United States Department of Transportation
i
Smart Curbspace: Optimized Parking Reservations for Diverse Stakeholders
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2023-07-31
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[PDF-3.76 MB]
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Edition:Final Research Report
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Abstract:As the demand for curb parking increases and new types of curb space users compete for space, the need to more efficiently manage how vehicles interact with the curb becomes more apparent. One solution is to allow curb space users to submit a reservation ahead of their arrival that can be centrally managed and scheduled if the resources are available. To study the potential benefits and drawbacks of an intelligently managed curb, we develop a dynamic parking reservation system that continually collects parking requests from delivery and private vehicles and re-optimizes the schedule of accepted parking requests periodically throughout the day. This process employs model predictive control (MPC) to iteratively apply a mixed integer linear programming parking slot assignment optimization formulation adapted from prior literature. In our preliminary results we observe that our MPC algorithm can reduce computation cost and provide tractable parking schedules, which is not often possible with complex day-ahead optimal schedule generation. Additionally, we compare the reduction in total minutes of double parking and cruising between a first-come first-serve (FCFS) paradigm and our MPC algorithm. We preliminarily observe that the MPC algorithm can reduce double parking and cruising under some conditions, but correlations between request time and dwell time lead to cases where dynamically optimized reservations can increase double parking and cruising relative to FCFS. We intend to further explore and characterize conditions under which reservation systems improve parking metrics in future research.
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