This paper poses the planning problem faced by the dispatcher responding to urban emergencies as a Hybrid (Discrete and Continuous) State and Action Markov Decision Process (HSA-MDP). We evaluate the performance of three online planning algorithms based on hindsight optimization for HSA- MDPs on real-world emergency data in the city of Corvallis, USA. The approach takes into account and respects the policy constraints imposed by the emergency department. We show that our algorithms outperform a heuristic policy commonly used by dispatchers by significantly reducing the average response time as well as lowering the fraction of unanswered calls. Our results give new insights into the problem such as withholding of resources for future emergencies in some situations.