.png){kind=icon}
Return
Accelerated Co-Movement Patterns Mining: A Heterogeneous Framework Based on GPU Clusters
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2025.108302.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In modern urban public transportation systems, tens of thousands of buses traverse on open road networks, serving millions of residents and generating massive GPS trajectory data. Effectively mining this data is critical for improving safety and efficiency. Co-movement pattern mining is a representative compute-intensive technique which is commonly used for bus bunching detection, but when executed on CPU-based systems, it faces scalability and latency challenges. To address this, we present an accelerated co-movement pattern mining framework based on GPU clusters. It integrates workflow management of PySpark with the high-performance computing capabilities of GPUs, and employs a pipeline to perform spatial projection, hybrid indexing, filter-verification, and memory management. We implement our approach on a Spark cluster with three nodes (equipped with six NVIDIA A40 GPUs) and evaluate it on a large-scale dataset comprising 12,788 vehicles, and over 3.22 billion GPS records collected over 31 days. The experimental results show that, compared to CPU-based approaches, our solution achieves a maximum speedup of 15.69 × . These results demonstrate that our solution can effectively support large-scale GPS trajectory analysis in bus transportation systems.
Journal
F
IF:
0
Papers:
642
Citations:
0
Organization
Cited Papers
Bus bunching: a comprehensive review from demand, supply, and decision-making perspectives.png)
TRANSPORT REVIEWS
OAAI
TRANSPORT REVIEWSIF9.9
OAAIShareSaveShareSaveShareSaveStreamlining trajectory map-matching: a framework leveraging spark and GPU-based stream processing
PREAI
ShareSave
View more