Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction
School of Information Systems, Singapore Management University（新加坡管理大学信息系统学院）；Smeal College of Business, Pennsylvania State University（宾夕法尼亚州立大学斯米尔商学院）
Truc Viet Le, Richard J. Oentaryo, Siyuan Liu, Hoong Chuin Lau
发表信息：
IEEE Transactions on Big Data, vol. 3, no. 2, pp. 194-207, 2017
Traffic speed is a key indicator for the efficiency of an urban transportation system. Accurate modeling of the spatiotemporally varying traffic speed thus plays a crucial role in urban planning and development. This paper addresses the problem of efficient fine-grained traffic speed prediction using big traffic data obtained from static sensors. Gaussian processes (GPs) have been previously used to model various traffic phenomena, including flow and speed. However, GPs do not scale with big traffic data due to their cubic time complexity. In this work, we address their efficiency issues by proposing local GPs to learn from and make predictions for correlated subsets of data. The main idea is to quickly group speed variables in both spatial and temporal dimensions into a finite number of clusters, so that future and unobserved traffic speed queries can be heuristically mapped to one of such clusters. A local GP corresponding to that cluster can then be trained on the fly to make predictions in real-time. We call this method localization. We use non-negative matrix factorization for localization and propose simple heuristics for cluster mapping. We additionally leverage on the expressiveness of GP kernel functions to model road network topology and incorporate side information. Extensive experiments using real-world traffic data collected in the two U.S. cities of Pittsburgh and Washington, D.C., show that our proposed local GPs significantly improve both runtime performances and prediction accuracies compared to the baseline global and local GPs.
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently offer limited insights. Extracting fine-grained patterns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile traffic super-resolution techni…
Benedict College（本尼迪克特学院）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Dynamic behavior of traffic adversely affect the performance of the prediction models in intelligent transportation applications. This study applies Gaussian processes (GPs) to traffic speed prediction. Such predictions can be used by various transportation applications, such as real-time route guidance, ramp metering, congestion pricing and special events traffic management. One-step predictions with various aggregation levels (1 to 60-minute) are tested for performance of the generated models. Univariate and multivariate GPs are compared with several other linear, nonlinear time series, and …
Technical University of Denmark (DTU)（丹麦技术大学）；Massachusetts Institute of Technology (MIT)（麻省理工学院）
Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severe…
College of Artificial Intelligence and Automation, Hohai University, China（人工智能与自动化学院，河海大学，中国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）；College of Information Science and Engineering, Hohai University, China（信息科学与工程学院，河海大学，中国）
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；Peng Cheng Laboratory（鹏城实验室）；University of Molise（莫利塞大学）；IIT-CNR（意大利国家研究委员会信息与电信研究所）；Chinese Academy of Sciences（中国科学院）
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与技术学院）；Tongyi Lab, Alibaba Group（阿里巴巴集团通义实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou University（广州大学）
A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control
School of Artificial Intelligence, Beijing University of Posts（人工智能学院，北京邮电大学）；Beijing Big Data Center（北京大数据中心）；Eastern Institute of Technology（东部技术研究所）
Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction
School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；Purple Mountain Laboratories（紫金山实验室）；China Telecom Corporation Limited Technology Innovation Center（中国电信股份有限公司技术创新中心）
Nanyang Technological University（南洋理工大学）；Ocean University of China（中国海洋大学）
Substantial efforts have been devoted to the investigation of spatiotemporal correlations for improving traffic speed prediction accuracy. However, existing works typically model the correlations based solely on the observed traffic state (e.g. traffic speed) without due consideration that different correlation measurements of the traffic data could exhibit a diverse set of patterns under different traffic situations. In addition, the existing works assume that all road segments can employ the same sampling frequency of traffic states, which is impractical. In this paper, we propose new measur…
Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation
Fudan University（复旦大学）；Zhuhai Fudan Innovation Research Institute（珠海复旦创新研究院）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Guangzhou University（广州大学）；Shanghai Jiao Tong University（上海交通大学）
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
University of Macau（澳门大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Arizona State University（亚利桑那州立大学）
Traffic light detection is essential for self-driving cars to navigate safely in urban areas. Publicly available traffic light datasets are inadequate for the development of algorithms for detecting distant traffic lights that provide important navigation information. We introduce a novel benchmark traffic light dataset captured using a synchronized pair of narrow-angle and wide-angle cameras covering urban and semi-urban roads. We provide 1032 images for training and 813 synchronized image pairs for testing. Additionally, we provide synchronized video pairs for qualitative analysis. The datas…
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology（麻省理工学院土木与环境工程系）；Laboratory for Information & Decision Systems, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）；Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology（首尔科学技术大学机械与汽车工程系）
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）
Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have…
Nanyang Technological University（南洋理工大学）；Agency for Science, Technology and Research（新加坡科技研究局）；Institute for Infocomm Research（资讯通信研究院）
Accurate long series forecasting of traffic information is critical for the development of intelligent traffic systems. We may benefit from the rapid growth of neural network analysis technology to better understand the underlying functioning patterns of traffic networks as a result of this progress. Due to the fact that traffic data and facility utilization circumstances are sequentially dependent on past and present situations, several related neural network techniques based on temporal dependency extraction models have been developed to solve the problem. The complicated topological road st…
Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow. A way to represent traffic data is in the form of temporally changing heatmaps visualizing attributes of traffic, such as speed and volume. In recent works, U-Net models have shown SOTA performance on traffic forecasting from heatmaps. We propose to combine the U-Net architecture with graph layers which improves spatial generalization to unseen road networks compared to a Vanilla U-Net. In particular…
Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild
Graduate School of Information Science and Technology, Hokkaido University（信息科学与技术研究生学校，北海道大学）；Education and Research Center for Mathematical and Data Science, Hokkaido University（数学与数据科学教育与研究中心，北海道大学）；Faculty of Information Science and Technology, Hokkaido University（信息科学与技术学院，北海道大学）
Tsinghua University（清华大学）；University of Washington（华盛顿大学）
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynamics and spatial dependency in multistep traffic-condition prediction, we propose a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the proposed deep learning framework, spatial and temporal dependencies are modeled through the Seq2Seq model and graph convolution network separately, and the attention mechanism along with a newly designed training method bas…
Bengal Engineering and Science University（孟加拉工程与科学大学）；Heritage Institute of Technology（海瑞特理工学院）
Bio-Inspired Algorithms on Road Traffic Congestion and safety is a very promising research problem. Searching for an efficient optimization method to increase the degree of speed optimization and thereby increasing the traffic Flow in an unplanned zone is a widely concerning issue. However, there has been a limited research effort on the optimization of the lane usage with speed optimization. The main objective of this article is to find avenues or techniques in a novel way to solve the problem optimally using the knowledge from analysis of speeds of vehicles, which, in turn will act as a guid…
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
Department of Building, Civil and Environmental Engineering, Concordia University（康科迪亚大学建筑、土木和环境工程系）；Urban Artificial Intelligence Laboratory, University of Florida（佛罗里达大学城市人工智能实验室）；Department of Civil, Environmental and Construction Engineering, University of Central Florida（中央佛罗里达大学土木、环境和建设工程系）
STAHGNet: Modeling Hybrid-grained Heterogenous Dependency Efficiently for Traffic Prediction
Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）系统枢纽）；Computer Science College, Sichuan University（四川大学计算机学院）；School of Engineering, The Hong Kong University of Science and Technology（香港科技大学工程学院）；Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息枢纽）
University of Engineering and Technology Peshawar（白沙瓦工程技术大学）；University of Swabi（斯瓦比大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）；Jessore University of Science and Technology（杰索尔科技大学）
Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or …
