A Travel Mode Identification Framework Based on Cellular Signaling Data

Abstract

The rapid development of telecommunication network has produced a large amount of spatial-temporal information of mobile phone users. GPS data are typically collected by smartphones apps, which are restricted to small samples of the population. Cellular signaling data (CSD) are usually collected by mobile network operators, which enables researchers to conduct travel behavior analysis of the entire population at a relatively low cost compared to GPS. However, extracting travel mode information from CSD is particularly challenging due to the noise data and low positioning accuracy. This paper proposes a travel mode identification framework based on CSD, which includes data cleansing and travel mode identification. In terms of data cleansing, oscillation sequence and drift data are mainly cleansed. For the oscillation sequence, this paper proposes a detection algorithm based on time window. For the drift data, this paper proposes a detection algorithm based on distance, velocity and frequency. In terms of travel mode identification, the task is divided into two dichotomous problems: motor and non-motor transport identification and public and private transport identification. Each dichotomous problem proposes an algorithm that does not rely on the ground truth dataset for model training. Finally, a ground truth dataset is constructed to verify all algorithms. The result shows that, in terms of data cleansing, the similarity between CSD after cleansing and the actual trajectory according to DTW improved by 101.13% on average. In terms of travel mode identification, the proposed method can achieve similar or even better accuracy than traditional supervised-learning algorithms (94% in motor and non-motor transport identification, 83.5% in public and private transport identification), which can be directly applied to large-scale population analysis scenarios.