Promoting viable alternatives to motorized trips is crucial in moving away from car dependency and reducing the number of vehicle-kilometers travelled. Among these alternatives, cycling has proven to be an effective mode of transport to replace motorized vehicles for certain trip types, provided that adequate bicycle infrastructure is in place to support this modal shift._x000D_ _x000D_ However, many bicycle networks are either too sparse or poorly connected, resulting in unsafe or inefficient routes that deter potential users. Budgetary constraints also limit the amount of new infrastructure that can be built to improve existing networks._x000D_ _x000D_ This study addresses these challenges by proposing an algorithmic approach to cycling investments prioritization, aiming to maximize network productivity. The methodology focuses on identifying currently infeasible trips on a given network due to safety concerns or excessive detours. The network is then strategically expanded such that it minimizes these downsides and enhances overall connectivity and usability. Montreal's bicycle network is used as a case study, leveraging publicly available network data and trip data from the region's origin-destination survey. By systematically optimizing infrastructure investments, this research offers a practical framework for improving bicycle networks that can be used by city planners to support a modal shift towards cycling.
