## 5 Discussion  
The logistics operation of convenience stores is limited by the location of the store and different goods must be delivered at the designated time. When arranging the route, the dispatchers cannot choose the roads or periods without traffic jams. If the travel time between locations can be predicted, it can be useful for route arrangement and unloading preparation operations. Thus, this study used GIS spatial technology to obtain the travel time records from the GPS driving records and then used machine learning to perform the average travel time. This study also added standard deviation, sample, and reference ratio we designed to filter the data to ensure the quality of the prediction results. The nonlinear regression equation is used to supplement the period of unavailable data, and the actual driving time is used to validate the predicted model. This study estimated travel times between two locations at different hours, which are particularly helpful for scheduling future delivery tasks. In the proposed approach, the total time required to complete deliveries at convenience stores along a route is calculated by summing the travel time between convenience stores and the unloading time at each store. The result is then used to schedule the following delivery route. The proposed method also reveals estimated arrival times, the knowledge of which allows convenience stores to make room in advance for delivery vehicles to park, unload, and complete other work. However, additional data might be required for intervallic records. The fleet investigated in this study did not use GPS on their vehicles until 2021; therefore, the study data (approximately 65 million travel records) were collected from April 2021 to March 2022. Future studies can expand their samples to enhance the representativeness of their results. This study focused on travel time discussion. The task clustering and travel route (arrival order) are excluded from this article. The future study will explore two crucial topics in depth in a separate study. In practice, the arrival time might vary because of weather conditions, traffic, and accidents. The influence of these unexpected events on travel times was not considered in this study. Future research can develop approaches to recommend alternative routes that minimize the additional travel time required in case of unexpected events. Figure 10: Plot of validation result. According to the results of this study, each combination of departure and arrival points usually had a few periods of the day in which the numbers of journeys were distinctly higher than those during the other periods of the day, and each period usually lasted for a few hours. For example, if the journeys between a specific combination of departure and arrival points are concentrated around two periods, namely the most common time 06:00-08:00 in the morning and the other time 13:00-15:00 in the afternoon, the travel times during the same period can be considered the same as each other but different from those during the other period. Accordingly, future research with larger samples can use 3 h as a time unit to simplify the relevant calculations. This study examined the travel times between warehouses and convenience stores or between convenience stores and thus is applicable to travel time estimation between fixed departure and arrival points. Future studies can expand on this study to estimate travel times between a warehouse and any given point for application in home delivery fleets. Studies can also divide a map into different areas, with each area being represented by a crossroad, and then identify the area that the target location belongs to so that they can estimate the travel time from the departure point to the corresponding crossroad and then sum it with the estimated travel time from the crossroad to the target location for obtaining the total travel time.