To fully take the advantages of ride-sharing ride hailing, such as high loading rate, high operating efficiency, and less traffic resources, and to alleviate the difficulty of getting a taxi in urban hubs, the topic of ride-sharing route optimization for ride hailing is studied in this paper. For the multiple ride hailing ride-sharing demands and multiple ride hailing services in the urban road network in a specific period, the objective function is established with the shortest route of the system. The constraint conditions of the optimization model are constructed by considering factors of the rated passenger capacity, route rationality, passenger benefits, driver benefits and time window. Based on the idea of the Genetic Algorithm, the solution algorithm of the optimization model is developed. According to the supply and demand data of taxi during peak hours in the local road network in the city of Dalian, the optimization model and algorithm are used to optimize the ride-sharing route scheme. Research results indicate that the optimization model and algorithm can find the approximate optimal solution of the system in a short time. Compared with the traditional non-ride-sharing mode, the ride-sharing scheme can not only effectively reduce the taxi empty-loaded rate and the travel cost of passengers, improve the efficiency of drivers, but also save energy and reduce emissions, and promote the sustainable development of urban traffic.

To fully take the advantages of conventional bus and subway, and to maximize the overall feeder efficiency of the public transport system, the topic of feeder bus route optimization is studied in this paper. Considering the origin destination demand of passenger flow between subway stations and bus stations and transfer characteristics, the objective function is established with the minimum sum of bus operation cost and passenger travel cost. Taking into account the integrity of the feeder bus route, the rationality of the route, the route capacity and the station transfer factors, the constraints of the optimization model are constructed. Based on the idea of the genetic algorithm, the solution algorithm of the optimization model is developed. The genetic algorithm and enumeration algorithm are used to solve the optimization of the feeder bus route in this case, and the accuracy and efficiency of the solution are analyzed. The influence of the number of feeder bus routes on the system in the case network is compared and discussed. We compare and analyze the differences between the original bus network and the feeder bus network in terms of bus operation cost, passenger flow demand and total passenger travel cost. The research shows that the model and algorithm can find the approximate optimal solution of the feeder bus network scheme related to the subway through fewer iterations. The number of routes in the model has little impact on the whole feeder system, and the optimization scheme using five routes is effective and reasonable in this paper. Compared with the existing bus network, the optimization scheme has obvious advantages in improving the passenger-carrying rate, reducing the per capita travel cost and improving the overall operation efficiency of the system.