With the rapid development of urbanization, the problem of urban traffic congestion has become increasingly prominent. Dynamic route guidance will be a powerful way to improve the capacity of urban traffic management and mitigate traffic congestion in big cities. In the design of simulation-based experiments for most dynamic route guidance methods, the simulation data is generally estimated from a specific traffic scenario in the real-world. However, highly dynamic traffic in the city means that traffic scenarios in real systems are diverse. Therefore, if a route guidance algorithm cannot adjust its strategy according to the spatial and temporal characteristics of different traffic scenarios, then it cannot guarantee good results under all traffic scenarios. Thus, the ideal dynamic route guidance methods should have highly adaptive learning ability under diverse traffic scenarios, so as to have extensive improvement capabilities for different traffic scenarios.