Bus Trip Planning Service Based on Real Time Data

Public transportation is regarded as one of the most efficient means to confront the explosively growing city traffic problems such as congestion and pollution. However, a major obstacle for public transportation to be more adopted by citizens is its generally poor user experience. This paper proposes a system of bus trip planning service, which can help public transportation users choose the most appropriate bus lines and transfers based on real time and predicted traffic data. Because of the large scale of the data size, a two-step combination of K-Transfer and Multi-Objective algorithms is used to optimize the system response time. Experimental results on real transportation networks of large cities prove that the system is efficient and practical.