<p>Both the existing parking allocation models and pricing algorithms might over-allocate parking flow to the high-utility parking lots, which become quickly saturated by the early-birds and consequently the more qualified latecomers have to switch to a low-utility parking lot. To solve the spatiotemporal imbalance of area demand and supply, this study proposes a proportional pricing algorithm with an announced parking rate to regulate parking flow proportionally to the number of available spaces within each parking lot. For each parking lot at each allocation step, the announced parking rates are iteratively optimized to minimize the parking deviation between the current parking flow (obtained from the ordinary allocation model with the latest announced parking rates) and target values obtained from the proportional allocation model with the predetermined permitted ratios. At each optimization iteration, a parking lot with the largest parking deviation is selected with a certain increment in the corresponding announced parking rate, which is iteratively increased until satisfying the termination conditions. Then, a SUMO-based simulation was carried out to investigate the performances of the proportional pricing algorithm, using an empirical case study in Wujiaochang central business district (CBD), Shanghai. The results demonstrate that the proportional pricing algorithm outperforms the static parking rate scheme and online pricing strategy in decreasing both the nonmonetary costs and parking disutility, and has a potential to perform as better as the laborious offline pricing method. The proposed proportional pricing algorithm may assist in the design and operation of urban parking reservation systems.</p>