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"Traffic speed"
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Short‐Term High-Speed Traffic Flow Prediction Based on ARIMA-GARCH-M Model
The traditional traffic flow prediction model acquired the poor characteristics of the traffic flow time series, which led to the low prediction accuracy. Therefore, the short-term high-speed traffic flow prediction based on arima-garch-m model was proposed. According to the urban traffic flow theory, ARIMA model and GARCH model are combined to obtain the corresponding fluctuation characteristics and realize the prediction of traffic flow. The experimental results show that the NRMSE and MAPE of the model in this paper are only 3.13 % and 8.76 %, respectively, with good prediction accuracy and better stability and accuracy than the other two models, proving that the model has good performance and can meet the needs of practical application.
Journal Article
Link traffic speed forecasting using convolutional attention-based gated recurrent unit
Traffic speed forecasting becomes a thriving research area in modern transportation systems. The intensification of travel flow volumes due to fast urbanization, vehicle path planning, demands on efficient transport planning policies, commercial objectives, and many other factors contribute to fuel this revival dynamics. Moreover, predicting vehicle speed is of paramount importance in congestion management to help transport authorities as well as network users to handle congestion over road infrastructures or to provide a global overview of daily passenger flow. In this work, we propose a novel approach to forecast the future traffic speed of the road segments (links) based on traffic flow data without the need for previous traffic speed as input. To do this, we first pre-process floating car data of several million vehicles for multiples network links spread all over the Greater Paris area from 2016 to 2017. A convolutional attention-based recurrent neural network is used to capture the local-temporal features of traffic data to unveil the underlying pattern between the traffic flow and speed sequences for all links over the network. While the convolutional layer captures the local dependency, the attention layer learns patterns from weights of near-term traffic flow. It extracts the inherent interdependency of traffic speed due to many factors such as incidents, rush hour, land use, to cite a few, in non-free-flow conditions. The efficiency of the proposed model is evaluated using several metrics in traffic speed forecasting excluding additional data such as historical traffic speed and network graph contrary to cutting-edge work in the field. This is a substantial property since it allows avoiding the cumbersomeness in data mixing and facilitating resource availability. The proposed model is also evaluated on several roads located in the Greater Paris area separately on weekdays and weekends.
Journal Article
Vehicle routing problem with time windows and carbon emissions: a case study in logistics distribution
Logistics and transportation industry is not only a major energy consumer, but also a major carbon emitter. Developing green logistics is the only way for the sustainable development of the logistics industry. One of the main factors of environmental pollution is caused by carbon emissions in the process of vehicle transportation, and carbon emissions of vehicle transportation are closely related to routing, road conditions, vehicle speed, and speed fluctuations. The low-carbon vehicle routing problem with high granularity time-dependent speeds, speed fluctuations, road conditions, and time windows is proposed and formally described. In order to finely evaluate the effects of vehicle speed and speed fluctuations on carbon emissions, a graph convolutional network (GCN) is used to predict the high granularity time-dependent traffic speeds. To solve this complicated low-carbon vehicle routing problem, a hybrid genetic algorithm with adaptive variable neighborhood search is proposed to obtain vehicle routing with low carbon emissions. Finally, this method is validated using a case study with the logistics and traffic data in Jingzhou, China, and also the results show the effectiveness of this proposed method.
Journal Article
Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features
With the more and more in-depth research on intelligent transportation, many scholars have proposed their models for accurate prediction of traffic. In this paper, we analyze the advantages and disadvantages of the existing models and propose our own model. In our model, the temporal and spatial factors are taken into account. Gate Recurrent Unit (GRU) and Gated Linear Units (GLU) are used to learn the short-term temporal features of traffic data, and Graph Convolutional Network (GCN) is used to learn the spatial features of traffic data. In order to fully learn short-term feature changes, a multi time step perception layer is proposed. A new network GCGRU is proposed to learn the long-term features of traffic data. As the sensor will be affected by urban canyon, weather, and other factors, there will be missing value and noise in the collected data. We created a short-term trend based missing value filling up algorithm to fill in missing values and use Singular Spectrum Analysis (SSA) algorithm to eliminate noise of training data set. In order to reduce the process of adjusting parameters manually in the model training process, we propose k-block search method based on fuzzy extreme points. Finally, the model is compared with the existing traffic forecasting models, and the analysis results show that our model has advantages in many indicators.
Journal Article
Multistep traffic speed prediction from multiple time-scale spatiotemporal features using graph attention network
Traffic forecasting using deep learning represents a crucial aspect of intelligent transportation systems, carrying substantial implications for congestion reduction and efficient route planning. Despite its significance, accurately predicting traffic states remains a challenge. Existing methodologies focus on capturing the temporal trends of traffic states and the spatial dependencies between roads to enhance prediction accuracy. However, two noteworthy limitations persist in these approaches: (1) Many models neglect the interaction between spatiotemporal features across varying time spans, hindering their ability to utilize traffic state information effectively for predicting future conditions. (2) Genuine correlations between roads are time-varying, making it inadequate to rely on static graphs or static pre-trained node embeddings to model dynamic correlations between roads. To address these challenges, we propose the Multiple Time-Scale Graph Attention Network (MTS-GATN), which comprises two key modules: the Multiple Time-Scale Spatiotemporal Features Extraction Module and the Feature Augmentation Module. The first module involves stacking multiple spatiotemporal extraction layers to discern traffic state information at different time scales. In the second module, we employ dynamic spatial semantic embedding for feature augmentation, providing nodes with dynamic representations over time. Subsequently, we leverage a multi-head spatiotemporal attention mechanism to comprehensively consider location information and real-time semantic data, facilitating the interaction of traffic state information across multiple time scales. Experimental results on two distinct traffic datasets validate the superior performance of MTS-GATN in medium-term and long-term forecasting scenarios.
Journal Article
Bi-directional Long Short Term Memory Neural Network for Short-Term Traffic Speed Prediction Using Gravitational Search Algorithm
Traffic speed prediction has implications for urban planning, congestion reduction, and intelligent control systems. To maintain a uniform traffic speed and to avoid issues related to traffic, an accurate traffic speed forecast can help in supplying significant information. The capacity to forecast short-term traffic speed is a fundamental part of both Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). To achieve better accuracy in predicting short-term traffic speed, we introduced a GSA-Bi-LSTM model by optimizing the Bi-directional Long Short-Term Memory (Bi-LSTM) network prediction framework with Gravitational Search Algorithm (GSA) due to its features of fast convergence, great reliability and significant global search ability of parameters. The utilization of the GSA optimization technique is employed to optimize the hyperparameters of the Bi-LSTM model. By making use of the bidirectional properties of Bi-LSTM layers, the model’s architecture aims to enhance prediction accuracy and effectively capture the intricate patterns present in the input data. From the analysis of the experimental results, it becomes evident that the convenience provided by our proposed GSA-Bi-LSTM model surpasses that of conventional models in terms of evaluation metrics. Additionally, it is also noted that GSA has superior optimization capabilities than Particle Swarm Optimization (PSO) in terms of optimizing the Bi-LSTM approach for traffic speed forecasting.
Journal Article
Deep Learning for Traffic Prediction and Trend Deviation Identification: A Case Study in Hong Kong
This paper introduces a robust methodology for predicting traffic volume and speed on major strategic routes in Hong Kong by leveraging data from data.gov.hk and utilizing deep learning models. The approach offers predictions from 6 min to 1 h, considering detector reliability. By extracting hidden deep features from historical detector data to establish detector profiles and grouping detectors into clusters based on profile similarities, the method employs a CNN-LSTM prediction model for each cluster. The study demonstrates the model’s resilience to detector failures, with tests conducted across failure rates from 1% to 20%, highlighting its ability to maintain accurate predictions despite random failures. In scenarios without failed detectors, the method achieves favorable performance metrics: MAE, RMSE, and MAPE for traffic volume prediction over the next 6 min stand at 5.17 vehicles/6 min, 7.64 vehicles/6 min, and 14.07%, respectively, while for traffic speed prediction, the values are 3.70 km/h, 6.32 km/h, and 6.33%. Considering a failure rate of approximately 6% in the Hong Kong dataset, in simulated scenarios with 6% failures, the model maintains its predictive accuracy, with average MAE, RMSE, and MAPE for traffic volume prediction at 5.24 vehicles/6 min, 7.81 vehicles/6 min, and 14.21%, and for traffic speed prediction at 3.87 km/h, 6.55 km/h, and 6.68%. However, the limitation of the proposed method is its potential to underperform when predicting rare or unseen scenarios, indicating the need for future research to incorporate additional data sources and methods to enhance predictive performance.
Journal Article
AMGCN: adaptive multigraph convolutional networks for traffic speed forecasting
Traffic speed forecasting is a crucial aspect of traffic management that requires an accurate multi spatiotemporal time series forecasting technique. Previous studies typically employ graph neural network (GNN)-based methods for this task, but they are limited by their focus on spatial dependence based on real geographic distance in road networks. These structures are often inadequate for accurately describing spatial dependencies in the real world. Recently, multigraph neural networks (MGNNs) have shown considerable promise for improving forecasting performance by modelling graph structures from different spatial relationships. However, these kinds of methods do not account for complex relationships between aspects and latent dependence that cannot be known beforehand. To address these shortcomings, we propose a novel traffic speed forecasting method called adaptive multigraph convolutional networks (AMGCN), where we first introduce five predefined graphs based on spatial distance, accessibility, pattern similarity, distribution similarity and KL divergence. We fuse these graphs into a complex prior graph using a method based on spatial attention and graph relation attention. In this process, the spatial dependence in the road network is modelled comprehensively from multiple perspectives. In addition, we introduce the adaptive graph to calculate the similarity between learnable node embeddings to assist the forecasting. In this process, spatial dependencies that still cannot be captured by predefined graphs can be obtained by the way of data-driven. We utilize a mix-hop graph convolution with a residual connection to capture spatial dependencies in prior graphs and adaptive graphs. Time dependencies are also captured through causal convolution based on equidistance downsampling to prevent overfitting and redundancy in capturing spatiotemporal interactions. Extensive experiments on four real-world datasets demonstrate that our proposed method achieves superior performance compared to other baselines and effectively captures the spatiotemporal dependencies of the road network. Source codes are available at https://github.com/hfimmortal/AMGCN.
Journal Article
Comparative Analysis of Anpr and TIRTL Systems Using Artificial Neural Networks for Traffic Speed Management
Transportation networks are struggling with increased traffic due to mixed flows and the unregulated growth of private vehicles. Over-speeding and congestion are critical issues for urban planners. Effective speed management and enforcement are essential to mitigate excessive speed, which is a major cause of traffic accidents. This study aims to develop efficient traffic speed management measures by evaluating the performance of Automatic Number Plate Recognition (ANPR) and The Infra-Red Traffic Logger (TIRTL) in data collection and the detection of excessive speeding. The results show ANPR detected only 51% of the vehicle classes, while TIRTL detected 96% of them. A maximum speed reduction of 20 km/h was observed in the vehicles, with an average reduction of 8 km/h. The ANN model developed can help urban planners devise new speed management techniques by accurately estimating their effectiveness in an urban setting.
Journal Article