Summary of Tas-tsc: a Data-driven Framework For Estimating Time Of Arrival Using Temporal-attribute-spatial Tri-space Coordination Of Truck Trajectories, by Mengran Li et al.
TAS-TsC: A Data-Driven Framework for Estimating Time of Arrival Using Temporal-Attribute-Spatial Tri-space Coordination of Truck Trajectories
by Mengran Li, Junzhou Chen, Guanying Jiang, Fuliang Li, Ronghui Zhang, Siyuan Gong, Zhihan Lv
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: The paper proposes the Temporal-Attribute-Spatial Tri-space Coordination (TAS-TsC) framework for accurately estimating time of arrival (ETA) for trucks in logistics. The framework leverages three feature spaces – temporal, attribute, and spatial – to enhance ETA estimation. It consists of a Temporal Learning Module using state space models to capture temporal dependencies, an Attribute Extraction Module transforming sequential features into structured attribute embeddings, and a Spatial Fusion Module modeling interactions among multiple trajectories using graph representation. These modules collaboratively learn trajectory embeddings used by the Downstream Prediction Module for arrival time estimation. The paper validates TAS-TsC on real truck trajectory datasets from Shenzhen, China, demonstrating its superior performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about improving how we estimate when trucks will arrive at their destinations. Right now, it’s tricky because we don’t have a lot of data and the trucks move around in different patterns. The researchers created a new system called TAS-TsC that uses three types of information – what happened before (temporal), what’s happening right now (attribute), and where things are located (spatial). This system helps figure out when trucks will arrive more accurately than other methods. The researchers tested this system on real data from China and found it worked better. |
