Congestion Pricing Part 2: NYC’s Digital Path to 'Everything Bagel'
From ‘Plain Bagel’ to Potential Shutdown
Given the President’s recent declaration that NYC must stop congestion pricing (aka the Congestion Relief Zone, CRZ or CBDTP) by March 21st, it is worth focusing on opportunities to complement the currently tenuous system. If the MTA is forced to shut down the system, New York City may be able to turn around and (re)introduce tolls on all of the currently untolled Manhattan bridge crossings, which can be designed to approximate the system’s congestion relief and revenue. I can’t claim to be a legal expert, though we should expect this matter to drag on.
In my previous congestion pricing post, I explored how our “Plain Bagel” single goal, revenue-first approach severely constrained congestion pricing’s ability to achieve the multiplicity of potential benefits, while simultaneously leaving it severely vulnerable to challenges and delays. Contrasting this with Uber’s digital approach to pricing revealed an “everything bagel” multi-solving alternative, flipping the review paradigm on its head, from pre-calculating to measuring impact in real-time. I did not, however, suggest how the City would get to this lofty ideal.
NYC’s Overlooked Step-Child Congestion Pricing
But the CRZ is not the only congestion pricing system at play in New York. The other system is New York State’s Congestion Surcharge for taxis and ride-hails, which was implemented prior to the passing of the State’s congestion pricing legislation. That enacted a per-trip "for-hire vehicle" (FHV) fee for riders within an area slightly larger than the current congestion relief zone—up to 96th rather than 60th Street. Politically, this was sold as a pilot measure of congestion pricing before the full CBDTP deployment, or “preliminary surcharge” giving New Yorkers time to acclimate.
From my vantage, this was all nonsense. First, the FHV fee involved no associated technology implementation, initially relying on NYC TLC to review paper copies or PDFs that Uber, Lyft, and taxi companies had sent over of all their trips. And second, the implementation was so utterly detached that the later CBDTP contract required a distinct system and process to capture & reconcile those transactions. Adding to the nonsense was that the blunt surcharge was being borne by riders not drivers, and that, together with its lower share of ride and total cost, meant its congestion reduction potential was always minimal.
But! viewed in another light this separate system represents a highly underutilized congestion pricing resource, one that can help fill the revenue gap for either an imposed shutdown or its temporarily lowered toll. This is a digital congestion pricing system, one with boundaries existing only solely due to legal decree.
The Digital “Everything Bagel” Vision
As mentioned in Uber’s Dynamic Pricing Model in Post 1, digital fee calculation is inherently how ride-hail companies operate. Sending a cavalcade of PDFs and relying on an agency to extract the fees was a big F U from Uber to the City—attaching fees to specific geographies is literally how Surge Pricing has worked from Day 1.
Since Uber came to NYC in 2011, the shift in the City’s character is readily apparent. For generations, the yellow taxi was emblematic of the City. Today those 13,587 medallioned vehicles are overwhelmed by the telltale "T" license plates of black Camry, Model Y, and Suburban, numbering some 80,000 ride-hail vehicles and representing 50% of traffic in Midtown.
This continued domination of traffic patterns provides the impetus to revisit the fee. Leaning on the inherent geographical & digital capabilities of ride-hail operators, the City can implement a dynamic, multi-dimensional "everything bagel" system that tackles multiple priorities—congestion, emissions, access—simultaneously. An early version could differentiate Midtown from Downtown and expand the geographic boundaries with a half fee for northern Manhattan or especially Downtown Brooklyn, where setting up a separate physical toll boundary is impractical.
Learning from Micromobility
Governments across the country have already leapfrogged congestion pricing with an advanced, dynamic, multi-dimensional pricing for one type of vehicle on the road: scooters.
The explosive, pre-COVID growth of scooters wasn’t preordained. It was enabled by blitzscaling operators’ Faustian bargain with the cities they entered, granting regulators precise controls over: how many vehicles could operate in an area, at what speeds, and which areas were out-of-bounds for both riding and parking. This regulatory leapfrogging included heretofore unimagined policy levers like Portland’s fleet size limits linked to real-time operation in underserved communities, enforced through the Mobility Data Standard (MDS).
A fascinating historical artifact is that the Mobility Data Standard (now, Specification) was originally conceived, a decade ago, as a mechanism for city government to manage the—according to the hype—imminent arrival of autonomous vehicles (AVs). Specifically, one city DOT leader was horrified by the prospect of millions of empty (aka “ghost”) AVs endlessly circling as one autonomous future laid out in the prescient Re-Programming Mobility report.
Countering this vision, regulators would leverage MDS to piggyback on the flood of generated location and movement data inherent in operating those futuristic vehicles. Only such a tech-forward approach could constrain the tide of AV usage in neighborhoods, on blocks, within lanes, and along the curb through policies and prices. (Note: this was also the core insight of the congestion pricing startup I previously co-founded.) It was only while the standard was under development that the connection was made that the scooter deployments springing up similarly required collecting accurate and frequently updated location data (though with significantly less precision).
MDS 2.0, the latest version of the technical standard anticipates expanding usage into other mobility segments including ride-hail, precisely the segment that NYC is under-managing with the FHV fee. Ironically, MDS relies fundamentally on H3, the hexagonal mapping standard that Uber created.
Market Shaping: A Digital Launchpad for City Policy
In all the ways that NYC's congestion relief zone is a "plain bagel" implementation—singularly priced, fixed infrastructure, process-constrained—MDS or a digital congestion pricing system is not similarly limited. These systems can expand zones across geographies AND jurisdictions, differentiate pricing tiers within and between zones, dynamically price based on on-the-ground conditions, and set temporary polices such as aligning with air quality alerts.
Ultimately, digital congestion pricing serves as a platform enabling market shaping—intentionally setting policies to steer towards a desired future state. Rather than the infrastructure, technology, and process defining the policy, a city can start by asking what behaviors it wants to prioritize & deprioritize. These could, but are not limited to, include incentivizing zero-emission vehicles, off-peak deliveries, transit use, or other urban livability targets. Then, and only then, the city can devise a combination of policies to achieve that perfect “everything bagel” state for itself.
Expanding Beyond Ride-hail
Digital congestion pricing’s flexibility & potential for expanded geography naturally reveals possibilities beyond ride-hail, into other segments of vehicles highly dependent on GPS-based routing and locating. The most obvious is package deliveries. FedEx, UPS, and Amazon, in combination with ride-hail, have remade the streetscape along with associated social compacts—accepting double-parking, routinizing blocking streets, and turning loading zones into micro-distribution centers.
While blunt per-package fees have been deployed to shore up state highway trust funds, and previously suggested for subway funding shortfalls, managing their multifaceted impacts demands nimble policy measures. Market shaping enabled by digital congestion pricing could prioritize consolidating trips, time-shifting deliveries, or using zero-emission, right-sized, and quieter vehicles, while also raising funds.
App-based deliveries, like Instacart, Doordash, or Grubhub, provide an even more technologically-aligned basis for pricing, but the segment is certain to be more contentious given the potential to impact delivery workers. Who owns and operates the fleet is a key consideration, though on this point MDS has done some future-proofing, baking in support for delivery robots.
Reclaiming NYC Streets with Digital Tools
Being able to draw an imaginary geographic boundary in the law and charge vehicles according to it is an incredibly powerful thing. One that cities, not only New York, are massively underappreciating. While mobility technologies and companies are more efficient than ever in exploiting gaps in regulation and enforcement, cities have yet to respond with similar agility. To meet the increasing complexity of 21st Century challenges, cities must embrace the digital tools and approaches at their disposal. Augmenting the policies already on the books is a good place to start.