As the concept of the"green railway"gains momentum within the dual-carbon economy framework,the incorporation of"green ecology"into the enhancement of railway planar alignment has emerged as a significant area of research interest in recent years.This study focused on achieving coordinated optimization between railway construction expenses and ecological impact costs.For this purpose,an autonomous driving navigation algorithm(Hybrid A*algorithm)was introduced and modified to effectively tackle intricate railway design challenges.Paramount to the study is the consideration of critical railway alignment constraints which include the minimum and maximum curve radii,the shortest permissible length of curves and transition curves,and the requisite lengths for transition curves.The findings suggest the following:(1)The modified algorithm incorporates external environmental influences in a discrete grid approach,facilitating advanced global exploration and yielding railway alignment designs that approach the global optimal solution.(2)This technique,when applied in the presence of intricate external environmental constraints,eliminates the need for predefining horizontal intersection points and their quantities.Instead,it can autonomously develop optimized planar alignment plans that satisfy the interdependent restrictions of alignment and environment.
railway line designhorizontal alignmentgreen ecologyHybrid A* algorithm
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