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Open AccessArticle

CityGML-Based Road Information Model for Route Optimization of Snow-Removal Vehicle

1
Department of Civil & Environmental Engineering, Yonsei University, Seoul 03722, Korea
2
Department of Energy IT, Gachon University, Seongnam 13120, Korea
*
Authors to whom correspondence should be addressed.
Current Address: Olivet Institute of Technology, Olivet University, Anza, CA 92539, USA.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 588; https://doi.org/10.3390/ijgi8120588
Received: 24 November 2019 / Revised: 12 December 2019 / Accepted: 16 December 2019 / Published: 17 December 2019
Infrastructure usability becomes limited during a heavy snowfall event. In order to prevent such limitations, damage calculations and a decision-making process are needed. Snow-removal routing is a type of relevant disaster-prevention service. While three-dimensional (3D) models support these measures, they contain complex information regarding compatibility. This study generates a city-level semantic information model for roads using CityGML, an open standard data schema, and calculates the optimal snow removal route using this model. To this end, constraint conditions are analyzed from the viewpoint of a snow-removal vehicle, and a road network for an optimal route is applied to a 3D road information model. Furthermore, this study proposes a new algorithm that reduces the number of nodes used in the optimal route calculation, and a genetic algorithm is used to find the solution of the formulated objective function. This new algorithm reduces the number of nodes to less than two-thirds that of the original numbers when determining the optimal travel route for snow-removal vehicles in the target area. View Full-Text
Keywords: CityGML; road information; snow-removal route; optimization; traveling salesman problem CityGML; road information; snow-removal route; optimization; traveling salesman problem
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Park, S.H.; Jang, Y.-H.; Geem, Z.W.; Lee, S.-H. CityGML-Based Road Information Model for Route Optimization of Snow-Removal Vehicle. ISPRS Int. J. Geo-Inf. 2019, 8, 588.

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