A planning tool for improving the provision of loading docks in cities
For freight and service vehicles entering a city, parking plays a crucial role in providing access to serve customers. This typically occurs on the street in designated parking spaces or modern buildings in off-street facilities. Given the evolving priorities in urban planning and the consequent reduction in on-street loading zone availability, this paper delves into the provision and challenges associated with off-street loading docks to facilitate freight and service-related activities in major urban centers.
Despite not always being fully acknowledged, ensuring proper accommodation for the freight tasks generated by a city is imperative for the broader objectives of urban planners. As an essential transport task, freight vehicles will inevitably continue to enter cities. Without well-designed off-street loading dock facilities, these vehicles may seek on-street parking, whether legally or illegally, potentially compromising the place-making objectives of urban planners.
A recent paper initially examines the planning approaches governing the establishment of loading docks, specifically focusing on Sydney while drawing comparisons, primarily with the Borough of the City of London. Drawing on recent observations in Sydney, the paper explores various stakeholder perspectives regarding loading dock provision and utilization. The paper outlines modeling approaches to forecast more accurate requirements to help address planning challenges. Ultimately, the paper highlights actions taken to tackle the identified challenges.
A predictive modeling methodology has been developed using regression and clustering analysis techniques to overcome the lack of confidence in current methods for forecasting freight demand at new major generators. This methodology promotes a versatile and scalable model input system, allowing the incorporation of various datasets and variables, such as different land-use types and vehicle categories. This approach proves valuable and applicable to the most prevalent forms and sizes of land use.
It’s important to note that the predictive model isn’t tailored to a specific building form or size. The versatility of the environment/platform modeling enables externalization, making the model accessible to relevant external stakeholders. Moreover, the inclusion of a diverse and representative set of buildings encompassing various land-use types, including “commercial, residential, and retail,” in the gathered parking surveys and model inputs facilitates the generation of valid and reliable estimates for the most common building types and sizes found in Central Business Districts (CBDs). The developed methodology allows for ongoing surveys to update the models, accommodating changes in demand patterns.
