Key Words: R, GIS, Predictive Modeling, Curb Space Management, Cluster Analysis, Random Forest, XGBoost, Pilot Design, Data-Driven City Planning, Technology Planning, Smart Cities, Smart Transit

Objectives

In 2022, The City of Philadelphia’s Office of Innovation and Technology (OIT) launched a smart loading zone pilot program. The pilot program, in collaboration with a Google company, captured curb sign inventory and launched a small-scale paid booking platform for commercial vehicles in loading zones. This is the first exploratory study of the pilot’s data outside of OIT. Our analysis includes:

  • Cluster Analysis to uncover signage and use patterns in the study area

  • Impact of local economic context on loading zone bookings

  • Predictive modeling of loading zone bookings

Policy Context

City curbs were originally designed to distinguish transportation versus pedestrian right of way. This physical threshold has evolved from stone-paved street markings to wooden bollards to the finished concrete edge we recognize in most cities around the U.S. Today, the curb is more than an aesthetic barrier, it is also a forever changing posterboard of transit uses and technologies, such as loading zones, bike share docks, food trucks, bus stops, containerized waste, parklets, or ‘streateries’. As a result, the demand for ‘share-of-street’ continues to grow while the supply remains constrained. New, high-capacity management tools are needed to balance this bullish market for curb space.

In order to be able to deploy new tools at scale, cities digitize the curb through sensor or camera networks that produce ‘digital twins’. Digitizing the curb is only as effective as the analysis of the data it produces, and the analysis is only as effective as the clarity of a pilot’s goals. Interdisciplinary innovations that leverage both qualitative and quantitative methodologies to optimize curb space allocation are an emerging way to respond to these challenges.

The federal Bipartisan Infrastructure Law (BIL) established the Strengthening Mobility and Revolutionizing Transportation [(SMART)] (https://www.transportation.gov/grants/SMART) discretionary grant program with $100 million appropriated annually to fill in this equity divide. In March 2023, Philadelphia received $2M in SMART funding to digitize its city streets and advance how they manage scarcity in the right-of-way. The following study provides insight to the operational utility of limited-scale data-driven pilots that are constrained to work within the bounds of existing curb signage (which may not be optimal for the pilot).

Furthermore, while these awards are substantial, they are not sufficient to operationalize all this digital data. Many municipalities lack the resources or expert staff to develop data-driven programs to realize the full potential of this information. This project intends to assist cities in how they can operationalize data from limited-scale pilots in order to optimize public value along the curb for all their constituents.

Clean Data and Select Variables

The SLZ vendor’s curb sign inventory was converted from image to text with optical character recognition that produced inconsistent results. Data was cleaned and converted to numeric.