Papers Travel Time Estimation
“Travel Time Estimation” 태그가 달린 논문 59편 · 필터 해제
MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation
Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level depen…
Travel Time EstimationIncremental LearningCapturing Context-Aware Route Choice Semantics for Trajectory Representation Learning
Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis. Fr…
Representation LearningTravel Time EstimationMultimodal Trajectory Representation Learning for Travel Time Estimation
Accurate travel time estimation (TTE) plays a crucial role in intelligent transportation systems. However, it remains challenging due to heterogeneous data sources and complex traffic dynamics. Moreover, traditional appr…
Representation LearningTravel Time EstimationTrajectory ModelingTraj-MLLM: Can Multimodal Large Language Models Reform Trajectory Data Mining?
Building a general model capable of analyzing human trajectories across different geographic regions and different tasks becomes an emergent yet important problem for various applications. However, existing works suffer …
Travel Time EstimationAnomaly DetectionTowards An Efficient and Effective En Route Travel Time Estimation Framework
En route travel time estimation (ER-TTE) focuses on predicting the travel time of the remaining route. Existing ER-TTE methods always make re-estimation which significantly hinders real-time performance, especially when …
Meta-LearningTravel Time EstimationA Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior
This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, t…
Data IntegrationFederated LearningMeta-LearningReinforcement Learning (RL)+1Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning
Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches …
Graph AttentionRepresentation LearningTravel Time EstimationRLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning
En Route Travel Time Estimation (ER-TTE) aims to learn driving patterns from traveled routes to achieve rapid and accurate real-time predictions. However, existing methods ignore the complexity and dynamism of real-world…
Decision MakingTravel Time EstimationSPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation
Accurate travel time estimation is essential for navigation and itinerary planning. While existing research employs probabilistic modeling to assess travel time uncertainty and account for correlations between multiple t…
Travel Time EstimationRED: Effective Trajectory Representation Learning with Comprehensive Information
Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation…
Representation LearningTravel Time EstimationTrajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics
Trajectory representation learning is a fundamental task for applications in fields including smart city, and urban planning, as it facilitates the utilization of trajectory data (e.g., vehicle movements) for various dow…
Representation LearningTravel Time EstimationContext-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models
Modeling trajectory data with generic-purpose dense representations has become a prevalent paradigm for various downstream applications, such as trajectory classification, travel time estimation and similarity computatio…
Representation LearningTravel Time EstimationDutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T). Accurately quantifying this uncertainty …
Deep Reinforcement LearningMixture-of-ExpertsTravel Time EstimationUncertainty QuantificationLink Representation Learning for Probabilistic Travel Time Estimation
Travel time estimation is a crucial application in navigation apps and web mapping services. Current deterministic and probabilistic methods primarily focus on modeling individual trips, assuming independence among trips…
Data AugmentationRepresentation LearningTravel Time EstimationInterpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems. As one of the world's largest ride-haili…
Mixture-of-ExpertsPredictionTravel Time EstimationDeep Learning for Trajectory Data Management and Mining: A Survey and Beyond
Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical applications such as location services, urban traff…
Anomaly DetectionDeep LearningManagementTravel Time EstimationUVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-time estimation, trajectory recovery, and t…
Trajectory ModelingTrajectory PredictionTrajectory RecoveryTravel Time EstimationTraffic estimation in unobserved network locations using data-driven macroscopic models
This paper leverages macroscopic models and multi-source spatiotemporal data collected from automatic traffic counters and probe vehicles to accurately estimate traffic flow and travel time in links where these measureme…
Travel Time EstimationEverybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Graph neural networks (GNNs) have led to major breakthroughs in a variety of domains such as drug discovery, social network analysis, and travel time estimation. However, they lack interpretability which hinders human tr…
Drug DiscoveryTravel Time EstimationEffects of Dynamic and Stochastic Travel Times on the Operation of Mobility-on-Demand Services
Mobility-on-Demand (MoD) services have been an active research topic in recent years. Many studies focused on developing control algorithms to supply efficient services. To cope with a large search space to solve the und…
Travel Time Estimation