A Predictive Framework for Siting Curbside Loading Zones

Client: City of Philadelphia Innovation Management Team and Office of Information Technology

In October 2022, the City of Philadelphia launched a pilot program featuring 20 paid curbside loading spaces for delivery drivers in Center City, termed "smart loading zones." These spaces allowed delivery companies to reserve spots and times through a smartphone app, aiming to alleviate congestion caused by double parking and the search for vacant spots. 

The collected data from the pilot program offers valuable insights into reservation patterns, guiding the potential expansion of the initiative. The primary question posed by this project is: Where should smart loading zones be located to maximize efficiency and reduce congestion? 
The absence of strategically located smart loading zones leads to inefficient parking practices by delivery drivers, hindering traffic flow and contributing to urban congestion. Our proposed solution involves the development of a backend application capable of predicting demand for curbside loading spaces at any location within the city. This application will consider various factors influencing a vehicle's decision to stop alongside a curb, including proximity to amenities, road classifications, time of day, and historical booking trends. 

The project aims to provide city planners and policymakers with a data-driven approach to optimize the placement of smart loading zones, thereby improving traffic flow, reducing congestion, and enhancing the efficiency of urban logistics in Philadelphia.

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