Abstract: Commuter passengers are people who travel during the rush hours in the morning and evening and have a regular pattern of travel. Accurate identification of commuter crowds from bus credit card data is of great significance for taking measures to alleviate traffic congestion during the rush hours in morning and evening and for overall urban line network planning and adjustment. Based on the data of Zhuhai bus IC card, this paper proposes a public transportation identification method based on differential evolutionary algorithm to optimize support vector machine (SVM). Firstly, the commuter passenger survey data are combined with the actual swipe data to analyze the characteristic attributes of commuter passenger travel. Then the SVM algorithm is used to build the classification recognition model, and a differential evolution algorithm (DE) is used to optimize the parameters of the SVM to obtain the optimal identification model, whose recognition accuracy is as high as 94.28%, better than other algorithm models. Finally, the model is used to identify the commuters in the Zhuhai bus IC data. The results show that the number of public transportation personnel is 178,259, accounting for 21.47% of the total number of bus trips.

Key words: urban traffic, bus IC card, differential evolution, SVM, commuter passengers