Predicting Park 'N Ride Parking Demand : Technical Paper

Abdus-Samad, Usamah Rashrash; Grecco, W L

doi:10.5703/1288284313839

Cited by 1 publication

(2 citation statements)

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“…This work follows the line of research on capacity sizing for park‐and‐ride lots. Early studies, such as Abdus‐Samad and Grecco (1972) and Spillar (1997), were mostly performed in collaboration with local DOTs and focused on developing site‐level demand models , in which regression analysis is applied to forecast the demand at an individual park‐and‐ride lot based on characteristics such as the location and accessibility. These studies treat demand forecasting and capacity sizing as two separate decision‐making processes: Demand is estimated first and a capacity size is then chosen to serve the anticipated demand.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meanwhile, the utility is formulated to depend on the demand and (average) parking utilization to account for the effects of congestion and parking information on commuter choice. The commuter choice model developed in this work can be viewed as an extended version of the site‐level demand model, which has been applied to forecast the demands for individual park‐and‐ride lots (Abdus‐Samad & Grecco, 1972; Bullard & Christiansen, 1983; Niles & Pogodzinski, 2021; Spillar, 1997). Using the MNL‐based commuter choice model, we represent the demands at all lots at equilibrium (given a capacity sizing decision) as a solution to a (vector) fixed‐point equation.…”

Section: Introductionmentioning

confidence: 99%

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Optimal capacity sizing of park‐and‐ride lots with information‐aware commuters

Wang,

2023

Production and Operations Management

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We study capacity sizing of park‐and‐ride lots that offer services to commuters sensitive to congestion and parking availability information. The goal is to determine parking lot capacities that maximize the total social welfare for commuters whose parking lot choices are predicted using the multinomial logit model. We formulate the problem as a nonconvex nonlinear program that involves a lower and an upper bound on each lot's capacity, and a fixed‐point constraint reflecting the effects of parking information and congestion on commuters' lot choices. We show that except for at most one lot, the optimal capacity of each lot takes one of three possible values. Based on analytical results, we develop a one‐variable search algorithm to solve the model. We learn from numerical results that the optimal capacity of a lot with a high intrinsic utility tends to be equal to the upper bound. By contrast, a lot with a low or moderate‐sized intrinsic utility tends to attain an optimal capacity on its effective lower bound. We evaluate the performance of the optimal solution under different choice scenarios of commuters who are shared with real‐time parking information. We learn that commuters are better off in an average choice scenario when both the effects of parking information and congestion are considered in the model than when either effect is ignored from the model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%