Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for vehicles remains a challenging task. To the best of our knowledge, none of the previous research considered using on-road taxicabs as mobile video surveillance cameras and road traffic flow patterns, therefore not suitable for recovering trajectories of vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose an approach to calculate possible location and time distribution of the vehicle, select the taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.