As part of my role as an appointee to SFUSD’s Citizens’ Bond Oversight Committee, I visited McAteer Culinary Center after a $5,000,000 investment in renovation. I discovered a multi-million dollar savings opportunity via increased utilization of the kitchen based on better alignment between opex and capex investments. Because the opex and capex budgets at SFUSD are legally separate, there are likely many such opportunities available.
Self-driving cars are scaling rapidly in San Francisco, and their cost structure is low enough to feasibly replace car ownership at a scale not possible via other ride-sharing services. Using mathematical modeling, I explored new tradeoffs this will present. For example 50% saturation would allow the removal of half of all street parking, increasing traffic flow exponentially, and allowing for increased safety, in some areas. This is one of many considerations, but one I haven’t seen accurately modeled or forecasted elsewhere, even as we head that way in San Francisco. The results are that traffic can improve significantly for high congestion areas, but the specifics depend on details such as congestion level, time of day, and patter of parking removal.
San Francisco has roughly 275,500 street-parking spaces. In many areas these heavily overlap high congestion areas.
Self-driving cars never need to park in high congestion areas (other than brief loading/unloading.) When fleets hit saturation above 10%, significant street parking can be removed piecemeal from high traffic areas. This opens new lanes to traffic, which can cause exponentially lower congestion due to the non-linear scaling of traffic. It can also increase safety due to improved visibility for both drivers and pedestrians.
A simulation of a single intersection with one (horizontal) lane of parking freed up for transit. Green autonomous vehicles are at 50% saturation. Autonomous vehicle interlinking, and long term induced demand and not included. Traffic changes depend on existing congestion levels.
As a side project, I took some orbital mechanics classes online via MIT and built a simulator for how bodies move through our solar system. I simulated a well-know asteroid, Apophis, and examined how much energy it would take to move it’s orbit relative to Earth. Such calculations are required for preventing a potential asteroid strike (though nothing as large as Apophis is likely to be both undetected and on a collision course with Earth) or solar-scale projects such as an Earth-Mars cycler.
Human access to information has transformed a number of times. Before the internet physical publications were expensive to create. This ensured that credible and economically useful information was prioritized for publication. Books, magazines, and newspapers were few enough that the community of buyers and readers could feasibly police them for credibility. In the early years of the internet, this model was assumed to continue – finding information became harder and therefore search was popularized as an access channel, yet credibility was an assumed non-concern. With the explosion of online information, the reduction of publication costs to zero, and most importantly, the dramatic downscaling of user relationships with publishers, the community can no longer police the massive volume of tiny bits of information for credibility.
To address the problem, I build a prototype of a new search engine that uses credibility scores from expert review, combined with traditional popularity scores, to produce credible results for search queries. Early results show several percent of Google search results are from sources that are not factually oriented. Prototype access available upon request.
