Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transforms and have built a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process.
An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is able to capture the influence of business cycles and the heterogeneity of the marketplace, improving prediction accuracy by more than fivefold, with an estimated annualized saving of \$50MM.
Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks
University of Pennsylvania（宾夕法尼亚大学）；University of Washington（华盛顿大学）；Princeton University（普林斯顿大学）；The University of Arizona（亚利桑那大学）；University of South California（南加利福尼亚大学）；Duke Kunshan University（昆山杜克大学）
School of Computer Science and Technology（计算机科学与技术学院）；Taiyuan Normal University（太原师范学院）；Institute of Computer Science（计算机科学研究所）；University of Göttingen（哥廷根大学）；James Watt School of Engineering（詹姆斯·瓦特工程学院）；University of Glasgow（格拉斯哥大学）；Laboratory of Intelligent Home Appliances（智能家电实验室）；College of Science and Technology（科学与技术学院）；Ningbo University（宁波大学）
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
Southeast University（东南大学）；Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies（江苏省现代城市交通技术协同创新中心）；City University of Hong Kong（香港城市大学）；School of Mathematics, Southeast University（东南大学数学学院）；Systems Research Institute of the Polish Academy of Sciences（波兰科学院系统研究所）；Luoyang Normal University（洛阳师范学院）；Purple Mountain Laboratories（紫金山实验室）；AGH University of Krakow（AGH科技大学）
Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models
Department of Computer Science, Memorial University of Newfoundland（纪念大学计算机科学系）；Industrial Engineering Department, Dalhousie University（达尔豪斯大学工业工程系）；Institute for Big Data Analytics, Dalhousie University（达尔豪斯大学大数据分析研究所）；Institute of Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）；Department of Computer Science and Media Technology, Linnaeus University（林纳大学计算机科学与媒体技术系）
Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership
Technological University of the Philippines（菲律宾技术大学）；National University（国立大学）；Philippine Coding Camp（菲律宾编程营）；University of the Philippines Open University（菲律宾大学开放大学）
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
School of Computer Science；Technology Beijing Jiaotong University Beijing China；Department of Computer Science Aalborg University Aalborg Denmark；Key Laboratory of Big Data \& Artificial Intelligence in Transportation, Ministry of Education Beijing China；Beijing Key Laboratory of Traffic Data Mining；Beijing Jiaotong University；Aalborg University；Key Laboratory of Big Data \& Artificial Intelligence in Transportation, Ministry of Education
AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting
Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Twin Cities, 500 Pillsbury Drive S.E., Minneapolis, MN 55455-0116, United States of America（土木、环境与地球工程系，明尼苏达大学，双城校区）
Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management
Technical University of Denmark（丹麦技术大学）；Technical University of Munich（慕尼黑工业大学）；University of Groningen（格罗宁根大学）；University of Twente（特文特大学）；Munich Data Science Institute（慕尼黑数据科学研究所）
