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In modern cities, public transit is important for meeting people's transportation needs. Dynamic bus dispatching plays an important role in a transit system, especially when the system experiences extreme weather, fluctuations in passenger flows, etc. Consequently, the assumption presented in the literature that departure timetables of buses are pre-determined at stops is not applicable to transit systems in developing countries, where traffic conditions vary frequently and dynamically. With the development of new technology, e.g. the internet of things, real-time information for public transit systems can be obtained conveniently. Considering multiple types of real-time information such as dynamic passenger flows and road traffic status, we propose an optimization model for dynamic bus dispatching to minimize the overall waiting time of passengers in a transit system. In our model, multiple bus routes and waiting times at the transfer stations are taken into consideration. Accordingly, we develop a genetic algorithm with memory-based initialization to solve the model. The effectiveness of the proposed approach is verified for different scenarios via numerical experiments. [ABSTRACT FROM AUTHOR]