Traffic congestion is a problem in cities all over the world. Globally, Moscow has the worst traffic, with drivers spending on average 210 hours in traffic annually, according to a 2019 report. Americans also suffer and waste time in traffic. Boston (164 hours lost due to congestion) and Washington, D.C., (155 hours) ranked as the most congested cities in the United States.
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According to TomTom’s traffic index, 239 cities saw increased congestion between 2018 and 2019, while only 63 had a decrease. Although congestion decreased from 2019 to 2020, a majority of this can be attributed to the effects of the COVID pandemic, and congestion is likely to increase once the world returns to “normal.”
One solution to traffic congestion is to increase the availability of new mobility solutions, which venture capitalists, global corporations and municipalities have been increasingly investing in over the last several years.
New mobility solutions offer a variety of cloud-connected vehicle types including bikes, scooters, cars, vans, buses, etc. The idea is that increasing the number of options will reduce the reliance on private car ownership, which in turn will reduce congestion, save money and deliver the urban utopia we’d all like to experience.
Despite these new solutions, congestion continues to get worse. The real challenge to reducing congestion and improving the mobility experience is not to develop a new vehicle type or form factor, it’s a fundamental lack of data and transparency to inform decisions. Mobility operators have to know if and when they are headed in the right direction with their product/service offerings.
Today, there are no efficient ways of doing this.
The problem: Slow data-gathering in a fast-moving world
To try to solve the problem, large players and governments often engage consultants or have internal strategy teams that conduct surveys, reference census data and analyze existing methods to try and nail down the right long-term strategy. This results in a “point in time” research snapshot of a world that changes minute by minute, and this approach wastes a lot of time and money.
Public bus routes are a great way to visualize the lack of data and transparency problem. There are an infinite number of permutations to consider even if you limit the problem to a few simple variables like number of buses per route (frequency) and the route itself.
Should we run more or fewer buses on a particular route, and at what fixed or variable intervals? Should we add a completely new route or modify existing routes? Should those routes diverge from and then rejoin the main route or remain separate? How does this change on weekdays versus weekends or in summer versus winter? Currently, the only way to really know is to test it. This means a bus operator has to risk going in completely the wrong direction, and that might lead to rider dissatisfaction, lost revenue and even customer loss.
Misguided attempts at new mobility
Some startups have bypassed studies in favor of rapid iteration, but they, too, are limited by the quantity and scope of their own data. Traditional forms of data-gathering and assessment are slow, limited in scope, and inaccurate, expensive and time-consuming. As a result, the mobility sector is blind to how, where, and even why to improve.
Some companies have even tried to build maximum flexibility into their mobility offerings by having completely crowd-sourced routes. One example of this was Chariot. Even coordinating among small groups of riders proved difficult because they also suffer from the lack of data and transparency. As a result, this approach ultimately failed, leading to Ford shutting it down only a few years after acquiring it for $65 million.
Governments, startups and big companies are all in the same boat, somewhere in the middle of the sea, not sure of the direction toward land. The pandemic has only confused things further, forcing everyone to ask about which direction transportation and new mobility should go.
The way to win: smart mobility insights
Instead of relying solely on sporadic, survey-oriented data gathering, the way to solve the problem is through continuous, rapidly updated and accurate information from which to measure, validate and optimize the impact of strategic decisions.
Annual census data will not help anyone make daily or even weekly tactical decisions. High-frequency data enables more frequent feedback loops and iterations. In an environment that changes quickly, data has to be gathered equally quickly. The companies with the right data and insights can measure against optimum conditions instead of measuring against themselves or estimations of their closest competitors.
Consider this: What if we had all the information on how people are moving, whether it is by foot, bike, scooter, car, train, subway, etc., and could then tailor our solutions accordingly? We would not be asking ourselves how we are doing versus our closest competitor or against our most logical substitute. Instead, we’d be asking ourselves: How are we doing versus how people are actually moving around? What if we better understood the missing links between different players in the ecosystem and could focus on developing the right partnerships in the right order? This is not a figment of someone’s imagination. It is being built right now by a variety of startups around the globe.
What these startups are building to solve the new mobility puzzle will have large, unbiased input data that updates frequently. It will integrate with existing planning tools and processes, and be used by the existing planning teams and stakeholders, without adding a staff of Ph.D.s. It will be scalable and cost-effective so that any player, large or small, within the ecosystem can effectively use the same tool.
In most rapidly changing industries, the part that actually touches the consumer tends to get the most focus. New mobility is no exception. This is why we’ve all heard of Uber, Lyft, Lime, Spin, etc. But when the desired customer experiences cannot profitably be met, support tools are developed out of necessity and the companies that leverage these tools the best will win.
Imagine a world where it’s faster and easier to get from point A to point B within cities, with less congestion, less struggle and much less stress. It is possible, with smart mobility insights and a connected ecosystem that constantly collects and analyzes actual movement data. Urban utopia is possible, with the right mobility solutions being driven by high-quality, holistic, data-driven decisions.
Sean Simpson is director at WIND Ventures where he invests in energy, retail and mobility startups. Prior to joining WIND he was an investment manager at GM Ventures, the investment arm of GM, where he was investing in the future of transportation technology. Before joining GM Ventures, he founded AutoBike, a startup that designed and built electronically controlled automatic shifting systems for bicycles.
Illustration: Li-Anne Dias.
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