Optimization of Transit Operation Strategies: Case Study of Guangzhou, China

This paper aims to optimize transit operation strategies including fare structure and service frequency to obtain demand-supply equilibrium in transit systems such as bus, BRT and metro. Transit fare structure and service level have a significant impact on passenger mode choice and system welfare. Considering these impacts in objective function, we propose an optimization model with constraints on fare control, capacity, budget and flow to meet both the agencies' and users' expectation to transit services. The penalty function method is adopted to simplify the optimization model into a general programming model with linear constraints. The Genetic Algorithm (GA) and the Simulated Annealing algorithm (SA) are used to obtain near-optimum solutions. Finally, the optimal transit operation strategies are applied to a real-scale network of Guangzhou to test the model and the suggested algorithms.