Open Access

ARTICLE

Model Construction for Complex and Heterogeneous Data of Urban Road Traffic Congestion

Jianchun Wen¹, Minghao Zhu^1,*, Bo Gao², Zhaojian Liu¹, Xuehan Li³

1 School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China
2 School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
3 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

* Corresponding Author: Minghao Zhu. Email:

Computers, Materials & Continua 2026, 86(2), 1-17. https://doi.org/10.32604/cmc.2025.069671

Received 27 June 2025; Accepted 26 September 2025; Issue published 09 December 2025

Abstract

Urban traffic generates massive and diverse data, yet most systems remain fragmented. Current approaches to congestion management suffer from weak data consistency and poor scalability. This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model (UTC-UMM). The goal is to provide a standardized and extensible framework for describing, extracting, and storing multisource traffic data in smart cities. The model defines a two-tier specification that organizes nine core traffic resource classes. It employs an eXtensible Markup Language (XML) Schema that connects general elements with resource-specific elements. This design ensures both syntactic and semantic interoperability across siloed datasets. Extension principles allow new elements or constraints to be introduced without breaking backward compatibility. A distributed pipeline is implemented using Hadoop Distributed File System (HDFS) and HBase. It integrates computer vision for video and natural language processing for text to automate metadata extraction. Optimized row-key designs enable low-latency queries. Performance is tested with the Yahoo! Cloud Serving Benchmark (YCSB), which shows linear scalability and high throughput. The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics. The discussion highlights its potential to improve data reuse, portability, and scalability in urban congestion studies. Future research will explore integration with association rule mining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.

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Keywords

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Supplementary Material

Supplementary Material File

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