Among these data sources, the application of mobile phone data has shown more potential for the extensive popularity of mobile phones, even in the developing countries. have been applied to characterise human activity. In recent years, position data from social networks, mobile phones, GPS etc. This paper aims to provide a new method for predicting the trip modes in the transportation system based on the mobile phone data, which will be helpful for transportation planning and management. It also provides useful technical support for tracking the temporal and spatial characteristics of trips. This new data collection and analysis method can reduce the investigation time largely meanwhile, it makes the characteristic analysis of urban personal-trip more effective. In addition, analysing the mobile phone data deeply can get the mode choice and other transportation-related results. Hence, the mobile phone data can be seen as a supplement to the existing traffic data acquisition technique. Owing to the popularisation of a mobile phone, a large sample of data including the identity, trip OD, travel path and time distribution can be obtained and analysed at a comparatively low cost. At the end of 2017, the number of smart mobile phone users in China is 0.753 billion. Nowadays, mobile phones have recently experienced a rapid growth and played an important role in people's life. With the fast development of sensing, computing, and network techniques, emerging data sources from the mobile devices have shown promise in helping the transportation professionals to better understand the people's movements through space and time. These methods have a few disadvantages of long-time consuming, complex data processing, and high implementation costs.

Usually, trip mode data is acquired by the origin–destination (OD) data collection. Trip mode distribution estimation is a challenging task, particularly in developing countries, mainly due to costly and time-consuming data collection requirements. The potential of using the mobile phone data to build a new mode choice prediction method in the field of transportation is shown. Finally, the results of the case study show that using a 30-point moving average training data set can improve the prediction accuracy largely, and the SVM method gets a better accuracy of 82%. Furthermore, the classifier method for mode choice prediction is developed by support vector machines (SVMs) and back propagation neutral network. Then training samples are drawn by two data selection methods including probability proportional to size sampling and equal amount sampling. Compared with the wave characteristics, the moving average method shows a better accuracy of 90%. Considering the differences in velocity and acceleration of different trip modes, a trip mode characteristic description model is built based on wave characteristics and moving average method. First, the effective mobile phone singling data and GPS data are collected from the communication operators and a mobile phone app, respectively. This study provides a methodology to identify travellers’ transportation modes by tracking the mobile phone data, which aims to obtain the accurate mode split rate for providing decision support in urban traffic planning.

IET Generation, Transmission & Distribution.

IET Electrical Systems in Transportation.

IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.

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