Urban transportation systems require precise modeling of dynamic spatiotemporal patterns across diverse tasks, such as traffic forecasting, electric vehicle (EV) charging demand prediction, and taxi dispatch. Existing approaches suffer from two key limitations: traditional deep learning models are task-specific and lack generalization capabilities, whereas Large Language Models (LLMs) struggle with structured spatiotemporal data and numerical reasoning. To bridge this gap, we propose TransLLM, a unified multi-task framework that synergizes spatiotemporal encoding with LLM reasoning through learnable prompt composition. To enable LLMs to perceive complex graph dependencies, we design a noise-augmented spatiotemporal encoder that projects structured signals into the LLM’s embedding space. Furthermore, to overcome the rigidity of fixed prompt templates in heterogeneous traffic scenarios, we introduce an instance-level prompt routing mechanism trained via reinforcement learning. The framework operates by encoding spatiotemporal patterns into contextual representations, dynamically composing personalized prompts to guide LLM reasoning, and projecting the resulting representations through specialized output layers to generate task-specific predictions. Experiments on seven datasets and three tasks demonstrate that TransLLM outperforms many baselines, showing superior adaptability in both supervised and zero-shot settings with excellent generalization and robustness. Our code and data are available at https://github.com/lengjiaming/TransLLM.