Predictive Urban Logistics & Parking Optimization
In recent years, increasing urbanization and the rapid growth of e-commerce have created an enormous challenge for logistics operators in delivering goods and services in densely populated urban areas. Traffic congestion, limited road infrastructure, and lack of adequate parking spaces are some of the main obstacles faced by logistics companies in cities. In this context, this project emerges as an innovative and sustainable solution to address logistics challenges in urban areas.
Parkunload is a digital platform for regulating, controlling, monitoring, and analyzing parking areas for urban merchandise distribution (DUM) vehicles. Based on the data that Parkunload generates, WhiteBox developed a predictive algorithm using a native cloud-based model (Google BigQuery) which allows logistics operators to optimize the management of their logistics route planning. In addition, the WhiteBox team was responsible for integrating different visualizations into the Parkunload platform in Looker Studio to improve the planning and decision-making of logistics operators, thus incorporating an additional layer of advanced analytics.
This solution has promoted greater efficiency in urban logistics management. It allows logistics operators to reduce transport costs and environmental impact by reducing the number of vehicles driving in search of a parking space. This translates into a significant reduction in traffic congestion and the emission of greenhouse gases, as well as an optimization of the use of parking spaces.
Passenger Satisfaction & NPS Driver Analysis
