Urban public transport planning methods under low carbon emission constraints

In order to reduce the carbon emissions of urban public transportation systems and improve service efficiency and coverage, a low-carbon emission constrained urban public transportation planning method is proposed. Firstly, three objective functions were set: minimising carbon emissions, minimising average waiting time for passengers, and maximising service coverage, with corresponding constraints clearly defined. Subsequently, based on these objective functions, a city public transportation planning model was constructed and solved using particle swarm optimisation algorithm. To enhance the efficiency of the problem-solving process, a mechanism capable of adaptation was devised to dynamically modify the inertia weight and acceleration constants within the particle swarm optimisation algorithm. The outcomes of the experiments indicate that this approach effectively curtails the average waiting period for passengers to 2.34 minutes and attains a substantial service coverage rate when the peak air quality index (AQI) reading is 88, thereby substantiating the efficacy of the planning methodology.