# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing  
This paper provides a detailed introduction to the source of the raw data and the reasons for selecting Chengdu taxi trajectory data as the research subject. It also discusses data preprocessing, which provides high-quality foundational data for the subsequent key issue of road passenger probability prediction.  
Taxi trajectory data are a type of spatiotemporal big data with high data quality, extensive collection range, and detailed records of spatiotemporal information characteristics. It contains rich spatiotemporal information, which can be analyzed to uncover the spatiotemporal travel patterns of residents, perceive urban traffic congestion, understand taxi driver driving experience, and analyze and predict taxi supply and demand balance based on demand. Currently, there is extensive research on taxi trajectory data mining. However, for data owners (taxi companies and ride-hailing platforms), there is a concern that taxi trajectory datasets may pose a risk of leaking personal travel information, especially sensitive fields such as user IDs, order costs, and driver incomes. Therefore, these datasets are not publicly available, making it difficult to obtain high-quality trajectory data [27].  
For mining urban residents' travel patterns, the more stable the temporal characteristics of urban travel, the more conducive it is to obtaining generalizable research conclusions. Therefore, this paper compares and selects trajectory data from Beijing, Shanghai, Guangzhou, and Chengdu, including data from the year 2020. The specifics are shown in Table 1.  
Under the dual impact of new transportation modes like ride-hailing and the COVID-19 pandemic, the taxi passenger volume in all four cities experienced varying degrees of decline compared to pre-pandemic levels. Chengdu saw the smallest decrease in taxi passenger volume, with a year-on-year decline of only 12.7%, demonstrating good representativeness in terms of the temporal stability of urban residents' travel patterns.  
Chengdu, located in the western part of the Sichuan Basin between longitudes 102\({}^{\circ}\)54\({}^{\prime}\) E and 104\({}^{\circ}\)53\({}^{\prime}\) E and latitudes 30\({}^{\circ}\)05\({}^{\prime}\) N and 31\({}^{\circ}\)26\({}^{\prime}\) N, covers a total area of 14,335 square kilometers, with an urban area of 1421.6 square kilometers. It is one of the central cities in China. As of the end of 2022, Chengdu administratively comprises 11 districts, including Jinjiang, Qingyang, Jinniu, Wuhou, and Chenghua; as well as five county-level cities, including Jianyang, Dujiangyan, and Pengzhou; and four counties: Jintang, Dayi, Pujiang, and Xinjin. This forms an administrative structure of 11 districts, five county-level cities, and four counties, as shown in Figure 1. Chengdu's permanent population and urban population are 21.192 million and 16.843 million, respectively, with an urbanization rate of 79.5%. The daily travel demand of urban residents is high. Chengdu has now essentially established an international inland comprehensive transportation hub and a modern integrated transportation system. Therefore, selecting Chengdu as the research area is highly representative. The location of Chengdu is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**City** & **Taxi Passenger Volume** \\ \hline Chengdu & In 2020, the taxi passenger volume in urban Chengdu was 190.843 million, a year-on-year decrease of 12.7%. \\ Shanghai & The daily passenger volume for cruising taxis was 1.164 million trips per day, down 81\% compared to the same period in 2019. \\  & Cruising taxis completed a passenger volume of 174 million trips in 2020, a decrease of 157 million trips from the previous year, representing a year-on-year decline of 47.4%. \\  & The daily passenger volume for cruising taxis was 1.06 million trips per day in 2020, a decrease of 34.9% compared to the same period in 2019. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of trajectory datasets.