arXiv:2301.05293
HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
원문 초록
Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Towards An Efficient and Effective En Route Travel Time Estimation Framework
Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning
SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Link Representation Learning for Probabilistic Travel Time Estimation
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
토론
이 논문과 연결된 포럼 글이 아직 없습니다.