Institute of Transportation, MOTC

       Understanding the movement of tourists between attractions and the number of visitors at attractions is helpful for the authorities to improve the public transportation services in scenic areas and to allocate resources. With the rapid development of technology and the increasing popularity of people using mobile phones to access the Internet, cellular-based vehicle probe (CVP) data has the advantages of larger sample size, broader coverage and lower collection cost in human mobility prediction. Users’ spatial-temporal trajectories can be effectively constructed by analyzing CVP data. The potential movement pattern of users can be extracted from those trajectories. This study develops a systematic approach to predict the number of visitors at attractions. Firstly, trip
chains and origin-destination (OD) matrix are constructed by analyzing users’ CVP data. The OD matrix is used as the basis for estimating the transition matrix between attractions. Then, a Markov Chain model is established to predict the number of tourists at each attraction in each hour. The proposed method is applied to predict the numbers of tourists at 59 major attractions, recommended by the Tourism Bureau, in Hualien county. The CVP data is provided by a major telecom in Taiwan. The mean absolute percentage error of the prediction results is about 20%, which is practically acceptable. The evaluation results indicate that the proposed method has a good prediction
performance.