Multi-scale learning for fine-grained traffic flow-based travel time estimation prediction

Abstract

In intelligent transportation systems (ITS), achieving accurate travel time estimation (TTE) is paramount, much like route planning. Precisely predicting travel time across different urban areas is vital, and an essential requirement for these privileges is having fine-grained knowledge of the city. In contrast to prior studies that are restricted to coarse-grained data, we broaden the scope of traffic flow forecasting to fine granularity, which provokes explicit challenges: (1) the prevalence of inter-grid transitions within fine-grained data introduces complexity in capturing spatial dependencies among grid cells on a global scale. (2) stemming from dynamic temporal dependencies. To address these challenges, we propose the multi-scaling hybrid model (MSHM) as a novel approach. Initially, a multi-directional convolutional layer is first used to acquire high-level depictions for each cell to retrieve the semantic attributes of the road network from local and global aspects. Next, we incorporate the characteristics of the road network and coarse-grained flow features to regularize the local and global spatial distribution modeling of road-relative traffic flow using an enhanced deep super-resolution (EDSR) technique. Benefiting from the EDSR method, our approach can generate high-quality fine-grained traffic flow maps. Furthermore, to continuously provide accurate TTE over time by leveraging well-designed multi-scale feature modeling, we incorporate a multi-scale feature expression of each road segment, capturing intricate details and important features at different scales to optimize the TTE. We conducted comprehensive trials on two real-world datasets, BJTaxi and NYCTaxi, aiming to achieve superior results compared to baseline methods.