## 2 Literature Review
They proposed the match-and-assign algorithm and compared its performance with that of other transportation algorithms. [PERSON] et al. [28] used vehicle trajectory data on an expressway to analyze the effectiveness of time scales, tolerance, and series length for the estimation of travel time. They also compared the prediction results obtained using the autoregressive integrated moving average method and a backpropagation neural network. [PERSON] [29] used k-nearest neighbor method to investigate the travel time on highways. They analyzed and compared the traffic information collected by an electronic toll collection system and vehicle detectors. [PERSON] examined how the traffic data were affected by the time and vehicle detector. The aforementioned studies used different methods within a specific period to perform research, and few studies have estimated the travel time between the same origin and destination in different periods. Therefore, in the present study, the travel times at different periods were determined. [PERSON] et al. [30] proposed that reliability be estimated according to the conception and estimation of reliability under the framework of classical test theory. On the basis of this suggestion, the present study determined a reference ratio for filtering data according to the data frequency and the difference between the data collected by this study and those obtained from the target fleet's electronic map. In this study, track logs, which included the coordinates recorded by the satellite positioning equipment on vehicles, were used. After spatial judgment, the track logs included information on the origin, departure time, destination, and arrival time. [PERSON] et al. [31] proposed an advanced calculation method for determining the accuracy and error of the coordinates determined through satellite positioning. This method reduces the ionospheric delay, tropospheric delay, and satellite clock error of the Global Navigation Satellite System (GNSS) at the cost of increasing the computational complexity. [PERSON] et al. [32] indicated that the radio signals of the GNSS have extended routes because they are reflected and refracted, Therefore, the measured distance changed, and multipath errors occurred. The aforementioned authors found that multipath errors and ionospheric delay have strong effects on the precision, and they proposed using choke ring antennas to reduce multipath errors. In this study, the travel time was investigated, and all the coordinates in the track logs were collected from the same satellite positioning equipment and expectedto have the same errors. Therefore, the errors were expected to cancel each other out; thus, no corrections were made to any GNSS coordinate. Studies that require precise positioning may consider using correction mechanisms for the aforementioned errors. [PERSON] et al. [33] used the machine learning method to predict the online taxi-hailing demand, and use RMSE, MAPE, and R square to evaluate the performance. [PERSON] et al. [34] used machine learning technology for research on correlations between the COVID-19 pandemic with socioeconomic, food access, and health problem variables by applying linear and nonlinear regression models. The GIS analysis was also for comparison. The results of machine learning and GIS had the same directions and indicated the correlation between the dependent variables and independent variables. The source data of this study are GPS driving records, and the travel time records obtained from the analysis are from GIS data. Regarding [33; 34], this study uses MAPE to verify the prediction effects based on the regression model. [PERSON] and [PERSON] [35] used ten machine learning algorithms to plan the predictive maintenance strategy of the sewer network and used a ratio of 80/20 to split the data set, 80% for building the model, and 20% for validating and selecting the sewer condition assessment model.