Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Abstract

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management.

hillmaker: A Python package for occupancy analysis in discrete entity flow systems

“…Discrete event simulation is widely used in such research projects, and hillmaker can aid in the statistical analysis needed for modeling entity arrival patterns and for analyzing and validating simulation output. While problems in the healthcare industry spurred the development of hillmaker, it has been used in other domains such as bike share systems, freight operations (Castrellon et al, 2023), customer contact centers, and even for analyzing usage patterns of a high performance computing cluster by engineers at a large automobile manufacturer. Any system for which you have data on start and stop times of events, or entry and exit times of entities, is amenable to using hillmaker for characterizing temporal patterns in arrivals, departures, and occupancy (or task starts, task completions, and work in progress).…”