Two-thirds of the people who buy a new car prefer to use a substitute instead of the built-in navigation system. However, for many applications, knowledge about a user's intended destination and route is crucial. For example, suggestions for available parking spots close to the destination can be made or ride-sharing opportunities along the route are facilitated. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data. The approach follows a three-step procedure: First, a $k$-d tree-based space discretization is performed, mapping GPS locations to discrete regions. Secondly, a recurrent neural network is trained to predict the destination based on partial sequences of trajectories. The neural network produces destination scores, signifying the probability of each region being the destination. Finally, the routes to the most probable destinations are calculated. To evaluate the method, we compare multiple neural architectures and present the experimental results of the destination prediction. The experiments are based on two public datasets of non-personalized, timestamped GPS locations of taxi trips. The best performing models were able to predict the destination of a vehicle with a mean error of 1.3 km and 1.43 km respectively.
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
Institute of High Performance Computing (IHPC), A*STAR, Singapore（新加坡科技研究局高性能计算研究所）；The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）；Meituan Inc., Shenzhen, China（美团（深圳））；Centre for Frontier AI Research (CFAR), A*STAR, Singapore（新加坡科技研究局前沿人工智能研究中心）；Nankai University（南开大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory
School of Industrial and Management Engineering, Korea University, Seoul 02841, Republic of Korea（工业与管理工程学院，韩国大学，首尔02841，大韩民国）；SeaVantage, Seoul 06120, Republic of Korea（SeaVantage，首尔06120，大韩民国）
Holistic Semantic Representation for Navigational Trajectory Generation
Zhejiang University（浙江大学）；Big Graph Center, Hangzhou City University（杭州城市大学大数据中心）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
Department of Engineering, Cambridge University（剑桥大学工程系）；The Alan Turing Institute（艾伦·图灵研究所）；Departments of Statistics & Computer Science, University of Warwick（沃里克大学统计学与计算机科学系）
Learning Navigation by Visual Localization and Trajectory Prediction
Institute of Mathematics of the Romanian Academy（罗马尼亚科学院数学研究所）；Faculty of Automatic Control and Computers, University Politehnica of Bucharest（布加勒斯特理工大学自动控制与计算机学院）
Learning Graph-Indexed Trajectory Patterns for Stochastic On-Time Arrival Routing
Sichuan University-Pittsburgh Institute（四川大学匹兹堡研究院）；Sichuan University（四川大学）；College of Computer Science（计算机科学学院）；University College Dublin（都柏林大学）；School of Electrical and Electronic Engineering（电子与电气工程学院）；School of Electronics and Information Engineering（电子与信息工程学院）
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Everyday Robots；Massachusetts Institute of Technology（麻省理工学院）；University of California, Berkeley（加利福尼亚大学伯克利分校）；University of California, Riverside（加利福尼亚大学河滨分校）
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
Institute of Innovative Research, Tokyo Institute of Technology（东京工业大学创新研究院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）
