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Zhang Q, Chang W, Yin C, Xiao P, Li K, Tan M. Attention-Based Spatial-Temporal Convolution Gated Recurrent Unit for Traffic Flow Forecasting. ENTROPY (BASEL, SWITZERLAND) 2023; 25:938. [PMID: 37372282 DOI: 10.3390/e25060938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial-temporal relationships. Although the existing methods have researched spatial-temporal relationships, they neglect the long periodic aspects of traffic flow data, and thus cannot attain a satisfactory result. In this paper, we propose a novel model Attention-Based Spatial-Temporal Convolution Gated Recurrent Unit (ASTCG) to solve the traffic flow forecasting problem. ASTCG has two core components: the multi-input module and the STA-ConvGru module. Based on the cyclical nature of traffic flow data, the data input to the multi-input module are divided into three parts, near-neighbor data, daily-periodic data, and weekly-periodic data, thus enabling the model to better capture the time dependence. The STA-ConvGru module, formed by CNN, GRU, and attention mechanism, can capture both temporal and spatial dependencies of traffic flow. We evaluate our proposed model using real-world datasets and experiments show that the ASTCG model outperforms the state-of-the-art model.
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Affiliation(s)
- Qingyong Zhang
- School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
| | - Wanfeng Chang
- School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
| | - Conghui Yin
- School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
| | - Peng Xiao
- School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
| | - Kelei Li
- School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
| | - Meifang Tan
- School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
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Ou J, Huang X, Zhou Y, Zhou Z, Nie Q. Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1392. [PMID: 37420412 DOI: 10.3390/e24101392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 07/09/2023]
Abstract
Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient λ, the shift factor b, and the rotation factor c. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations.
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Affiliation(s)
- Jishun Ou
- College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Xiangmei Huang
- College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
| | - Yang Zhou
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77840, USA
| | - Zhigang Zhou
- College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
| | - Qinghui Nie
- College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
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Huang L, Liu XX, Huang SQ, Wang CD, Tu W, Xie JM, Tang S, Xie W. Temporal Hierarchical Graph Attention Network for Traffic Prediction. ACM T INTEL SYST TEC 2021. [DOI: 10.1145/3446430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
As a critical task in intelligent traffic systems, traffic prediction has received a large amount of attention in the past few decades. The early efforts mainly model traffic prediction as the time-series mining problem, in which the spatial dependence has been largely ignored. As the rapid development of deep learning, some attempts have been made in modeling traffic prediction as the spatio-temporal data mining problem in a road network, in which deep learning techniques can be adopted for modeling the spatial and temporal dependencies simultaneously. Despite the success, the spatial and temporal dependencies are only modeled in a regionless network without considering the underlying hierarchical regional structure of the spatial nodes, which is an important structure naturally existing in the real-world road network. Apart from the challenge of modeling the spatial and temporal dependencies like the existing studies, the extra challenge caused by considering the hierarchical regional structure of the road network lies in simultaneously modeling the spatial and temporal dependencies between nodes and regions and the spatial and temporal dependencies between regions. To this end, this article proposes a new Temporal Hierarchical Graph Attention Network (TH-GAT). The main idea lies in augmenting the original road network into a region-augmented network, in which the hierarchical regional structure can be modeled. Based on the region-augmented network, the region-aware spatial dependence model and the region-aware temporal dependence model can be constructed, which are two main components of the proposed TH-GAT model. In addition, in the region-aware spatial dependence model, the graph attention network is adopted, in which the importance of a node to another node, of a node to a region, of a region to a node, and of a region to another region, can be captured automatically by means of the attention coefficients. Extensive experiments are conducted on two real-world traffic datasets, and the results have confirmed the superiority of the proposed TH-GAT model.
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Affiliation(s)
- Ling Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, P. R. China
| | - Xing-Xing Liu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China
| | - Shu-Qiang Huang
- College of Science & Engineering, Jinan University, Guangzhou, P. R. China
| | - Chang-Dong Wang
- School of Computer Science and Engineering, Sun Yat-sen University, P. R. China and Guangdong Province Key Laboratory of Computational Science, P. R. China and Key Laboratory of Machine Intelligence and Advanced Computing, P. R. China
| | - Wei Tu
- School of Architecture & Urban Planning, Shenzhen University, Shenzhen, P. R. China
| | - Jia-Meng Xie
- Traffic Administration Bureau of Guangdong Province, Guangzhou, P. R. China
| | - Shuai Tang
- Nanjing Fenghuotiandi Communication Technology Co., Ltd., Nanjing, P. R. China
| | - Wendi Xie
- Guangzhou Canwin Computer Technology Co., Ltd., Guangzhou, P. R. China
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Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05115-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Data-driven analysis and forecasting of highway traffic dynamics. Nat Commun 2020; 11:2090. [PMID: 32350245 PMCID: PMC7190853 DOI: 10.1038/s41467-020-15582-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 03/04/2020] [Indexed: 11/08/2022] Open
Abstract
The unpredictable elements involved in a vehicular traffic system, like human interaction and weather, lead to a very complicated, high-dimensional, nonlinear dynamical system. Therefore, it is difficult to develop a mathematical or artificial intelligence model that describes the time evolution of traffic systems. All the while, the ever-increasing demands on transportation systems has left traffic agencies in dire need of a robust method for analyzing and forecasting traffic. Here we demonstrate how the Koopman mode decomposition can offer a model-free, data-driven approach for analyzing and forecasting traffic dynamics. By obtaining a decomposition of data sets collected by the Federal Highway Administration and the California Department of Transportation, we are able to reconstruct observed data, distinguish any growing or decaying patterns, and obtain a hierarchy of previously identified and never before identified spatiotemporal patterns. Furthermore, it is demonstrated how this methodology can be utilized to forecast highway network conditions. The demands on transportation systems continue to grow while the methods for analyzing and forecasting traffic conditions remain limited. Here the authors show a parameter-independent approach for an accurate description, identification and forecasting of spatio-temporal traffic patterns directly from data.
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Xian X, Ye H, Wang X, Liu K. Spatiotemporal Modeling and Real-Time Prediction of Origin-Destination Traffic Demand. Technometrics 2020. [DOI: 10.1080/00401706.2019.1704887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Xiaochen Xian
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL
| | - Honghan Ye
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
| | - Xin Wang
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
- Grainger Institute for Engineering, University of Wisconsin-Madison, Madison, WI
| | - Kaibo Liu
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
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Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach. SUSTAINABILITY 2020. [DOI: 10.3390/su12020646] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Short-term traffic speed prediction is vital for proactive traffic control, and is one of the integral components of an intelligent transportation system (ITS). Accurate prediction of short-term travel speed has numerous applications for traffic monitoring, route planning, as well as helping to relieve traffic congestion. Previous studies have attempted to approach this problem using statistical and conventional artificial intelligence (AI) methods without accounting for influence of data collection time-horizons. However, statistical methods have received widespread criticism concerning prediction accuracy performance, while traditional AI approaches have too shallow architecture to capture non-linear stochastics variations in traffic flow. Hence, this study aims to explore prediction of short-term traffic speed at multiple time-ahead intervals using data collected from loop detectors. A fast forest quantile regression (FFQR) via hyperparameters optimization was introduced for predicting short-term traffic speed prediction. FFQR is an ensemble machine learning model that combines several regression trees to improve speed prediction accuracy. The accuracy of short-term traffic speed prediction was compared using the FFQR model at different data collection time-horizons. Prediction results demonstrated the adequacy and robustness of the proposed approach under different scenarios. It was concluded that prediction performance of FFQR was significantly enhanced and robust, particularly at time intervals larger than 5 min. The findings also revealed that speed prediction error (in terms of quantiles loss) ranged between 0.58 and 1.18.
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Song Y, Noyce D. Effects of transit signal priority on traffic safety: Interrupted time series analysis of Portland, Oregon, implementations. ACCIDENT; ANALYSIS AND PREVENTION 2019; 123:291-302. [PMID: 30557754 DOI: 10.1016/j.aap.2018.12.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 11/02/2018] [Accepted: 12/03/2018] [Indexed: 05/15/2023]
Abstract
Transit signal priority (TSP) has been implemented to transit systems in many cities of the United States. In evaluating TSP systems, more attention has been given to its operational effects than to its safety effects. Existing studies assessing TSP's safety effects reported mixed results, indicating that the safety effects of TSP vary in different contexts. In this study, TSP implementations in Portland, Oregon, were assessed using interrupted time series analysis (ITSA) on month-to-month changes in number of crashes from January 1995 to December 2010. Single-group and controlled ITSA were conducted for all crashes, property-damage-only crashes, fatal and injury crashes, pedestrian-involved crashes, and bike-involved crashes. Evaluation of the post-intervention period (2003-2010) showed a reduction in all crashes on street sections with TSP (-4.5%), comparing with the counterfactual estimations based on the control group data. The reduction in property-damage-only crashes (-10.0%) contributed the most to the overall reduction. Fatal and injury crashes leveled out after TSP implementation but did not change significantly comparing with the control group. Pedestrian and bike-involved crashes were found to increase in the post-intervention period with TSP, comparing with the control group. Potential reasons to these TSP effects on traffic safety were discussed.
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Affiliation(s)
- Yu Song
- Department of Civil and Environmental Engineering, Traffic Operations and Safety Laboratory, University of Wisconsin-Madison, 1415 Engineering Dr. Rm. 1249A, Madison, WI, 53706, United States.
| | - David Noyce
- Department of Civil and Environmental Engineering, Traffic Operations and Safety Laboratory, University of Wisconsin-Madison, 1415 Engineering Dr. Rm. 2205, Madison, WI, 53706, United States.
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Žliobaitė I, Khokhlov M. Optimal estimates for short horizon travel time prediction in urban areas. INTELL DATA ANAL 2016. [DOI: 10.3233/ida-150292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Indrė Žliobaitė
- Helsinki Institute for Information Technology HIIT, Finland
- Department of Computer Science, Aalto University, Finland
- Department of Geosciences and Geography, University of Helsinki, Finland
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Predicting and visualizing traffic congestion in the presence of planned special events. JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2014. [DOI: 10.1016/j.jvlc.2014.10.028] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Srinivasan D, Wai Chan C, Balaji P. Computational intelligence-based congestion prediction for a dynamic urban street network. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.01.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ishak S, Alecsandru C. Optimizing Traffic Prediction Performance of Neural Networks under Various Topological, Input, and Traffic Condition Settings. ACTA ACUST UNITED AC 2004. [DOI: 10.1061/(asce)0733-947x(2004)130:4(452)] [Citation(s) in RCA: 63] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- S. Ishak
- Assistant Professor, Dept. of Civil and Environmental Engineering, Louisiana State Univ., CEBA Building, Baton Rouge, LA 70803
| | - C. Alecsandru
- Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Louisiana State Univ., CEBA Building, Baton Rouge, LA 70803
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