This paper investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding estimated waiting times to mobile consumers while meeting the consumer's maximum travel distance and waiting time constraints. The goal of the broker is to maximize the number of matched requests while also keeping the "eco-system" functioning by engaging many service providers and balancing their assigned requests to provide them with incentives to stay in the system. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g. on-demand ride hailing services, on-demand food delivery, etc). Challenges of this problem include the need to satisfy many conflicting requirements for the broker, consumers and service providers and the high computational complexity for a large number of consumers and service providers. Related work in spatial crowdsourcing and ridesharing has mainly focused on maximizing the number of matched requests and minimizing travel cost, but did not consider the importance of engaging more service providers and balancing their assignments, which could become a priority when the available supply exceeds the demand. In this work, we propose a new category of service provider centric heuristics for meeting these conflicting requirements. We evaluated our algorithms using synthetic datasets with real-world characteristics. Experimental results show that our proposed heuristics can achieve a larger number of matched requests when supply and demand are balanced. They also engage a larger number of service providers with a more balanced provider assignment when the available supply greatly exceeds demand.