Adverse weather such as rain and snow often aggravates urban traffic congestion, and large cities such as Beijing and Tianjin often experience multi section regional traffic congestion under rainfall conditions. Therefore, based on the real traffic data of the road network in the central and suburban area of Tianjin, with the macroscopic fundamental diagram model as the research foundation, the paper compared the time series and the macroscopic fundamental diagram changes of road network traffic flow for different rainfall and road networks. It analyzed the impact of different rainfall on the traffic status of the road network in the central and suburban area of Tianjin. The dynamic evolution models of the road network were constructed based on the change law in macroscopic traffic flow of the road network in the central and suburban area under different rainfall, and their parameters were also calibrated and validated. Aiming at solving the regional congestion problem of the road network, the control strategies of the road network in the central area and the suburban area under different rainfall conditions were designed based on the perimeter control of macroscopic fundamental diagram. The effectiveness of different control strategies was verified through simulation experiments, and feasible strategies were proposed to alleviate road network congestion in central and suburban areas. The simulation results show that: under light rain conditions, the traffic status between the central area and the suburban area is more balanced and the road network regulation effect is better when the transfer proportion of traffic flow from the suburban area to the central area is reduced within the range of 9% to 50%; under the condition of heavy rain, the traffic status of the central area and the suburban area is more balanced and the road network regulation effect is better when the reduction is controlled within the range of 23% to 50%. This shows that the control strategy can alleviate the traffic congestion of the road network and ensure the stable operation of the road network traffic system.

The unstable running time and the difficulty to accurately estimate the arrival time reduce passengers’ satisfaction with bus traveling. With the diversification of cities, bus line types are gradually becoming more diverse, and the variations in bus operating time of the various bus line types cause trouble in bus scheduling and inconvenience for passengers. To solve above problems, firstly, this paper used K-means ++ algorithm to cluster the stations with the sum of squares of error (SSE) as the measurement index. The operation characteristics and reliability influencing factors of different types of bus lines were taken into consideration to determine the alternative station sets of limited-stop bus service. Then, a joint optimization strategy model of limited-stop bus service and speed regulation was proposed to determine the limited-stop bus station, the departure time, and the running speed of all buses. This model took the minimization of bus operation cost, passenger travel cost, and reliability cost as the objectives, and took the constraints of operation process and headway of limited-stop bus service and all-stop bus service into consideration. Finally, genetic algorithm was used to solve the optimization model and Beijing bus system was selected for case analysis. The results show that the limited-stop bus service can reduce the operation cost of public transport, the speed regulation strategy can better lower the passenger travel cost and reliability cost, and the joint optimization strategy of limited-stop bus service and speed regulation can effectively reduce the total cost of the bus system.