To obtain the transfer information of passengers' public transit behavior

a public transit transfer recognition method is designed based on multi-class support vector machine (multi-class SVM). GPS data and intelligent card data are fused to get sufficient samples

then the Multi-class support vector machine model is used to train the samples. The best sample size could be acquired by accuracy control

and the Grid-Search method combined with Particle Swarm Optimization method is employed to determine the parameter for gaining the optimal SVM model. Finally

a case study with GPS data and intelligent card data in Foshan city is conducted to verify the algorithm

this method can acquire transfer characteristics including transfer flow and transfer proportion etc. Results show that the proposed method could complete public transit transfer recognition with high classification accuracy even if the size of training sample is rather small. Especially

it is useful for transfer recognition in large cities with complex public transit networks

which provides a basis for public transit lines planning and pub selection.