Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.