Leveraging Generative Design and Point Cloud Data to Improve Conformance to Passing Lane Layout

Area of Science:

• Transportation Engineering
• Computational Design
• Road Safety

Background:

• Inadequate highway design contributes to traffic accidents, necessitating adherence to safety standards.
• Existing highway design guidelines address critical factors like road geometry, signage, and lane markings.
• Computational methods are crucial for optimizing highway design for safety, capacity, efficiency, and sustainability.

Purpose of the Study:

• To develop and evaluate a computational method for designing highway passing lanes using a logic-based language.
• To integrate generative design principles within the Building Information Modeling (BIM) software toolkit.
• To assess the impact of generative design on rule compliance and efficiency in real-world highway projects.

Main Methods:

• Utilized a user-friendly, logic-based language to encode highway design rules for passing lanes.
• Employed generative design techniques within the BIM-kit software toolkit for automatic design evaluation and generation.
• Applied the developed approach to 16 real-world passing lane designs in Alberta.

Main Results:

• Rule compliance for highway passing lane designs increased significantly from 61.82% to 91.31% after applying generative design.
• Demonstrated the practical utility of the computational approach in transportation engineering.
• Showcased the ability of the method to generate innovative and compliant highway design solutions.

Conclusions:

• Generative design, integrated with BIM, offers an efficient tool for enhancing highway safety and compliance.
• The developed logic-based rule encoding and generative design process significantly improves adherence to design standards.
• This approach provides transportation engineers with a powerful method for creating optimized and compliant highway infrastructure.