SMARTSe
Project Details

Title:	Smart Parking Systems
Principal Investigators:	Raj Bridgelall

Abstract

Parking demand is a significant land-use problem in campus planning. The parking policies of universities and large corporations with facilities located in small urban areas shape the character of their campuses. These facilities will benefit from a simplified methodology to study the effects of parking availability and real-time parking information technology on transportation mode mix and impacts on recruitment and staffing policies. This study introduces an analytical framework using simple models to provide campus planners with insights about how parking supply and demand affects campus transportation mode-choice. The methodology relies only on aggregate mode choice data for the special generator zone and the average aggregate volume/capacity ratio projections for all external routes that access the zone. This reduced data requirement significantly lowers the analysis cost and time and obviates the need for specialized modeling software and spatial network analysis tools.

Preliminary results illustrate that the framework is effective for analyzing mode choice changes under different scenarios of parking supply, availability of parking information technology, and population growth. The model provides insights that would benefit campus planners and employers with facilities that share similar trip generation and attraction characteristics. The information is useful in land use planning, recruitment and target market development. The chart below is an example output from the simulator showing the relative mode shifts expected in time for a small urban area campus with the initial population and mode choice mix indicated, and no changes in parking supply. The only change in this example is a steady 2% campus population growth. The study will calibrate the mode choice model with revealed preference survey data. Stated preference data will calibrate convenience factors such as the availability of real-time parking information to simulate and quantify potential mode changes.