Localization of Relevant Urban Micro-Consolidation Centers for Last-Mile Cargo Bike Delivery Based on Real Demand Data and City Characteristics

Abstract

The use of cargo cycles for last-mile parcel distribution requires urban micro-consolidation centers (UMC). We develop an approach to localize suitable locations for UMCs with the consideration of three criteria: demand, land use, and type of road. The analysis considers metric levels (demand), linguistic levels (land use), and cardinal levels (type of road). The land-use category is divided into commercial, residential, mixed commercial and residential, and others. The type of road category is divided into bicycle road, pedestrian zone, oneway road, and traffic-calmed road. The approach is a hybrid multi-criteria analysis combining an Analytical Hierarchical Process (AHP) and PROMETHEE methods. We apply the approach to the city center of Stuttgart in Germany, using real demand data provided by a large logistics service provider. We compared different scenarios weighting the criteria differently with DART software. The different weight allocation results in different numbers of required UMCs and slightly different locations. This research was able to develop, implement, and successfully apply the proposed approach. In subsequent steps, stakeholders such as logistics companies and cities should be involved at all levels of this approach to validate the selected criteria and depict the “weight” of each criterion.

‘Proximity logistics’: Characterizing the development of logistics facilities in dense, mixed-use urban areas around the world

“…They relate closely to one of the archetypes and are specifically of interest for developments in dense, mixed-use urban areas. This facility type also covers urban consolidation centers (Dupas et al, 2020;Marujo et al, 2018;Rudolph et al, 2021); micro-consolidation centers (Janjevic and Ndiaye, 2014;Marujo et al, 2018;Rudolph et al, 2021); micro-hubs which can be independent, shared or consolidated (Kim and Bhatt, 2019;Rudolph et al, 2021;Russo et al, 2021;Schodl et al, 2019); and mobile hubs (Arvidsson and Pazirandeh, 2017;Sheffi, 2020;Srivatsa Srinivas and Marathe, 2021;Verlinde et al, 2014).…”

Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study

“…The main algorithms applied to perform clustering analysis were K-means [16], [17], [26] in travel activity from taxis and bikes and to find the places that gave rise to shorter travel distances, and DBSCAN was implemented to found travel paths made by users of public transportations [18] and to study private car trajectories [21]. In the decision taking for hub location, the two principals algorithms implemented were the Genetic Algorithm [6], [27] and PROMETHEE [26], [28].…”

“…Finding and defining a location for micro hubs is an important and complex task [6], [14], the rising costs of urban land, lack of adequate infrastructure, changing demand, changing city characteristics [28] and regulatory requirements [14], do not ease the task of being able to find an optimal solution that minimizes operating costs and impact on communities. The most common characteristics addressed in the literature to study this problem are demand (e.g., residential, commercial, and/or employment density), infrastructure considerations (e.g., pedestrian/bicycle infrastructure provision, road classifications, pedestrian zones, and measures to assess traffic), and land use constraints [12],…”

“…When the deliveries are made by cargo bikes, the location of the micro hub should always be the closest to the delivery point [28], [38]. Assman et al [15] recommends locating them in areas of higher commercial density.…”

Directions for future research on urban mobility and city logistics

“…The first task is to identify potential UMC locations. [81] takes a geo‐driven approach toward the problem of identifying sound locations for UMCs based on data available in geographical information systems. They apply various (heuristic) considerations.…”