USING CLUSTER ANALYSIS TO STUDY THE CHARACTERISTICS OF MICRO-FREIGHT FLOWS OF SHOPPERS

Abstract

The article presents the results of a study aimed at identifying socio-demographic and behavioral factors influencing people’s choice of transport mode during shopping trips. Considering the increasing volume of shopping-related trips and the growing attention to the environmental aspects of urban mobility, the study examines the phenomenon of micro-shopping flows as part of the broader urban freight transport system.

The study is based on an online survey of 479 respondents. Using hierarchical clustering, two main clusters of shoppers were identified. The first cluster consists of middle-aged individuals, predominantly women, who combine active employment with medium-weight purchases (2–5 kg), mostly during daytime hours, and prefer traveling by private car or on foot. The second cluster primarily comprises students who make small purchases (up to 2 kg), more often after 3:00 p.m., often combining public transport and walking.

The results confirm the presence of statistically significant differences between the groups in terms of age, gender, occupation, shopping time, and purchase weight. A clear relationship was found between purchase weight and choice of transport mode: as the weight increases, the likelihood of using a private car rises significantly. Similarly, temporal characteristics influence modal choice — walking predominates in the morning hours, while the share of car and public transport trips increases in the afternoon.

The practical significance of this study lies in providing a scientific basis for developing targeted sustainable transport planning measures aimed at specific user groups. For young people, it is advisable to focus on improving access to public transportation and developing micro-mobility infrastructure. For the working population, effective measures may include incentives to reduce private car use.

Future research prospects are related to modeling the impact of micro-shopping flows on overall urban traffic. Such analysis is important for managing peak loads in the transport system and for developing policies aimed at the environmentally sustainable development of urban transport.

Keywords: shopping trip, cluster analysis, socio-demographics indicators, mode choice, micro freight flows