Research on the Application of Data Science in Traffic Optimization under the Background of Smart City

Qiman Yang

doi:10.54097/fqg4qt32

Authors

Qiman Yang

DOI:

https://doi.org/10.54097/fqg4qt32

Keywords:

Data Science; Traffic Optimization; Smart City; Big Data Analytics; Machine Learning.

Abstract

The rapid expansion of urban areas in China has exacerbated traffic congestion and environmental pressures, leading to the rise of smart city projects that apply data science techniques to optimize traffic systems. This study investigates the use of various data-driven approaches—including big data analytics, machine learning algorithms, and Internet of Things (IoT) frameworks—to improve traffic management in Chinese smart cities. It provides an overview of fundamental methods and real-world applications such as continuous traffic monitoring, predictive modeling of traffic flows, and the enhancement of public transportation services. Examples from metropolitan areas like Beijing and Shenzhen are presented to demonstrate practical outcomes. The discussion also covers key obstacles involving data security, system integration, infrastructure readiness, and the availability of skilled personnel. The paper concludes by identifying future avenues for research focusing on cutting-edge AI technologies, self-driving vehicles, governance of data, and active citizen involvement. The evidence indicates that leveraging data-centric solutions can substantially advance urban transportation efficiency, environmental sustainability, and residents’ overall living standards in China’s fast-developing cities.

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