在智慧城市交通背景下，实现出租车供给与乘客需求的高效匹配需实时融合城市交通路网数据与移动性模式。传统出租车热点预测模型通常仅依赖历史需求数据，忽视了交通拥堵、道路事件及公共活动等动态影响因素。本文提出一种面向交通感知的、基于图的强化学习（RL）框架，用于大都市环境下的最优出租车布点。该框架将城市道路网络建模为图结构：交叉口为节点，路段为边；节点属性包含历史需求、事件邻近度以及从实时交通API获取的拥堵评分。利用图神经网络（GNN）嵌入对交通网络中的时空依赖关系进行编码，并将其输入Q-learning智能体以推荐最优出租车热点。奖励机制联合优化乘客等待时间、司机行驶距离与拥堵规避目标。在基于德里真实地理边界与历史网约车请求模式生成的模拟出租车数据集上的实验表明，相较于基线随机选择方法，所提模型使乘客等待时间降低约56%，行驶距离减少38%。该方法可适配多模态交通系统，并能集成至智慧城市平台，支持城市出行的实时优化。

In the context of smart city transportation, efficient matching of taxi supply with passenger demand requires real-time integration of urban traffic network data and mobility patterns. Conventional taxi hotspot prediction models often rely solely on historical demand, overlooking dynamic influences such as traffic congestion, road incidents, and public events. This paper presents a traffic-aware, graph-based reinforcement learning (RL) framework for optimal taxi placement in metropolitan environments. The urban road network is modeled as a graph where intersections represent nodes, road segments serve as edges, and node attributes capture historical demand, event proximity, and real-time congestion scores obtained from live traffic APIs. Graph Neural Network (GNN) embeddings are employed to encode spatial-temporal dependencies within the traffic network, which are then used by a Q-learning agent to recommend optimal taxi hotspots. The reward mechanism jointly optimizes passenger waiting time, driver travel distance, and congestion avoidance. Experiments on a simulated Delhi taxi dataset, generated using real geospatial boundaries and historic ride-hailing request patterns, demonstrate that the proposed model reduced passenger waiting time by about 56% and reduced travel distance by 38% compared to baseline stochastic selection. The proposed approach is adaptable to multi-modal transport systems and can be integrated into smart city platforms for real-time urban mobility optimization.