Efficiently getting around Manhattan takes a certain combination of gumption, agility, and a keen sense of both where you and, as I so kindly refer to them, all of those people who don’t know how to walk are trying to go. You don’t try to hawk a cab on 49th and Broadway after the Eugene O’Neill theater empties out. Each new person looking for a cab moves 20 ft further up Broadway, undercutting those hopelessly waiting on the corner before a cab can reach them. The seasoned veteran knows to avoid jostling with tourists and the Times Square mafia by walking over to 8th or 9th ave to find an open cab.
The key is knowledge. Whether you’re in town for the weekend or a resident who’s fortunate enough to experience NYC price inflation each day of the year, knowing the little details like the direction of avenues and when to not take the FDR are all part of adroitly navigating NYC. Enter Uber. Uber’s service and app are both designed to take thinking out of traveling. The app takes care of payments, suggests easy pick-up locations nearby, and even optimizes the route by deferring to Waze. However, the cost Uber imposes for softening many of the pain-points of traveling comes in the form of surge pricing. I don’t believe the end-of-days narrative that taxi monopolies across the country are trying to sell when it comes to Uber’s business model. Those riders who are left rankled by surge pricing in their area need to take a page from my anecdote above and simply employ some Uber knowledge to get from point A to point B. Both Uber and Lyft have rolled out surge pricing maps to help riders understand the demand in their area at any given time and to help drivers meet demand. In certain scenarios, you might not be able to escape surge pricing and should look to other ride sharing methods, public transportation, or even a taxi. It’s all about having as much information as possible to make a decision that you’re most comfortable with.
The goal of this exercise is both selfish and altruistic.
– Play with the Uber API
– Learn some nifty Python mapping packages
– Ramble in blog form
– Observe how surge pricing moves throughout evening rush hour
– Find areas of reduced surge pricing near areas that often have heightened demand
The aforementioned surge pricing apps are definitely more polished than my plots and they operate in real-time, but I hope that my work can tell a short story that ultimately leaves you with better information to get around the city. Check out my GitHub for the source code.
The Eye Candy
Before I show some pretty plots, I just wanted to take a moment to give credit where credit is due. I made my plots while heavily referring to the sample plots of London I found here. Also, I used the database of Manhattan restaurants at NYC Open Data to generate my rough grid of points. The points are roughly 2 city avenues apart (about a 5 min walk). You’ll notice empty patches over Central Park, Hudson Yards, and other areas devoid of restaurants. I essentially used restaurants as a proxy for foot traffic areas in Manhattan. I thought this was a valid compromise between the coverage issues of using subway stops and an exhaustive grid of the island.
4:30 pm-7:30 pm Weekday Rush Hour
The first period I looked at was evening rush hour. You can see that getting an Uber in midtown between 4:30 pm and 6:30 pm on a weekday will almost always come with 1.5x surge pricing. For reference, I’ve annotated a few Manhattan points of interest, some of which are in high-business areas. The earliest surge pricing occurs is downtown near the Stock Exchange at 4 pm when markets close. This demand seems to be transient as the rates are back to normal fifteen minutes later.
From 4:30 on, however, you can see varying surge prices in midtown from 20th st up to Central Park. Some intervals, like 4:45 pm and 5:45 pm exhibit very localized demand near Penn Station. Between intervals, it’s pretty common for surge to bounce between 2-2.9x. If you’re looking to wait our the surge, you may be waiting until 7 pm when driver supply seems to meet demand. Please see the appendix for links to the individual images making up the gif.
In general, it seems that any Wall Street-based surge pricing remains very localized to the area, not extending too far uptown. If people working on the street don’t mind the walk, I’d highly recommend walking a few blocks north to see largely reduced prices. Midtown offers little respite from demand-based pricing during the evening rush hour. The time-lapse above shows that you can sometimes find pockets of low-surge but if you find yourself at Grand Central you may have to walk 20 blocks in either direction to see a change. At that point, just hail a cab or sweat it out in the subway.
Here’s the same time-lapse for the following day’s evening rush hour (it was a Wednesday). I can’t explain why there was such high demand in midtown as early as 4 pm with surge pricing extending even into the Upper East/West Sides. One area for future research would be to plot both the number of Uber drivers and Uber users at any given time to see whether surge pricing is the result of reduced supply or increased demand for a given time period. If I had to guess, I would say that there were fewer available drivers on this day as there’s increased surge even in SoHo and the East Village. I would expect rider demand during the evening rush hour to be pretty stable in those areas from day to day as they are generally less corporate than midtown.
Like above, here’s the average surge over the Wednesday evening rush hour. This further illustrates that this was a generally poor time to ride Uber if you’re pinching pennies.
One note I’d like to make is that the gifs above are still only two samples from two evenings in Manhattan. My observations and the reasoning I attach to them are mostly conjecture. I hope this exploration will at least get you to move the pin over a few blocks next time surge pricing makes you second guess calling an Uber. The change in price may be nominal or you may end up finding a cab in the time it takes you to walk over to your new pick up location. Regardless, I’m generally happier with my decision when I have a fuller understanding of my options.
7:30 pm – 10:30 pm Rush Hour
After a lull from 7:30 – 8:45 pm, surge pricing rose again mostly around Times Square and extended throughout the west side from Tribeca up to Columbus Circle. Broadway shows typically let out around 9 pm which may explain the activity between Penn and Columbus Circle, but the demand seems much more widespread than Broadway alone could explain.
I think the high demand from 9-10 pm may be a perfect storm of post-work dinner finishing up while other Manhattanites are just starting their nights in Chelsea, Tribeca, and the Meatpacking District. The time-lapse below exhibits the same phenomenon along the west side, this time for Wednesday May 25. The lesson to be learned here is if you’re on the west side during a weeknight, either finishing up or just starting your night, you may have a harder time escaping surge pricing from 9-10 pm. You’ll have a much easier time avoiding surge pricing if you’re spending your weeknights on the east part of Manhattan.
For plots of the average surge prices over the two days, see the appendix.
Final Remarks and Further Work
Remember, the following points are remarks are from two days of monitoring Uber. There’s always the risk of being mislead by small sample bias, but I had to start somewhere. With that said…
- Surge pricing can vary every fifteen minutes, but there are some areas of Manhattan where demand is off the charts for sustained periods of time.
- If you’re near Grand Central Station, try walking up to 50th st. for a chance at lower surge pricing.
- Wall Street surge pricing seems relatively isolated. Walking a few blocks north may lead to lower fares.
- Surge pricing is pretty low during the evening rush hour between Wall Street and Union Square.
- After 9 pm on weekdays, expect surge pricing along the west side from Columbus Circle down to Chelsea.
Now that I have a reasonable framework for collecting and processing Uber API requests, I could do something similar with Yankee games in the Bronx, late night downtown, or weekend brunch. While I’m generally happy with the plots I’ve made, an interactive application built with D3.js could really shine.
Single Interval Images from May 24, 2016 can be found here
Single Interval Images from May 25, 2016 can be found here