Donald Shoup (1938-2025) led the war on (too much) free parking

 Here's his WSJ obit:

Donald Shoup, a Parking Guru Who Reshaped the Urban Landscape, Dies at 86
An economist at UCLA, Shoup said free parking carries a high cost, which is borne by everybody, By Jon Mooallem 

“Nothing is more pedestrian than parking,” he often joked. Everyone else is focused on traffic, Shoup told the website Streetsblog. “I thought I could find something useful if I studied what cars do for 95 percent of the time, which is park.”

...

"In the mid-1960s, he was working in Midtown Manhattan while completing a Ph.D. at Yale and suddenly noticed a paradox he couldn’t make sense of as an economist, but which everyone took for granted as human beings. Up and down West 44th Street, “almost all cars were parking for free on some of the most valuable land on earth,” Shoup recalled on the “Curb Enthusiasm” podcast. 

...

“We have expensive housing for people, and free parking for cars! We have our priorities the wrong way around,” Shoup cried out...

 ...

“A surprising amount of traffic isn’t caused by people who are on their way somewhere,” Shoup concluded in a 2007 New York Times opinion column. “Rather, it is caused by those who have already arrived.” 

...

"Shoup’s policy prescriptions were straightforward: Get rid of minimum parking requirements; bring the price of on-street parking in line with demand, enough to maintain one or two empty spots on every block; and funnel the resulting revenue into upkeep and other public services for the immediate area, creating what Shoup called a “parking benefit district,” to bring residents and local businesses on board.

"There were already some successful test cases of these reforms when “The High Cost of Free Parking” was published, most notably in Pasadena, Calif., where reinstalling parking meters was key to revitalizing a historic shopping district. And Shoup considered the proposals in his book so sensible and self-evident that, he later recalled, he naively assumed his vision would immediately become a reality. “I thought the world would change next month,” he told the podcast, “The War on Cars.”

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HT: Atila Abduldakiroglu 

Stable Matching with Interviews, by Ashlagi, Chen, Roghani and Saberi

 Job applicants can now easily submit many job applications, and so interviewing applicants, which is time consuming, has become a major source of congestion in many labor markets.  But even in labor markets that use a clearinghouse to process offers and acceptances (like the market for medical residents in the U.S., the NRMP match) interviews are often organized in a decentralized manner. Here's a paper that tackles the question of how to organize an interview match, under some assumptions about what kind of information is obtained in interviews.  Two approaches are considered: an 'adaptive' algorithm that takes into account the results of previous interviews in assigning subsequent interviews, and a 'non-adaptive' algorithm that matches candidates to interviews before any interview results are known.

Stable Matching with Interviews, by Itai Ashlagi, Jiale Chen, Mohammad Roghani, and Amin Saberi (all at Stanford)

Abstract
"In several two-sided markets, including labor and dating, agents typically have limited information about their preferences prior to mutual interactions. This issue can result in matching frictions, as arising in the labor market for medical residencies, where high application rates are followed by a large number of interviews. Yet, the extensive literature on two-sided matching primarily focuses on models where agents know their preferences, leaving the interactions necessary for preference discovery largely overlooked. This paper studies this problem using an algorithmic approach, extending Gale-Shapley’s deferred acceptance to this context. Two algorithms are proposed. The first is an adaptive algorithm that expands upon GaleShapley’s deferred acceptance by incorporating interviews between applicants and positions. Similar to deferred acceptance, one side sequentially proposes to the other. However, the order of proposals is carefully chosen to ensure an interim stable matching is found. Furthermore, with high probability, the number of interviews conducted by each applicant or position is limited to O(log^2 n).
"In many seasonal markets, interactions occur more simultaneously, consisting of an initial interview phase followed by a clearing stage. We present a non-adaptive algorithm for generating a single stage set of in tiered random markets. The algorithm finds an interim stable matching in such markets while assigning no more than O(log^3 n) interviews to each applicant or position. "

Signaling to decongest job applications and interviews: update on the market for medical residents

 Signaling for interviews is evolving in the market for new doctors, i.e. for medical residencies.  Some specialties are allowing a relatively small number of signals (as in Economics), while others are moving towards many signals, which are functioning as soft caps on the number of applications, since many residency programs in those specialties won't give an interview to someone who doesn't signal them.

Ozair, A., Hanson, J. T., Detchou, D. K., Blackwell, M. P., Jenkins, A., Tissot, M. I., Barrie, U., & McDermott, M.  W. (2024). Program Signaling and Geographic Preferences in the United States Residency Match for Neurosurgery. Cureus, 16(9), e69780. https://doi.org/10.7759/cureus.69780