Urban cellular networks face complex performance challenges due to high infrastructure density, varied user mobility, and diverse service demands. While several datasets address network behaviour across different environments, there is a lack of datasets that captures user centric Quality of Experience (QoE), and diverse mobility patterns needed for efficient network planning and optimization solutions, which are important for QoE driven optimizations and mobility management. This study presents a curated dataset of 30,925 labelled records, collected using GNetTrack Pro within a 2 km2 dense urban area, spanning three major commercial network operators. The dataset captures key signal quality parameters (e.g., RSRP, RSRQ, SNR), across multiple real world mobility modes including pedestrian routes, canopy walkways, shuttle buses, and Bus Rapid Transit (BRT) routes. It also includes diverse network traffic scenarios including (1) FTP upload and download, (2) video streaming, and (3) HTTP browsing. A total of 132 physical cell sites were identified and validated through OpenCellID and on-site field inspections, illustrating the high cell density characteristic of 5G and emerging heterogeneous network deployment. The dataset is particularly suited for machine learning applications, such as handover optimization, signal quality prediction, and multi operator performance evaluation. Released in a structured CSV format with accompanying preprocessing and visualization scripts, this dataset offers a reproducible, application ready resource for researchers and practitioners working on urban cellular network planning and optimization.