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Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers’ route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.