# Planning Urban Internal Transport Based on Cell Phone Data
## 2 Background and Related Works
When the load of the base station adjacent to the mobile phone is large, the mobile phone signal will be automatically switched to the base station, which is farther away and less loaded, resulting in signal drift. At the same time, the difference in the distribution density area of the base station may cause defects in the mobile phone positioning data in some areas. These problems reduce the accuracy of the phone's data. [PERSON] obtained the residents' travel OD matrix to reduce the occurrence of such problems based on \"space-time \" the two-level clustering stop-point recognition algorithm, and analysed the spatial distribution characteristics of OD passenger flow with ArcGIS [13]. Researchers have also summarized different methods for initial filtering and screening of positioning offset, over-range, and \"noise\" data in cell phone data, and combined them with the area to determine the travel chain and obtain accurate travel OD [14; 15]. Mobile data processing technology continues to develop, and the accuracy of the analysis results has also improved. It is widely used to analyze the current situation of crowd travel characteristics, urban commuting, work, residence space, etc. [16; 17]. [PERSON] extracts the travel characteristics data of the current urban residents, obtains the permanent population and job position distribution, the residents' travel rate, the total number of trips, and the OD distribution, and obtains the actual travel needs of the current urban residents [18]. [PERSON] et al. analyzed the spatial relationship of the work-resident contact strength of permanent residents in the Jiangbei area through the Harbin cell phone data, the commute OD during the morning and evening peak hours of the working day, the current coverage of conventional buses, and the distribution of cross-river traffic, and finally proposed optimized traffic planning strategies [19]. Based on the cell phone data of Hubei Mobile users, [PERSON] et al. established an algorithm model to analyze urban population distribution, rail transit passenger flow distribution, and regional passenger flow [20]. Foreign studies have proposed a mobile space dynamic method based on mobile network cloud travel data to estimate the starting point-midpoint journey to verify the reliability of the origin and destination travel between regions [21], and the concept of urban population flow based on the number of individuals entering and leaving a certain area per unit time. Based on the cell phone collection data, the human body trajectory grid reconstruction method is used to estimate urban crowd flow and guide traffic planning [22]. Cell phone data have the advantages of high information integration, large sample size, real-time, and high authenticity that is not affected by subjective factors and is gradually applied to future traffic demand forecasting and road planning. Some scholars obtain the OD matrix of residents' travel between different areas through mobile phone positioning data, then set different distance time thresholds, transfer times, and other constraints, and build a bus route planning model that calculates the bus planning route [23; 24], based on the method of dividing travel transportation by the advantage of travel distance and cost. [PERSON] used the map analysis software Transcad to obtain the bus travel matrix and forecast the public transportation demand [25]. Foreign research has developed a mobile phone location tracking algorithm from a large mobile phone network database to estimate dynamic origin-destination (od) traffic flow and travel demand data, as well as commuter traffic, and to estimate dynamic traffic demand for public transportation system planning and scheduling [26]. It can be seen from the above research status that many scholars have proposed methods for obtaining traffic travel characteristics from mobile phone data from multiple angles, but the content lacks a specific description of the methods for obtaining traffic travel characteristics (Table 1).