Improving Urban Cycling Safety and Comfort through Optimized Infrastructure Upgrades

This paper proposes a bi-level optimization framework to improve urban cycling infrastructure by upgrading bike lanes’ degree of separation from motorized traffic. The six-level degree of separation classification reflects increasing safety and comfort as cyclists’ exposure to annual average daily traffic decreases. The lower level estimates cyclist flows using an all-or-nothing assignment method that accounts for factors like slope, angular changes, and traffic, ensuring realistic flow distribution. The upper level minimizes traffic exposure by upgrading segments to higher DoS levels within a fixed budget. A case study on Grenoble’s cycling network applies this framework using a modified genetic algorithm to optimize safety, comfort, and budget efficiency. Results are compared to a specialized algorithm designed to improve overall perceived safety and comfort. Findings highlight that targeted infrastructure upgrades enhance both local and network-wide safety, better balancing cyclist flows. This framework provides urban planners with a data-driven tool for prioritizing cycling infrastructure investments.