# Planning Urban Internal Transport Based on Cell Phone Data
## 2 Background and Related Works  
In the 1990s, scholars in different fields used road traffic volume to calculate the OD travel matrix. [PERSON] pointed out the limitations of the optimization model represented by the maximum entropy method and proposed the Bayes statistical method [1]. [PERSON] 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, [PERSON] [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. [PERSON] 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]. [PERSON] et al. [5] predicted the spatial distribution of various parking needs based on vehicle ownership situations and vehicle travel OD. [PERSON] 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. [PERSON] established a mobile phone traffic information collection system, using Java language to develop a J2 ME-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. [PERSON] 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, [PERSON] 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.