Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning issue by leveraging taxi GPS traces. In this paper, we propose a two-phase approach based on the crowd-sourced GPS data for night-bus route planning. In the first phase, we develop a process to cluster "hot" areas with dense passenger pick-up/drop-off, and then propose effective methods to split big "hot" areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops as well as bus operation time constraints, we derive several effective rules to build bus routing graph and prune the invalid stops and edges iteratively. We further develop two heuristic algorithms to automatically generate candidate bus routes, and finally we select the best route which expects the maximum number of passengers under the given conditions. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set which contains more than 1.57 million passenger delivery trips, generated by 7,600 taxis for a month in Hangzhou, China.