Abstract
The usage of mobile phones has undergone tremendous growth in the past decades. Large amounts of mobile-phone signaling data (MSD) are generated while using various mobile phone applications. The large-scale MSD presents opportunities for transport planners to utilize it for better planning and management of the transportation system. In this paper, we use MSD to analyze subway passengers’ travel behavior and extract their complete travel trajectories. The complete travel trajectories of subway passengers include their trajectories, both inside and outside the subway system. In the first stage, the MSD from the subway base stations is selected, sorted by time, and the rough trajectory in the subway system is extracted. The ground base stations around the subway station are then considered to correct the boarding and alighting subway stations in order to obtain a more detailed trajectory. In the second stage, the service range of the base station is determined according to the Thiessen polygon, and a temporal dynamic threshold is proposed to extract the passenger’s stop point outside the subway system. Finally, the complete trajectories of subway passengers are obtained. The proposed algorithms are verified using a set of MSD collected in Suzhou, China. The results show that the proposed algorithms can effectively extract the complete travel trajectory of subway passengers.
Highlights
With the rapid development of information and communication technologies, we have entered the era of big data
We propose a new algorithm to extract the complete trajectory of subway passengers, which contains the space-time information of the origin and destination outside and inside the subway system. e contributions of this paper are twofold
mobile phone signaling data (MSD) data is collected by telecommunications companies. e data consists of the user’s unique code and position information. e position is recorded as the base station ID
Summary
With the rapid development of information and communication technologies, we have entered the era of big data. Past travel behavior studies have applied mobile phone data to detect stops and extract trips. Geographic information matching algorithms can be used to identify the transfer routes and stations by using MSD and subway-related data [22]. In addition to MSD, Wifi data can accurately detect subway passenger flow and obtain the behavior of entrance, transfer, and exit. We propose a new algorithm to extract the complete trajectory of subway passengers, which contains the space-time information of the origin and destination outside and inside the subway system. E second section describes the specific problems and the relationship between the passenger travel trajectory, MSD, and mobile phone base stations. The discussion section is followed by the conclusion and recommendations
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