THE IMPACT OF SOCIO-DEMOGRAPHIC INDICATORS ON URBAN SHOPPING TRIP PARAMETERS

Mykola  ZHUK; Halyna  PIVTORAK; Anastasiia MARKEVYCH

doi:10.36910/automash.v2i23.1522

Mykola ZHUK candidate of technical science, Associate Professor, department of Transport Technologies, Lviv Polytechnic National University https://orcid.org/0000-0003-1989-1053
Halyna PIVTORAK candidate of technical science, Associate Professor, department of Transport Technologies, Lviv Polytechnic National University https://orcid.org/0000-0003-3645-1586
Anastasiia MARKEVYCH bachelor student, department of Transport Technologies, Lviv Polytechnic National University

DOI: https://doi.org/10.36910/automash.v2i23.1522

Abstract

Understanding the factors influencing a user's choice of mode and travel time within an urban transport system is a crucial element of urban mobility research. This study focuses on the characteristics of shopping trips, which are less time-regulated compared to work or school commutes but have a significant impact on urban traffic flows. The aim of the research was to identify the connections between socio-demographic indicators and people's choice of place and time for shopping. An online survey was conducted among residents of Lviv, yielding 152 responses suitable for further analysis. The results indicate that the likelihood of using transport is much higher when shopping at large malls or markets (64.2%) compared to shopping at smaller stores (31.3%). Most shopping trips are made in the afternoon (after 3:00 p.m.). The study of the influence of socio-demographic characteristics on the choice of shopping location and time was carried out using the cross-tabulation method, followed by a χ²-test to verify the hypothesis of variable independence. According to the results, car ownership significantly influences the choice of shopping location, while gender and household income affect the choice of shopping time. A further cluster analysis based on statistically significant indicators, using the k-means method, identified five population clusters with distinct preferences for shopping time and location. These findings are valuable for modeling shopping trip demand and distribution over time and space.

Keywords: shopping trip, shopping time, shopping place, cluster analysis, socio-demographics indicators