, namely, the long short-term memory (LSTM), convolutional neural network (CNN), ChebNet, and graph attention network (GAT) models. The FCGCN prediction is closer to the real value, with nearly the same performance as the global model at a lower time cost, thus achieving accurate and secure short-term traffic flow predictions with three parameters: flow, speed, and occupancy.

Based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm and kernel online sequential extreme learning machine (KOSELM) algorithm, a new hybrid short-term traffic flow prediction model (ICEEMDAN-KOSELM-ARIMA) for signalized intersections is proposed according to the current and historical traffic flow data. First, traffic flow historical time series are decomposed by ICEEMDAN algorithm for the purpose of improving the prediction accuracy. Several intrinsic mode functions could be obtained by the decomposition process. Then, permutation entropy algorithm is employed to analyze the random properties of intrinsic mode function components. According to the different random properties of intrinsic mode functions, different prediction models can be built. On this basis, KOSELM prediction models are established for the intrinsic mode function components with big randomness. And auto-regressive integrated moving average (ARIMA) prediction models are built for the intrinsic mode function components with small randomness. Finally, an actual signalized intersection is selected to verify the effect and performance of the hybrid prediction model proposed in this article. Results show that compared with other models, the new proposed hybrid prediction model can effectively improve prediction accuracy, of which prediction errors are the lowest and fitting effect with actual values is the best.

Timely and accurate traffic flow information is essential for the realization of intelligent transportation systems (ITS). For the existing traffic flow prediction models, only the temporal features were extracted but the spatial features were neglects. A short-term traffic flow prediction model based on Pearson Correlation Coefficient (PCC) and Bi-directional Long Short-Term Memory Network (BiLSTM) model was proposed. The PCC was used to mine the spatial dependence of the road network, and the temporal features of traffic flow were mined using BiLSTM model. The extracted temporal and spatial features were combined to achieve short-term flow prediction. The experimental results show that the PCC-BiLSTM model has higher prediction accuracy and lower error than the prediction models considering only temporal features, which verifies the effectiveness of traffic flow prediction in consideration of both temporal and spatial characteristics.

Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of traffic data. This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework. In the proposed framework, the optimal input data time lags and amounts of spatial data are determined by a spatio-temporal feature selection algorithm (STFSA), and selected spatio-temporal traffic flow features are extracted from actual data and converted into a two-dimensional matrix. The CNN then learns these features to construct a predictive model. The effectiveness of the proposed method is evaluated by comparing the forecast results with actual traffic data. Other existing models are also evaluated for comparison. The proposed method outperforms baseline models in terms of accuracy.

Short-term traffic flow prediction plays an important role in intelligent transportation system. Numerous researchers have paid much attention to it in the past decades. However, the performance of traditional traffic flow prediction methods is not satisfactory, for those methods cannot describe the complicated nonlinearity and uncertainty of the traffic flow precisely. Neural networks were used to deal with the issues, but most of them failed to capture the deep features of traffic flow and be sensitive enough to the time-aware traffic flow data. In this paper, we propose a deep bi-directional long short-term memory (DBL) model by introducing long short-term memory (LSTM) recurrent neural network, residual connections, deeply hierarchical networks and bi-directional traffic flow. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data. Additionally, we introduce the DBL model, regression layer and dropout training method into a traffic flow prediction architecture. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS). The experiment results demonstrate that the proposed model for short-term traffic flow prediction obtains high accuracy and generalizes well compared with other models.

Based on historical data, a new short-term traffic flow prediction model (ICEEMDAN-MPE-PSO-DELM-ARIMA) for intersections is proposed. In order to improve prediction accuracy, ICEEMDAN decomposition algorithm is applied on traffic flow time series to obtain multiple Intrinsic Mode Function (IMF) components. Then PSO-MPE algorithm is introduced to obtain the multi-scale permutation entropy values of each IMF component, to judge the randomness. According to the randomness, different prediction models are established. Prediction models based on PSO-DELM algorithm are established for IMF components with big randomness. ARIMA prediction models are established for IMF components with small randomness. In order to obtain the final predicted traffic flow values, multiple prediction results are added together. Finally, two actual intersections are selected to verify the proposed model. Results show that compared with other models, the proposed model has the smallest prediction errors and the best fitting effect with the real values, which can effectively improve prediction accuracy.

Short-Term prediction aimed at the urban traffic congestion is an important goal for Intelligent Transport Systems (ITS). Short-term traffic prediction module tries to predict Traffic Congestion Index accurately and in approximately real-time. Up to date, there have been three basic methods in short-Term traffic prediction research namely the Kalman Filtering (KF) (Okutani and Stephanedes in Transport Res Part B 18B:1–11, 1984, [1]) method, Time Series models (Williams and Hoel in ASCE J Transport Eng 129:664–672, 2003, [2]) and Neural Network (NN) models (Smith and Demetsky in Transport Res Rec J Transport Res Board 1453:98–104, 1994, [3]). The Neural Networks based methods have proven to give good accuracy rate but they are time consuming in training. In our paper, we have implemented the new Neural Networks based algorithm called Extreme Learning Machine (ELM) (Huang and Siew in ICARCV, pp. 1029–1036, 2004, [4]) to design a real-time traffic index in the data of a real world city of Nanning in South China. Our experiment results show that ELM algorithm provides good generalization performance at extremely fast learning speed compared with other state-of-art algorithms. The algorithm obtains high accuracy in practical prediction application. In addition, quick training and good fitting results on our own large scale traffic data set proves ELM algorithm works well on large data sets.

Short term traffic flow prediction of expressway service area based on STL-OMS

With the urbanization, urban transportation has become a key factor restricting the development of a city. In a big city, it is important to improve the efficiency of urban transportation. The key to realize short-term traffic flow prediction is to learn its complex spatial correlation, temporal correlation and randomness of traffic flow. In this paper, the convolution neural network (CNN) is proposed to deal with spatial correlation among different regions, considering that the large urban areas leads to a relatively deep Network layer. First three gated recurrent unit (GRU) were used to deal with recent time dependence, daily period dependence and weekly period dependence. Considering that each historical period data to forecast the influence degree of the time period is different, three attention mechanism was taken into GRU. Second, a two-layer full connection network was applied to deal with the randomness of short-term flow combined with additional information such as weather data. Besides, the prediction model was established by combining these three modules. Furthermore, in order to verify the influence of spatial correlation on prediction model, an urban functional area identification model was introduced to identify different functional regions. Finally, the proposed model was validated based on the history of New York City taxi order data and reptiles for weather data. The experimental results show that the prediction precision of our model is obviously superior to the mainstream of the existing prediction methods.

Network traffic prediction is hot spot in recent years' research, which is of great significance in area such as congestion control, network management and diagnostic. Network traffic is non-linear, non-stationary, and uncertain, and its uncertainty increases rapidly when making short-term traffic flow prediction. After reviewing current network traffic prediction algorithms' merits and drawbacks based on Time-Series analysis, Artificial Neural Network here, a new network traffic prediction algorithms in short-term is proposed. The time interval when detecting that network data packet pass on certain section is treated as a stochastic process. In the ARCH (autoregressive conditional heteroskedasticity) framework, stochastic process is described by a marked point process that different point processes may generate different ACD (autoregressive conditional duration) model, then ACD model can be used to complete the description of time interval when network data packet passing. Based on this model, a particle filter is applied to non-stationary motion system for short-term network traffic prediction. At last, this algorithm is applied to real data for real-evidence analysis.

<p>Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As potential solutions to mitigate GHG emissions, several approaches have been proposed to better control traffic and manage transportation systems. The employment of Intelligent Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose of routing, this research aims to exploit the advancements in ICT by including connected and automated vehicles (CAVs) and sensing technology in an urban congested network.</p> <p>Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network. The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs was examined for various traffic conditions. E2ECAV was adopted for both the myopic and anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing application, predictive models were developed based on Long-Short Term Memory (LSTM), a deep learning approach, while considering a high level of spatial (link level) and temporal (one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters. The anticipatory routing strategy significantly outperformed the myopic routing strategy based on the [..] traffic and environmental perspectives. This research shows that ITS can help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an urban network. Furthermore, the proposed anticipatory routing framework can be applied in a real-time situation.</p>