摘要：AbstractPurposeThis paper assesses the parking needs of freight and service related commercial activities and identifies the role of demand management in mitigating these needs.MethodsTo provide a context for the analyses, the authors selected two small commercial areas of about the same number of commercial establishments—one in Troy, NY, and the other in New York City—and applied freight and service trip generation models to estimate the total freight and service traffic generated at these sites. Then, using different assumptions of the amount of time these vehicles spend at a parking location, the authors estimated the number of parking spaces required by time of day under different assumptions of demand management.ResultsThe results show that parking needs are proportional to the average parking durations. Essentially, the longer the duration the higher the parking needs. In terms of impacts on demand management, the results show that the 100% Off-Hour Deliveries (OHD) program is expected to be the most impactful as it reduces the parking needs by 70–80% during peak hours. In second place, Staggered Deliveries reduces parking needs by about 60% during the peak hours. The third place is occupied by the 30% OHD Scenario and the Receiver-Led Consolidation programs, which are virtually tied, offering about 10–25% reduction.ConclusionsThe initial analysis revealed the importance of parking duration as it was shown to be proportional to parking needs; the longer the duration the higher the need for parking. The delivery simulation further bolstered this finding by showing that the optimal case occurs (i.e. minimizing parking duration) the closer the parking location is to the establishment. The further away the vehicle is parked the longer the walking time to the establishment, hence increasing the time the vehicle occupies the parking spot. The strategies applied to the case studies showed that Transportation Demand Management (TDM) strategies are effective in decreasing the number of parking spots needed during peak periods.
