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.

翻译：在智慧城市交通背景下，出租车供给与乘客需求的高效匹配需要实时整合城市交通网络数据与移动模式。传统出租车热点预测模型通常仅依赖历史需求数据，忽略了交通拥堵、道路事故及公共活动等动态影响因素。本文提出一种面向大都市环境的交通感知、基于图的强化学习框架，用于实现最优出租车调度。该框架将城市道路网络建模为图结构：交叉口作为节点，路段作为边，节点属性则涵盖历史需求、事件邻近度以及从实时交通API获取的拥堵评分。通过采用图神经网络嵌入对交通网络内的时空依赖关系进行编码，并利用Q学习智能体推荐最优出租车热点。奖励机制协同优化乘客等待时间、司机行驶距离与拥堵规避。基于真实地理空间边界和历史网约车请求模式生成的德里出租车仿真数据集实验表明，相较于基线随机选择方法，所提模型将乘客等待时间减少约56%，行驶距离降低38%。该方法可适配多模式交通系统，并能集成至智慧城市平台以实现实时城市移动性优化。