There is data to suggest that on-demand (OD) mobility services -- such as Uber -- are increasing vehicle kilometers traveled (i.e. causing greater traffic congestion) by inducing people away from public transit and other forms of urban mobility. This is potentially even more of an issue right now with most urban transit agencies looking at massive budget shortfalls.
But there's potentially another way to look at this problem. A recent study led by Dániel Kondor of the MIT Senseable City Lab has looked at not only vehicle kilometers traveled but also something that the team calls the "minimum parking problem." What is the minimum amount of parking that you need assuming a world with more on-demand mobility, and eventually autonomous vehicles?
To try and answer this problem the researchers looked at the small city-state of Singapore. With a population of about 5.6 million people and somewhere around 1 million vehicles, Singapore actually has one of the lowest number of private vehicles per capita in the developed world. Even still, it has some 1.37 million parking spaces taking up valuable room.
What the team found was that on-demand mobility could reduce parking infrastructure needs in Singapore by as much as 86%. This is the absolute minimum number, which would take the current estimate of 1.37 million spots down to about 189,000 -- a significant reduction.
However, the tradeoff is that it could increase vehicle kilometers traveled by about 24%. Without ample parking, their model assumes that these on-demand vehicles would need to "deadhead" between trips. That is, drive around aimlessly while they wait for their next passenger. Demand isn't usually neat and tidy.
However, it's worth noting that the above percentage increase assumes that if people were instead driving themselves around that they always found a parking spot as soon as they arrived at their destination. This, as we all know, is not often the case, and so this increase is probably a worst case scenario.
Nevertheless, the team did also find that a 57% reduction in parking could be achieved with only a modest 1.3% increase in vehicle kilometers traveled. This, to me, is meaningful because it says that you could, in theory, cut parking supply in at least half and not much would happen in the way of traffic congestion.
It would, however, free up a bunch of space for things like bicycle lanes, green space, and other valuable urban amenities. Now, if on-demand vehicles are pulling people away from transit, then maybe we're no better off. But if the alternative is people driving and parking everywhere they go, then it would seem that there are much better uses for that space.
Photo by Jordi Moncasi on Unsplash
A recent study by the MIT Senseable City Lab has used cellphone data to map both social and physical segregation within Singapore. To start, they used residential sale prices as a proxy for socioeconomic status. They then used call and text records (presumably it was all anonymous) from 1.8 million cellphone users in Singapore (2011) to map who interacted with who. Pictured above is one of those mappings.
What they discovered was evidence of a "rich club effect." In other words, the richer the person the less likely they were to interact with people outside of their socioeconomic band. The study calls this their communication segregation index.
A similar phenomenon was noted as people moved around Singapore. (This is the study's physical segregation index.) People tend to spend time in spaces alongside people with similar socioeconomic attributes. However, they did notice that this tends to wane during the day as people move around the city -- presumably for work and other such things.
I think it would be interesting to get a bit more granular about the findings in order to try and see, among other things, if certain public spaces are more successful than others at encouraging a broader socioeconomic mix. And it's probably only a matter of time before we start using tools like this to plan our cities. For more on the study, click here.
Image: MIT Senseable City Lab
A recent study and research paper by the MIT Senseable City Lab -- called, Tasty Data -- has discovered that restaurant data alone can be used to accurately predict location-based factors such as daytime population, nighttime population, number of businesses, and overall consumer spending within a specific geography.
They started by pulling restaurant data from Dianping (Chinese equivalent of Yelp) for 9 Chinese cities: Baoding, Beijing, Chengdu, Hengyang, Kunming, Shenyang, Shenzen, Yueyang, and Zhengzhou. They then paired their Dianping data with other available data (such as aggregated mobile phone data) and used machine learning to search for any correlations.
Below is a diagram of "nighttime population" in Beijing. They are using a 3 km2 grid.
If you're a regular reader of this blog, you'll know that I like these kinds of studies. By 2020, it is estimated that 1.7MB of data will be created every second by every person on earth. The numbers are staggering. And yet, "official" data sources, such as census data, remain slow and fairly limited. Studies like this one continue to show us what's next.
Image: MIT Senseable City Lab