With the increasing number and quality of transportation options, how people get around in cities is becoming more diverse and variable. A recent survey suggested that 35% of people take shared mobility options to reach fixed transit at least occasionally. In NYC, most bike-share trips are taken by New Yorkers trying to get to transit stations faster.

However common taking multiple modes is, it can be very hard to figure out what the best options are for a particular trip. For instance, none of the major navigation apps currently has bike-share as a travel option, let alone showing the available bikes near you. They also only suggest transit routes if a stop is within reasonable walking distance from the origin and destination. This is generally not a bad rule of thumb. However, there are plenty of situations in which taking a bike-share or ride-hail to or from public transit would shorten the travel time. More significantly, it may make traveling by public transit an attractive option, where previously unfeasible.

The reason for the lack of these modal options lies largely in the apps’ routing algorithms (commonly called a “router”) and the data available to the router. To be able to recommend trips by bike-share, a router first needs accurate information about the location and availability of shared bikes and docks. This information is scattered and, in some cities, not publicly available. Computational complexity is another challenge. There are also many ways to combine transit and first/last-mile services; this is especially a challenge when incorporating dockless shared bikes, as they can be dropped off almost anywhere. Should you stay on the bike the whole way or switch to transit? Which subway station or bus stop should you ride the bike to?

We decided to build a router to help answer these questions, and we are excited to release the first version our Routing API today. This hosted routing service supports trip planning for travels by bike-share, transit, and a combination of the two. We are starting off with the Washington D.C. metro area and New York City, and will expand to other geographies in the near future.

Let us show you a few really cool features of this Routing API. You can see them in action in our demo app, which we hope inspires some ideas of what you could build with this API.

First, you can leverage it to find the best ways to get from A to B using bike-share — docked, dockless, or both.

The router only suggests such a trip if there is a bike available near you right now, and, for docked systems (such as Capital Bikeshare and Citi Bike), if there is at least one empty dock near your destination so you can check in the bike at the end of your trip. The router also takes into account dockless bike-shares’ service areas — where one’s allowed to drop off a bike.

All this bike location, availability, and service area information is made possible through our very own Bike-Share API. We built the Bike-Share API earlier this year to harmonize the 65+ bike-share systems in the country into a single, standard information portal, so navigation apps — such as our demo app — could easily integrate bike-share info into their system.

But, what if the bike or dock availability changes during a trip? Good question! We know it is frustrating to be stranded without a bike, or be stuck with a shared bike when you need to jump onto the subway. We’ll be working on adding predictive insights in future iterations.

Second, this is a multimodal router that thinks in mixed-mode ways. Instead of looking at bike-share and transit as an “either-or” choice, it can surface the best ways to combine the two modes as legs of a trip. Instead of taking a meandering bus, it can be faster (and more pleasant) to take the subway station most of the way and then bike to the final destination.

Numerous research and case studies point to the bike-share systems’ benefits for transit, especially in expanding access to transit and relieving transit congestion during peak hours. We are hopeful that tools that provide inter-modal insights, such as our router, will help planners, policymakers, and operators leverage different modes to create transportation networks that better serve people’s travel needs.

We’d love to hear about what you’ll build with our Routing API and get feedback on features and coverage. Chat with us on our site to get in touch!

This article was written in collaboration with Amy Kyleen Lute. Illustrations by Stephen Kennedy.

Bike N’ Ride: linking first and last mile bike-share with transit was originally published in Coord on Medium, where people are continuing the conversation by highlighting and responding to this story.

New digital tools can help us gain better insights into how people move around cities, and ask better questions about what we want our transportation systems to achieve.

It might come as a surprise to anyone who doesn’t work in transportation, but cities have a pretty limited understanding of where, when, why, and how people travel. For the most part, they have no idea where entire neighborhoods of people go when they leave home (let alone specific individuals). And they know even less about the trips people aren’t taking but might take if they had more affordable or convenient transportation options.

This information gap matters a lot for our cities on the move. For transportation agencies, greater insight into travel behavior helps design roads, manage transit service, and plan capital projects to meet people’s daily needs. It also helps them adapt to the times as residential patterns change, work geographies shift, and new mobility services emerge. Maybe it’s time to change a bus route, or add a bike lane, or partner with an on-demand ride service. And for people like you and me, better information helps us decide how to get around, whether that means carpooling with someone who has a similar commute schedule or avoiding congested roads.

In fact, big advances in travel data have powered many of the recent innovations in urban transportation. Ride-hail companies (like Uber and Lyft) and demand-responsive transit services (like Bridj) require direct access to individual travel needs. Successful bike-share networks depend on knowing which docks are popular at which times. This precise, real-time understanding of who wants to travel where and when enables coordination with available services.

These are big steps forward. But there’s still a long way to go when it comes to understanding how people move around cities. Fortunately, recent advances can help us gain more complete insights, form stronger public-private collaborations, and answer the hard questions about what we want our transportation systems to achieve.

Better insights

For most of the last century, any understanding of travel demand has been founded on surveys. People document details like starting points, destinations, modes, and estimated travel times over a 24-hour period, in addition to their demographic information. Transportation planners and engineers use these data to model and predict transportation patterns across a whole region.

The shortcomings of travel surveys are widely acknowledged. They can’t always capture changes in our day-to-day travel behavior, nor do they reflect people’s tendency to underreport certain trips, especially non-commutes (such as that quick trip to the corner store). They cost a lot to administer with a large, representative sample. In short, travel surveys can’t provide a full picture of how people actually move around our cities and regions.

New technology has started to introduce more granular, wide-reaching, and low-cost ways to capture travel behavior. Automatic counters and cameras deployed on highways and city streets can count cars, bikes, and pedestrian volumes at key intersections or along key corridors. Onboard telematics record detailed data on vehicle location. Transit farecard data and taxi trip logs offer a good picture of when and where people use public transportation. Some researchers have used anonymized call detail records, collected by telecommunication companies, to infer travel patterns within a region.

While a step up from surveys, none of these methods can provide comprehensive, continuous, and multi-modal insights into how people move. But as smartphones become ever more ubiquitous, smartphone-collected data hold the potential to yield increasingly rich mobility insights.

Of course, there are plenty of challenges that come with smartphone data, both technical and social. The raw data are usually large and messy. Sensitive personal data require anonymization and aggregation methods to protect privacy. And smartphone data have blind spots, too; they might miss underserved populations most in need of better transportation options. Calibrating smartphone data towards representative insights will take thoughtful understanding of such biases, and will mean leveraging complementary data sources, new and old.

Better partnerships

Traditionally, one of the biggest barriers to better travel insights has been poor collaboration between the private and public sectors.

Cities and local agencies tend to collect traffic and public transit data, and private companies often develop their own data sources about travel demand, but the two sides struggle to share what they learn. For the most part, government agencies either procure data from companies, or they mandate that private transportation providers report their operational metrics and customer demand. In the case of data procurement, the two sides miss the chance to work together and explore each other’s data methods. In the case of reporting mandates, the result is sometimes antagonism between local governments and companies.

Improving these relationships means aligning value propositions and lowering barriers for exchange. Two concerns commonly cited are user privacy and business competition. Some partnerships, such as Google’s urban mobility work with cities, have explored using statistical methods, such as differential privacy, to protect user privacy while making meaningful use of new data. Admittedly, there is no clear path to resolving the competition challenge, which includes protecting proprietary forms of data-collection. But this should motivate further explorations for a digital and institutional framework that enables data-sharing while reducing a company’s competitive risk.

Better decision-making

It’s critical for both city agencies and tech companies to focus these new data capabilities on pressing urban mobility challenges. Here are some of the biggest ones:

Providing mobility and access for those who need it most. As it turns out, neighborhoods that rely most on public transportation often tend to lack good transit services. As housing costs increase in urban core locations near mass transit, many people with few means have moved to the inner suburbs. Owning a car is an excessive financial burden, and the alternative (or, for some, the only option) is public transit. But due to a mix of political, fiscal, and geographical factors, fixed-route transit services in these lower-density areas are often infrequent and indirect. A 30-minute trip by car can easily take two hours and two transfers by public transit.

Whereas people who work midnight shifts would likely never be part of conventional onboard bus surveys, their phone signals capture their travels any time of day. As cities and transportation agencies re-examine their service policies and routes, mobile phone data might provide valuable insight into where and when these populations travel.

Understanding mobility outcomes. Equally important to making data-driven transportation decisions is conducting data-supported evaluations of policies, programs, and infrastructure projects. But cities have traditionally lacked the tools to understand the impact of transportation interventions on mobility outcomes. This applies to individuals (e.g., how the project impacts personal travel time), infrastructure (e.g., how travelers respond to a road closure), and city networks (e.g., overall congestion patterns).

With sufficient data, these analyses could enrich existing transportation planning and simulation tools. They could also help businesses and residents better envision what a policy, program, or physical change would mean for their daily experiences in the city.

Adapting to new mobility options. A big change is underway in the way cities conceive, manage, and deliver mobility services. Instead of taking ridership and traffic volume as a given, cities and agencies are placing unprecedented focus on understanding users and their behaviors. Some are enhancing multimodal first- and last-mile services to complement fixed routes (via bike-share, TNCs, and taxis), or enabling door-to-door mobility through in-house efforts or partnership with private providers.

Real-world travel data are the starting point to designing such efforts. Want to know where to bolster first/last-mile access? Look into areas that are farther from transit stops but have high automobile travel demand along major corridors. More importantly, we should re-examine travel data during and after any changes, so we can evaluate their impact on mobility outcomes.

Better tools

If you combine all these advances — better insights into how people move around cities, better public-private data collaborations, and better ways to measure mobility outcomes — you can imagine tools that help cities model and manage their transportation networks more efficiently and equitably. Maybe that means starting a new vanpool service or transitioning to a demand-responsive transit service in a low-density corridor. Maybe it’s managing curb space on high-volume streets more dynamically. Or maybe it’s rethinking and reprioritizing funding across various modes and geographies.

Of course, the reality is that transportation decisions result from many factors beyond data: political priorities and constraints, equity considerations, public agency resources, among them. For these reasons, the aspiration should not be blind pursuit of illusory “perfect” data, but rather a push for data that empower and guide us to ask critical questions about quality of life in our cities, and that inspire public and private sides to work together on potential answers.

If you’re a city official working on this problem, we’d love to hear what tools might help you. And if you’re an entrepreneur interested in solving these types of urban challenges, we’d love to hear what you’re working on, too. Contact us.

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Improving urban mobility starts with better travel data was originally published in Sidewalk Talk on Medium, where people are continuing the conversation by highlighting and responding to this story.