Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasant moments for residents and tourists. Since the number of features that may be used to predict a user transportation mode can be substantial, finding a subset of features that maximizes a performance measure is worth investigating. In this work, we explore wrapper and information retrieval methods to find the best subset of trajectory features. After finding the best classifier and the best feature subset, our results were compared with two related papers that applied deep learning methods and the results showed that our framework achieved better performance. Furthermore, two types of cross-validation approaches were investigated, and the performance results show that the random cross-validation method provides optimistic results.
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
Univ Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学克洛德·贝尔纳大学）；UJM-Saint Etienne（圣艾蒂安大学（让·莫奈大学））；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）
Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；Shanghai AI Laboratory（上海人工智能实验室）；State Key Laboratory of Integrated Services Networks, Xidian University（西安电子科技大学集成服务网络国家重点实验室）；Nokia Bell Labs（诺基亚贝尔实验室）
Automatic Combination of Sample Selection Strategies for Few-Shot Learning
Faculty of Information Technology, Brno University of Technology（布拉格技术学院，布拉格技术大学）；Kempelen Institute of Intelligent Technologies（智能技术研究所）；Eindhoven University of Technology（埃因霍温技术大学）
BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning
Intelligent Embedded Systems University of Kassel（智能嵌入式系统卡塞尔大学）；Machine Learning Research Unit TU Wien（机器学习研究单位维也纳技术大学）；University of Kassel（卡塞尔大学）；TU Wien（维也纳技术大学）
TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation
School of Transportation（交通学院）；Southeast University（东南大学）；School of Artificial Intelligence and Computer Science（人工智能与计算机科学学院）；Jiangnan University（江南大学）；Department of Civil and Environmental Engineering（土木与环境工程系）；Hong Kong Polytechnic University（香港理工大学）
Dept. Computing Science & Alberta Machine Intelligence Institute (Amii), University of Alberta（计算科学系及阿尔伯塔人工智能研究所（Amii），阿尔伯塔大学）；Canada CIFAR AI Chair, Amii（加拿大CIFAR人工智能主席，Amii）
