Data Analytics for Dynamic Urban Operations: A Test-Based Study on Data Analytics Efficiency

¹ Department of management and innovation, National Research University Moscow State University of Civil Engineering, 129337 Yaroslavskoe shosse, 26, Moscow, Russia
² Uttaranchal University, Dehradun 248007, India
³ Lovely Professional University Phagwara, Punjab, India
⁴ K R Mangalam University, Gurgaon, India
⁵ GD Goenka University, Sohna, Haryana, India
⁶ GRIET, Bachupally, Hyderabad, Telangana

^* Corresponding Email- DmitrievaEI@gic.mgsu.ru

Abstract

This paper explores the field of data analytics for dynamic urban operations and provides a systematic analysis of the importance and possible implications of this field. Our investigation indicates significant data volumes in an urban setting that is data-rich: 500 GB are generated by traffic sensors, 300 GB by environmental monitors, 150 GB by mobile apps, and 75 GB by emergency calls. A variety of analytics techniques, each with a different processing time, are built upon these data sources. These techniques include descriptive, predictive, prescriptive, and diagnostic analytics. The outcomes, which include 90% accuracy, an average processing time of 40 minutes, 80% resource utilization, and 4.2 user satisfaction ratings, highlight the benefits of data analytics. According to the comparison study, diagnostic analytics has a score of 7.8, indicating room for development, while prescriptive analytics leads with an efficiency score of 8.4. As urban stakeholders and academics work to improve urban systems and solve urban issues, the results give a thorough understanding of the effectiveness and application of data analytics in the context of dynamic urban operations.