Mobile signaling data has the characteristics of low accuracy, large time interval and the existence of signal "ping-pong switching". In order to identify residents' travel Origin and Destination (OD) using mobile location data, a method based on Naive Bayesian Classification (NBC) was proposed. Firstly, according to the distance between places of residence and working, the travel log data measured by 80 volunteers for one month were classified statistically, and the conditional probability distribution of moving and staying states was obtained. Then, the feature parameters used to represent the user's states of moving and staying were established, including angular separation and minimum covering circle diameter. Finally, the conditional probability distribution of moving and staying states was calculated according to NBC theory, the processes with more than two consecutive moving states were clustered into travel OD. The analysis results on Xiamen mobile location data indicate that the travel time per capita obtained by proposed method has the Mean Absolute Percentage Error (MAPE) of 7.79%, which has a high precision, and the analysis results of travel OD can better reflect real travel rules.

Traditional theory-deduced route choice always has large deviation from the actual one in complex rail transit network. The signaling data were collected from the passengers' mobile phone in rail wireless communication network. According to these data, a new travel route identification algorithm was proposed based on normal location update. Meanwhile, concerning the data missing, a repair algorithm was also put forward by using other signaling data of users to deduce their actual travel route by the K shortest paths. And the route validity would be checked to get the actual travel route. Finally, typical application in Beijing rail transit network was selected to validate this algorithm. The application results show that the algorithm has a good performance in illustrating the actual travelers' travel behaviors.