In recent years, a variety of forces (economic, environmental, and social) have quickly given rise to “shared mobility,” a collective of entrepreneurs and consumers leveraging technology to share transportation resources, save money, and generate capital. Bikesharing services, such as BCycle, and business-to-consumer carsharing services, such as Zipcar, have become part of a sociodemographic trend that has pushed shared mobility from the fringe to the mainstream. The role of shared mobility in the broader landscape of urban mobility has become a frequent topic of discussion. Shared transportation modes—such as bikesharing, carsharing, ridesharing, ridesourcing/transportation network companies (TNCs), and microtransit—are changing how people travel and are having a transformative effect on smart cities.
Changing Mobility Data Collection to Build Smarter Transportation Systems
The Status Quo
Our cities’ transportation systems have a tremendous impact on quality of life – just ask anyone who commutes by car in a major metro area. As James Alosi pointed out in a recent CityMinded blog post, “[Today’s auto-centric mobility] system cannot scale easily, not without significant damage to…the quality of life we seek in urban environments that are truly livable.” Americans have begun to take notice. According the Mineta Transportation Institute’s 2015 survey, “more than 75% of Americans support using tax dollars to create, expand, and improve public transportation in their communities.”
Despite this broad support for public transportation, Americans managed to drive a record-setting 3.148 trillion miles in 2015 per U.S. DOT calculations. While popular public transportation systems in major metropolitan areas like San Francisco and New York face challenges like overcrowding and maintenance delays, systems in smaller cities face lower ridership. It’s clear that there is plenty of room to improve mobility in cities across the U.S. and globally. To build the smart cities of the future, we need to retool the way transportation infrastructure systems are developed so that they’re designed to meet the needs of residents.
Changing Our Data Collection Philosophy
Changing the way that we collect data for transportation planning is one of the most important steps. Instead of relying on old methods – think costly, time-consuming, and infrequently updated household, intercept, and license plate surveys – to build the regional travel demand models that inform major decisions, cities need on-going and accurate data collection.
Perhaps the best opportunity for cities to get such information comes from Big Data. For the purposes of this blog post, by “Big Data” we mean anonymized locational data from mobile devices like smart phones, connected cars, and commercial fleet management systems that is supplemented by contextual data sources. This data accurately, comprehensively, and precisely describes residents’ mobility behavior, so it provides a much more accurate framework for decision-making traditional sources. For more information, take a look at my blog post on “Big, Slow Data.”
It’s not enough to just collect Big Data, though. To build truly smart and sustainable transportation systems, cities must put this Big Data to work. That means using it to 1) Prioritize projects, 2) Drive accountability, and 3) Communicate with the public.
- Measure Twice, Cut Once: Using Big Data to Prioritize Projects
Measuring where people go today can help city planners identify gaps in the public transit systems, estimate which projects will impact the greatest number people, and even identify new opportunities. By using objective measures to determine the projects that stand to make the biggest difference in residents’ quality of life, transportation planners are much better equipped to maximize the impact of valuable tax dollars.
The key elements of “measuring twice, cut once” when it comes to transportation are quantifying the behavior you want to change (i.e.: the number of people in single occupancy vehicles during rush hour) as well as the behavior you want to encourage (i.e.: the number of people carpooling or using a transit shuttle instead of driving).
Let’s say that you’re a city planner who is considering several transportation projects to reduce demand for the limited space on your region’s roadways: a new express shuttle to an existing subway station, expanding that subway’s capacity, and extending a subway line to a new station. Without knowing how many automobile trips have the potential for conversion to other modes, it’s impossible to determine which project has the highest potential.
Thanks to the ubiquity of smart phones equipped with navigation apps (and other apps that use “location services”) as well as “connected cars” equipped with GPS systems, this information is already being collected by private sector companies. Instead of designing complex studies to count traffic, city planners can utilize Big Data to measure travel patterns efficiently and cost-effectively.
- Measure After You Cut: Using Data to Drive Accountability
Since the effects of many transportation infrastructure decisions last 20+ years, measuring “after you cut” is even more important that measuring “before you cut.” Transportation planning as a practice needs to develop better habits by evaluating the impact of the policies, infrastructure changes, and technologies that are deployed. Without measuring after making major decisions, we don’t have evidence of how projects actually perform over time.
By measuring the way a service is used before and after a policy change, urban planners can hold themselves accountable to their goals and to the communities they serve. This means that for every policy or infrastructure change, it’s critical to:
- Collect “status-quo” data: How was this service used previously? (If you measured “before you cut,” that part is easy.)
- Collect “aftermath” data: Has user adoption increased, and have services improved?
- Report back: it’s not enough to simply collect both sets of data. Planners must step back and evaluate whether or not the policy change was effective, too.
Other industries, like Building Energy, have come to pursue performance-based measurements and accountability, and the same benefits will hold for transportation. A great example of this process in action in the transportation industry is Boulder, Colorado’s Living Labs initiative.
As the city of the Boulder implements different pilots of multimodal projects and policies that relate to its 2014 Transportation Plan, they’re collecting data, and they’re sharing it with the public as they do so. The pilot environment has allowed the city to quickly backtrack on projects, such as a controversial road diet, that increased commute times for city residents. Likewise, Boulder is now is expanding one of its protected bike lane initiatives because the data showed their efficacy.
- Using Big Data to Communicate with the Public
Public opposition or support for critical infrastructure improvements and policy decisions can define a project’s success or failure. Construction and alterations to core infrastructure that disrupts the movement of goods and people through communities often face stringent opposition, even when the project is designed to drive major quality of life improvements. However, effective public engagement can also help planners win funding for much-needed initiatives, as well as uncover unforeseen issues before they arise.
To gain the public’s trust and support, urban planners should use the Big Data they collect in stakeholder communications. Coming to the table with the evidence to support your project’s potential can transform the way community meetings unfold for the better. Instead of a dialogue focused on subjective personal experiences, city residents can evaluate the lived reality of their commutes in the context of empirical data. The result is more meaningful feedback for planners, and more meaningful engagement from communities in their transportation systems.
Where We Go From Here
To build truly smart, sustainable transportation systems that improve quality of life, transportation planners must redefine the industry’s approach to data collection. Using data everyday on an ongoing basis as a decision-making tool, and sharing project results nationally so that communities can easily learn from one another are two of the most important first steps.
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Many of these, disaster management included, are the focus of smart city innovations. Not surprisingly, therefore, as they seek to improve and optimize these systems, smart cities are beginning to understand the connection between weather and many of their goals. A number of vendors (for example, IBM, Schneider Electric, and others) now offer weather data-driven services focused specifically on smart city interests.
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