In our daily life, we commute between home and office. Even if we leave at around the same time and on the the same route, the arrival time might not be the same due to the difference in walking speed, traffic conditions and trains to ride. Therefore, it is useful to develop a movement assisting system which can give the user timely pace-making advice. In order to give such advice automatically, the system has to know an accurate progress of the movement, the method of transportation and its mobility characteristics; walking speed, routes and traffic congestion characteristics. Hence, we propose a movement assisting system that proactively checks user's movement progress and gives timely advice upon automatically created personalized plan. Utilizing user's daily life log data of movements, it extracts key features as the places the often visited at and methods of transportation between them, and estimates arrival time to the destination according to their movement characteristics. In the case of movement by bus, our system could reduce 25% of arrival time estimation error compared with the conventional methods.