Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task
This work addresses ETA prediction for intelligent taxi services and navigation systems, but it appears incremental as it builds on existing embedding techniques.
The paper tackles the problem of predicting Estimated Time of Arrival (ETA) in road networks by addressing the generalization issues of spatial embeddings due to sparse data from floating cars and temporal events, proposing a two-stage approach to improve accuracy.
Recently, deep learning has achieved promising results in the calculation of Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the start point to a certain place along a given path. ETA plays an essential role in intelligent taxi services or automotive navigation systems. A common practice is to use embedding vectors to represent the elements of a road network, such as road segments and crossroads. Road elements have their own attributes like length, presence of crosswalks, lanes number, etc. However, many links in the road network are traversed by too few floating cars even in large ride-hailing platforms and affected by the wide range of temporal events. As the primary goal of the research, we explore the generalization ability of different spatial embedding strategies and propose a two-stage approach to deal with such problems.