Unique identification of vector geographic objects by location and geometry

Sun, Yizhong; Yao, Chen; Zhao, Shixiang; Xu, Mingzhu

doi:10.1080/13658816.2014.933482

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…Guan and Clarke [21] proposed using a parallel raster processing programming library to reduce the computation time when using spatial data. Sun 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.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

2023

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Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes.

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Section: Literature Reviewmentioning

confidence: 99%

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

2023

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“…Boundary algebra filling (BAF), which is based on the concept of integration, is an algorithm for a vector to raster conversion approach; moreover, the grid segment of the polygon boundary can be captured by BAF [5]. Common tree structures such as R-tree and Quadtree are discussed in [5,36]. R-tree is the solution used to overcome the limit that a node can only have two sub-nodes.…”

Section: Spatial Data Structurementioning

confidence: 99%

“…The Quadtree method provides a very useful data structure for many spatial database applications. Sun et al [36] proposed a unique identification method using a Quadtree grid, and Yao and Li [18] listed other progressive techniques such as Hadoop architecture for Quadtree.…”

Section: Spatial Data Structurementioning

confidence: 99%

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Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

Chou

Hoang

et al. 2021

IJGI

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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.

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Unique identification of vector geographic objects by location and geometry

Cited by 2 publications

References 16 publications

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

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