The Application of Big Data in Urban Traffic Prediction and Management
Keywords:
Big data, urban traffic, traffic management, smart city, traffic prediction, intelligent transportation systemsAbstract
The purpose of this study is to systematically review and analyze scientific evidence on the role and functions of big data in predicting and managing urban traffic. This research employed a qualitative systematic review and thematic analysis approach. A comprehensive search was conducted across major databases including Scopus, Web of Science, IEEE Xplore, Google Scholar, and ScienceDirect. Based on predefined inclusion and exclusion criteria, 18 articles were selected for in-depth review. Data analysis was performed using NVivo 14 through open, axial, and selective coding. Credibility of the analysis was ensured through constant comparison, iterative code refinement, and theoretical saturation. The findings revealed that big data significantly enhances traffic prediction accuracy, spatiotemporal pattern analysis, machine learning model performance, and detection of network anomalies. Additionally, extensive applications were identified in signal timing optimization, dynamic flow management, improvement of public transportation efficiency, emergency response enhancement, and smart parking management. Challenges such as data quality issues, infrastructural limitations, privacy concerns, and organizational barriers were also identified. Overall, big data offers a powerful foundation for intelligent traffic management, supporting improved decision-making, congestion reduction, enhanced safety, and better quality of urban life. However, effective utilization requires addressing technical, legal, and organizational challenges and strengthening data-driven urban infrastructure.
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References
Alsaadi, A. (2022). Big data governance challenges in smart cities. Journal of Urban Technology, 29(4), 45–62.
Chen, Y., Xu, H., & Zhao, L. (2021). Sensor-based traffic flow prediction using big data analytics. Transportation Research Part C, 129, 103–121.
Cooper, D., Martin, E., & Jones, T. (2019). Real-time traffic incident detection using big data. Accident Analysis & Prevention, 132, 105–118.
Gao, H., & Liu, Z. (2022). Organizational barriers in implementing big data systems in urban transport management. Cities, 121, 103–148.
Jin, P., Wang, K., & Zhou, X. (2021). Using mobile phone data to model urban mobility patterns. Computers, Environment and Urban Systems, 88, 101–136.
Li, X., & Wang, Y. (2020). Multimodal data fusion for traffic flow analytics. IEEE Transactions on Intelligent Transportation Systems, 21(6), 2349–2360.
Rahmani, M., Eshghi, A., & Shafiei, S. (2021). Infrastructure readiness for big data-based traffic systems. International Journal of Transportation Science, 10(3), 112–129.
Shoup, D. (2018). Parking and the city. Routledge.
Sun, L., & Axhausen, K. (2020). Integrating public transport data with traffic flow modeling. Transportation Research Part A, 136, 123–140.
Taylor, B., & Francis, D. (2020). Challenges in integrating heterogeneous transportation data. Journal of Transportation Data Analytics, 7(2), 67–85.
Yu, H., & Ma, X. (2021). Intelligent signal control using real-time big data. Transportation Research Interdisciplinary Perspectives, 11, 100–432.
Zhang, J., Li, S., & Chen, R. (2022). Deep learning models for traffic prediction using big data. Neural Networks, 150, 240–258.
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