The rapid proliferation of mobile services has increased the need for data-driven oversight of service quality, yet deriving insights from regulator-collected datasets remains challenging. This study demonstrates techniques to tap the rich potential of drive test measurement data for analytical regulatory and policy decision-making. Focusing on leading operator MTN in Ghana, we analyzed 4 years of drive test data supplied by the telecom regulator for the capital city of Accra. Three key performance indicators were evaluated—coverage, call setup time, and speech quality. We assessed service quality trends through statistical summaries, data visualization, and machine learning modeling and predicted speech quality scores. Our analysis revealed deteriorating performance post-2019 and found that the light gradient boosting machine algorithm provided the highest accuracy predictions of speech quality. Overall, this work showcases how regulators can capitalize on vast datasets using big data mining techniques to evaluate network conditions over time and geography, enhancing field measurements for oversight. Our approach and techniques provide a template for evidence-based policy-making to uphold consumer service quality as mobile networks evolve.

中文翻译：

---

加纳移动运营商服务质量的数据驱动评估

移动服务的快速普及增加了对数据驱动的服务质量监督的需求，但从监管机构收集的数据集中获取见解仍然具有挑战性。这项研究展示了利用路测测量数据的丰富潜力进行分析监管和政策决策的技术。我们重点关注加纳领先的运营商 MTN，分析了电信监管机构为首都阿克拉提供的 4 年路测数据。评估了三个关键性能指标——覆盖范围、呼叫建立时间和语音质量。我们通过统计摘要、数据可视化和机器学习建模来评估服务质量趋势，并预测语音质量得分。我们的分析显示 2019 年后性能不断恶化，并发现光梯度增强机算法提供了最准确的语音质量预测。总体而言，这项工作展示了监管机构如何利用大数据挖掘技术来评估网络状况随时间和地理位置的变化，从而加强现场测量以进行监督。我们的方法和技术为基于证据的决策提供了模板，以随着移动网络的发展维护消费者服务质量。