With the rapid growth of the logistics and delivery industry, there has been a growing demand in recent years for delivery planning that balances safety and efficiency. In this paper, the authors propose a safe and efficient delivery planning method based on road characteristics, taking into account the risk level and distance of each road segment calculated using telematics data. The proposed method models the actual road network as an undirected graph, assigns distance and risk levels to each link, and constructs a mathematical model that optimizes the actual routes traveled by delivery vehicles. To reduce computational load, the authors introduce delivery area segmentation via clustering and perform route optimization using a hybrid genetic algorithm (GA). Simulation results on a large-scale road network covering central Fukui City showed that the proposed method can formulate delivery plans appropriately considering both driving distance and risk levels by adjusting the weighting coefficients. Furthermore, through comparison with the previous method, the authors confirmed that the proposed method can reduce a computation time even for large-scale networks.
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