## 2 Literature Review  
Many studies have performed travel time estimation by using different methods, including spanning trees, regression analysis, neural networks, and machine learning methods. In these studies, usable data were collected in advance to construct a prediction model to estimate the travel time. Prediction models have been constructed using various methods and algorithms. For delivery tasks with different departure times, intervallic travel time estimation is particularly helpful for scheduling future delivery tasks and managing these tasks in real time. Figure 1: Google Maps’ estimated travel time from the Taichung Industrial Park to Taichung Railway Station for immediate departure (31 min) (Source: Google Maps, accessed on 16 May 2023). Figure 2: Google Maps’ estimated travel time from the Taichung Industrial Park to Taichung Railway Station at 08:30 on the next day (20 min to 1 h) (Source: Google Maps, accessed on 16 May 2023). According to relevant research, collecting data is an essential step when addressing the travel time problem. Suitable algorithms can be used with large quantities of data to construct models for travel time estimation. The essential data that must be collected include departure location, departure time, arrival location, arrival time, and route taken in historical travel. These data may be sourced from manual documentation at the times of delivering out and receiving deliveries, the times shown on the driver's package-checking device, the coordinate data sent out from delivery vehicles' GPS devices, or charge-coupled device camera data on vehicle entries and exits at the loading or unloading locations. In this study, travel history generated on the basis of coordinate data were collected from the GPS devices of delivery vehicles to estimate the travel times between two points at different hours. Collecting the actual travel records of logistics fleets is difficult. Previous relevant studies have rarely used actual travel data for analysis, and most of them have adopted open data published by governmental institutions, such as the average travel speed and the speed limit of roads. The actual travel records of logistics fleets include records on delivered and received goods, which can include the names of the owners of the goods and information on the goods. Such information is unsuitable for open distribution and research. Therefore, this study used the coordinates recorded by the satellite positioning equipment on vehicles and eliminated vehicle identification data, such as vehicle plate numbers. Information on only the origin, departure time, destination, and arrival time was used in this study to obtain the travel times between two locations on a route during different periods. Numerous data samples were collected in this study, and unreasonable outliers were excluded to obtain the average travel time on a route during different periods. The average travel time can be used to estimate the travel time on a route during a specific period. To estimate the arrival time of vehicles, geographic information technology must be used to convert coordinates into locations. Rapid advances have been achieved in geographic information systems (GISs), and these systems are currently applied in different domains, particularly transportation and traffic. The conversion of a set of coordinates (x, y) into a location by a GIS is known as geocoding or map matching. Common map matching methods based on vector data include the puncture method and scanning method, which involve calculations. Large numbers of coordinates are converted into locations in transportation problems of large logistics fleets, and these coordinates cause a high load on the system and server. In this study, the number of GPS tracks was more than 65 million. Therefore, an excellent calculation method was required to analyze a large quantity of data in a short time. Many studies have been conducted on map matching. [PERSON] et al.