## 2 Literature Review
[3] proposed the conversion of vector data into raster data and the use of a look-up table for rapidly finding the location corresponding to a set of destination coordinates from each pixel in the raster data. Their method was more efficient than the conventional puncture method. [PERSON] et al. [4] proposed a map matching algorithm based on the coordination between user location and basic maps. [PERSON] et al. [5; 6] integrated GPS data with dead reckoning data to develop a map matching algorithm with the extended Kalman filter and the spatial digital database of a road network. [PERSON] et al. [7] used a map matching algorithm with the space-time prism and k-shortest path algorithms. [PERSON] et al. [8] proposed a map matching system based on the evaluation of similar trajectories. [PERSON] et al. [9], [PERSON] and [PERSON] [10], [PERSON] and [PERSON] [11], and [PERSON] et al. [12] have proposed different improvements for map matching based on road filters and road trajectories. In this study, OpenStreetMap (OSM) was used as the reference for road data. [PERSON] et al. [13] and [PERSON] et al. [14] have performed comprehensive research on the usage of road data and geographic information in OSM. Increasing the data calculation and processing speed is crucial when performing statistical analysis on a large quantity of data. [PERSON] and [PERSON] [15] proposed the use of big spatial vector data to improve the efficiency of data storage and organization, spatial index, data processing, and spatial analysis. [PERSON] et al. [16] proposed using massive floating car data for traffic surveillance to solve data-intensive geospatial problems for urban traffic systems. [PERSON] et al. [17] proposed using MapReduce to improve the computation performance of spatial data models. [PERSON] et al. [18], [PERSON] et al. [19], and [PERSON] et al. [20] have proposed using spatial regression models, geo-visualization techniques, and vehicle position management to construct spatial and transportation models and improve the computation performance when using spatial data. [PERSON] and [PERSON] [21] proposed using a parallel raster processing programming library to reduce the computation time when using spatial data. [PERSON] et al. [22] used a spatial index based on raster data and extracted the geometric features of geographic objects corresponding to the raster to improve the positioning, searching, and querying performance. Concerning studies related to travel time prediction, [PERSON] et al. [23] proposed a wide deep recurrent learning model, which comprised train-wide models, deep neural networks, and recurrent neural networks, to estimate the travel time for a fixed route and departure time. [PERSON] et al. [24] proposed an explainable artificial intelligence method to evaluate and compare different prediction models and to explain the effects of temporal and spatial characteristics on travel time. [PERSON] et al. [25] used extremely randomized trees, adaptive boosting, and support vector regression (SVR) to solve problems with a small quantity of data in a short time. [PERSON] and [PERSON] [26] conducted short-term travel time prediction and examined the prediction accuracies of the Regional Integrated Transportation Information System, decision trees, random forest, extreme gradient boosting, and a long short-term memory neural network for the spatial and temporal characteristics of travel time. [PERSON] et al. [25] used machine learning, extremely randomized trees, adaptive boosting, and SVR to construct models for travel time prediction. They then evaluated a model established for predicting multimodal container transport between the United States and Germany. Building on their research, this study conducted a regression analysis to estimate the travel time matrix for periods in which travel history was unavailable. [PERSON] and [PERSON] [27] determined the minimum travel time and minimum total latency when serving multiple requests.