travel behavior analysis based on mobile phone data

Understanding People’s Daily travel behavior patterns (such as spatial location, length of travel, travel order, travel patterns and type of activity) can support urban planning and traffic management. Many methods can be used to collect people’s travel data, such as GPS data, bus and Metro card data, resident Travel survey data, remote sensing data, credit card transaction data and so on. Each data has its own unique advantages, but mobile data can be used as a mobile sensor to capture resident travel data in real time. First, mobile cellular networks cover a wide range [1]. Second, as an essential communication tool in people’s daily life, mobile phones usually work all day, so the recording time is long. Again, mobile phone penetration is higher in life. With the exception of a handful of people who can’t afford a mobile phone or are too young to use it, almost all city dwellers can monitor it with mobile phones without the need for additional hardware costs [2].

1 data declaration

The content of mobile phone data simulated in this paper mainly includes user ID, Time , Latitude and longitude , cell number

2 data Processing

2.1 bit extraction the location data in the simulated original data is duplicated, and the statistics get the user coordinates. 2.2 Redundant data elimination due to unknown factors in the transmission process or communication system, redundant data will be generated. This part of the data can not be used for subsequent research, mainly manifested in two categories: one is the data collected information can not be analyzed, so it is deleted.
The other category is exception data.
2.3 Time Processing
Therefore, select one minute as the minimum time interval.

3 behavior analysis

through some statistics we can find the population gathering process. The start time of the trip is at 7:30 A.M… Before 9:30, large numbers of residents poured into urban centres and sub-centres. When the city center tends to be saturated, the aggregation phenomenon message at 11 o’clock in the morning. The main urban corridor connects the main urban centres with the sub-centres and their surrounding areas.
Aggregation can also be seen around the sub-centres, mainly due to the fact that the sub-centres have transport hubs to transfer regional residents. Aggregation process: from 8:00 to 12:00 noon, there was a gathering of people. Starting 8:00 The population gathers from all directions to the city center. 8:30-11:30 the fastest population gathering speed, after 11:30, the rate of population aggregation fell sharply, 12:30 the gathering process ended.

4 conclusion

Extract the characteristics of residents ’ travel behavior from large mobile phone data. Reveal the spatial structure of travel demand and depict the travel behavior pattern of urban residents is helpful for the government to make reasonable urban planning plan.