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Location Analysis of Parcel-Pickup Points in the Guangzhou Metropolitan Area, China

鄭, 紫来 筑波大学 DOI:10.15068/0002002097

2021.12.02

概要

The use of parcel-pickup points (PPPs) is an effective way of solving the last-mile problem of logistics. There are mainly two types of PPPs: parcel pick-up locker (PPL) and parcel pick-up shop (PPS). However, there are limited reports that provide decision-makers with concrete solutions for PPP organizations using quantitative methods. Where and how to select a suitable location for PPPs remains challenging. The majority of the quantitative studies focus on small residential areas, rather than metropolitan areas, as the study area. Moreover, most studies merely analyzed one type of PPP, which overlooks the interaction of the two types. This study aims to identify the location differences between the two types of PPPs in the Guangzhou metropolitan area and the layout strategy considering the interaction of the two types. The research simulated suitable location areas for the two types of PPPs with grid units, respectively. Based on the simulation results, the location differences between the two types of PPPs were analyzed. The symbiosis environment between the two types of PPPs and the same type of PPPs was analyzed in the simulated suitable location area. The study provides insights to decision-makers for rationally planning two types of PPPs to prevent mutual competition and achieve sustainability.

This study has three novel outcomes. First, ecological niche overlap theory is applied to location analysis for the sustainable development of PPPs. To avoid competition between PPPs, the two types of PPPs should be planned as a whole system when selecting sites. This study also analyzes the symbiosis environment between the two PPP types and the same PPP type. It provides reference data for locating PPPs in a suitable area. Second, this study performs a suitability simulation of PPPs in a metropolitan area using the logistic regression (LR) model of machine learning (ML). Third, this study uses detailed data to identify the specific factors for PPP locations. The study attempts to refine the population-related factors on four residential and two commercial building types; the transport infrastructure is refined to seven types of roads and three types of transportation nodes (bus stop, metro station, and parking lot).

The contributions of this study are as follows: First, the location differences between two types of PPPs were analyzed using six characteristics: the main service objects, facility attributes, impact of land price, road factors, transportation, and population. The properties of PPPs are evident based on their location characteristic. PPLs and PPSs are more inclined towards public and commercial service facilities, respectively. Second, the interaction between the two types of PPPs was clarified. PPL exhibits a high compatibility with PPS, whereas PPS exhibits a low compatibility with PPL. PPL can be considered as a supplement to PPS. Third, the LR model of the ML method performed well in both PPL and PPS. The multi-zone LR model was superior to the standard LR model. A metropolitan area is a region consisting of a densely populated urban core and its less-populated surrounding territories. Population is an important factor of PPPs. The multi-zone simulation model was preferred, and the result was more accurate in a macro- scale study area with imbalanced population. Fourth, the simulation model results revealed that the PPS area accounted for 16.5% of the total area of Guangzhou, whereas PPL accounted for 10.7%. PPP allocation focused on these suitable areas. PPP allocation significantly reduced the difficulty of the analysis and time taken during decision-making. Fifth, the structure zones impacted the PPP location. The PPP location in the three metropolitan structure zones is characterized by the fact that the most critical factor in the central zone is close to the service buildings, and the most critical factor in the middle zone and the suburban zone is close to the infrastructure.

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