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By Betsy Gardner • December 12, 2019

Keith Chen headshot
Professor Keith Chen, UCLA

 

The scooters are coming. And the dockless bikes. And maybe even dockless mopeds? These micro-mobility vehicles can be useful last-mile solutions and complement the public transportation infrastructure of a city. They can also be dangerous public nuisances. So how can cities reap the benefits of micro-mobility while mitigating the costs? Keith Chen, Associate Professor of Behavioral Economics at the University of California Los Angeles, has been on both the industry and the research side of new mobility, and feels that the path forward is through behaviorally-informed smart policies. I spoke with Chen to get his advice for cities, and he shared the five key principles below.

 

Consider user behavior:

Instead of broad regulations, Chen advocates that cities make decisions that are informed by the behaviors of its residents. Unfortunately, many traditional approaches have been all-or-nothing extremes with heavy-handed regulations. Chen believes this is not the best approach, because regulations are often crude and leave many benefits of micro-mobility on the table. In contrast, a city that implements a behaviorally-informed approach will do better on all dimensions; it will gain revenue, provide better transport service, and learn new insights from micro-mobility data.    

City officials should consider all the ways in which their constituents can interact with micro-mobility modes. Are they frequent riders? Do they find scooters strewn around their sidewalks and blocking handicap ramps? Is a dockless bike their last-minute solution for beating traffic? Are they users who park thoughtfully or not? Informed by the needs and behaviors of their residents, city officials can put forward policies and work with the mobility providers to make sure they’re providing services in the ways that the residents need. Despite the differences in their ultimate goals (providers want a financially viable company, and cities want to improve resident welfare), cities can align provider incentives to work toward welfare goals.  

Utilize smart caps:

An example of this is moving from crude caps to smart caps. Many cities want to impose caps to prevent too many dockless scooters or bikes from being on the streets. But Chen warns that a static cap basically replicates the taxi medallion system, which he calls an “18th century way of dealing with a 21st century problem.” Instead, cities should have smart, variable caps, because “we now have the technology to do so much better.” 

Smart caps could be season-specific; the demand for scooters in Chicago is much lower in the winter than the summer. Caps could differ based on location; using geolocation, there could be a higher cap in a pedestrian-heavy area. Caps could be based on time of day; more dockless bikes could be available during commuting hours. Simply capping the entire city as a whole isn’t the solution. 

Find the right incentives:

The dynamic capping system could also work to incentivize micro-mobility providers to hit certain benchmarks set by the city. Since the city wants to fulfill a welfare goal, they could establish certain monthly benchmarks around equity or safety and, if a provider hits those goals, they could be rewarded with a higher cap the next month. Chen says that the “north star” is smart, utilization-based pricing that would actually eliminate the need for caps altogether. Many cities are currently experimenting with this model, where the providers are charged a fee for each bike or scooter that is unused and just sitting around the city. However, the fee could be reduced if they were situated at approved city locations, like light-rail stations or underserved areas.

If cities can get that pricing right, then the provider will be incentivized to use their data and find the right numbers, times, and locations for the dockless vehicles. The providers can even engage the users in this. Right now, at the end of many dockless rides, users are asked to take a picture of the vehicle they parked. Using machine learning, the providers could scan the photo and identify if the scooter or bike is in an optimal, low-fee location. Riders could be incentivized to park in the best places with discounts or rewards. 

Share data:

These types of policies do necessitate a level of data sharing and transparency. Individual rider data is controversial; the micro-mobility provider knows that information, but sharing it with a city can be complicated due to privacy and data protection concerns. However, Chen believes that simply having the trip and state data (starting and ending points, and the active or parked position) will get cities 90 percent of the data needed to implement behaviorally smart policies. That trip and state data is enough to verify compliance with a smart cap and differentiate utilization by zones.   

Prioritize the relationships:

Finally, for all of this to come together, cities and micro-mobility companies need to have good working relationships. “It should be a trust-but-verify, smart partnership between cities and micro-mobility partnerships,” says Chen. “And what I see as a very positive element is cities taking the lead on this.” And when cities lead the conversation, they can implement behaviorally-informed smart policies that meet resident needs.