Currently, many types of sensors, such as cameras and radars, have been widely deployed on road networks according to historical accident data for traffic accident detection and prevention; however, the uncertainty of traffic accidents and finite historical accident data will induce the bias of sensor deployment. As a result, engineers have to adjust the locations of sensors many times to pursue better accident detection and prevention performance in practice. This will cause a large number of engineering costs and can hardly realize the optimal reallocation of sensors. To address the problem, an optimal reallocation method of heterogeneous sensors is proposed. In the method, to reduce the influence of accident uncertainty under finite historical accident samples, optimal transport is introduced to estimate the stable spatial distribution of accident risk. Then the sensor reallocation problem is reduced as a coverage enhancement problem on the stable spatial distribution of accident risk. The optimal reallocation model of heterogeneous sensors is constructed to optimize the type, number, and location of reallocated sensors by maximizing the coverage quality with the constraint of cost-effectiveness, the number of existing heterogeneous sensors, and so on. And the bisection method and AO-GI-MO algorithm are combined to solve it. In experiments, stability, consistency, reliability, and comparison experiments are carried out to validate the proposed method by using international open traffic accident data. The results show the proposed method could improve the detection performance of traffic accidents effectively.