In the 1990s, scholars in different fields used road traffic volume to calculate the OD travel matrix. Fan pointed out the limitations of the optimization model represented by the maximum entropy method and proposed the Bayes statistical method [
1]. Li et al. [
2] proposed an iterative backstepping method based on a multi-path probability allocation model. However, these methods still have large errors. To realize low-cost and high-precision surveys of residents’ travel surveys, Shi [
3] uses public bus IC card data to build a resident travel information database, infers the cardholder’s starting and ending points of public bus travel, and obtains the passengers of the bus route travel OD matrix. These methods of obtaining the travel OD matrix have made some progress, which has changed qualitative decision-making in the past. The country has begun to provide a basis for urban traffic management and planning through a quantitative analysis of various travel characteristics and needs. Li et al. used taxi OD data in Suzhou, Jiangsu Province, China to construct an index system and analysis method for taxi travel characteristics, time, and space distribution [
4]. Wu et al. [
5] predicted the spatial distribution of various parking needs based on vehicle ownership situations and vehicle travel OD. Lu et al. used the effective data of public transit OD trips in five typical cities of Weifang, Changzhou, Shangyu, Bengbu, and Huzhou to study the temporal and spatial distribution characteristics of residents’ public transit trips by using graphics processing and model calibration methods to provide a quantitative basis for urban public transport demand forecasting and public transportation network planning [
6]. The traffic travel OD matrix is also applied to the analysis of urban public transportation, rail transit, and urban agglomeration intercity railway passenger flow [
7,
8,
9].
With the development of communication and information technology and the popularization of mobile phones, obtaining travel OD through mobile phone data provides new ideas for transportation planning. Ma established a mobile phone traffic information collection system, using Java language to develop a J2ME-based intelligent collection of traffic travel behavior information embedded software, to realize the function of mobile phone SMS collection of travel information, and obtain various road section traffic information, dynamic travel OD matrix, etc. [
10]. CDMA is a spread spectrum multi-access digital communication technology that has the advantages of strong anti-interference ability, broadband transmission, and anti-fading ability. GPSOne positioning technology is a system pushed out of the shortcomings of improving GPS positioning technology, compared with traditional GPS, which has higher accuracy, short positioning time, and can achieve indoor positioning. Huang et al. obtained the starting and ending point position information through continuous position tracking based on the CDMA communication network and GPSone cell phone location technology. In the Yangjiaping area of the Jiulongpo District, Chongqing City, they used the area judgment algorithm in the algorithm to obtain travel OD information on the basis of existing administrative divisions [
11]. Based on the travel chain of cell phone location information, Wu recognizes OD points through the limitations of speed threshold, time threshold, and distance threshold [
12]. In the area covered by the communication network, a specific location is usually covered by the signal overlap of several surrounding base stations. The base stations connected by mobile phones in this area will continue to change with the change in signal strength of each base station, resulting in a “ping-pong effect” phenomenon of switching between two or more base stations. In addition, to ensure the efficiency and quality of mobile phone user communication, service base stations often have a load optimization adjustment mechanism. 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. Tang 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]. Hu 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]. Shan 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, Sha 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. Jia 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].