Taxicabs equipped with Global Positioning System (GPS) devices can serve as useful probes for monitoring the traffic state in an urban area. This paper presents a new descriptive model for estimating hourly average of urban link travel times using taxicab origin–destination (OD) trip data. The focus of this study is to develop a methodology to estimate link travel times from OD trip data and demonstrate the feasibility of estimating network condition using large-scale geo-location data with partial information. The data, collected from the taxicabs in New York City, provides the locations of origins and destinations, travel times, fares and other information of taxi trips. The new model infers the possible paths for each trip and then estimates the link travel times by minimizing the error between the expected path travel times and the observed path travel times. The model is evaluated using a test network from Midtown Manhattan. Results indicate that the proposed method can efficiently estimate hourly average link travel times. This research provides new possibilities for fully utilizing the partial information obtained from urban taxicab data for estimating network condition, which is not only very useful but also is inexpensive and has much better coverage than traditional sensor data.