Optimization approach for planning urban on-street parking considering car and truck users

Planning on-street parking for cars and trucks in urban areas with limited curbside space poses significant challenges for decision-makers. This paper proposes a flexible, multi-criteria planning tool for allocating on-street parking that accounts for heterogeneous user needs. The problem is formulated as a Mixed-Integer Programming (MIP) model, suitable for applications where real-time information is unavailable, during initial planning stages, or when curbside space must be reallocated. The allocation criteria aim to maximize the level of service within the planning area, defined in terms of the number of users served, infrastructure utilization, and parking search impacts. Results show that explicitly modeling cars and trucks separately substantially alters optimal allocation outcomes, increasing truck service by up to 50%, reducing truck parking search time by 75%, and increasing the parking search component of the objective function by 8%. Sensitivity analyses further indicate that prioritizing user types yields moderate changes in performance (−3% to +9%), whereas shifting the planning emphasis between service and utilization versus minimizing parking search externalities leads to the largest improvements, increasing the overall level of service by up to 153%. These findings highlight the importance of accounting for user heterogeneity in curbside planning and demonstrate the usefulness of the proposed tool in supporting objective-driven parking allocation decisions.