Big Data, Automation, and the Future of Transportation
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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.
Although the concept of shared mobility is not new (ridesharing traces its origins to World War II), early concepts of shared mobility have evolved from manual operations to highly dependent information-technology operations. For shared mobility, technology is a critical enabler and “multi-modal multiplier.” Technology dramatically multiplies the effectiveness of shared modes, allowing existing modes to serve more riders, more trips, and more miles with fewer resources than before. Digitization of shared mobility (and the broader transportation network) – from real-time analytics, mobile applications, sensors, and satellite navigation – allows travelers to be more informed, agile, and mobile in their transportation decisions. Leveraging data and real-time analytics at all stages of the traveler process is key for shared mobility and broader transportation operations and planning. Data understanding can aid public agencies and transportation operators (public and private) build a more intelligent, responsive, and agile transportation network.
The dramatic increase in intelligent transportation systems, location-based services, wireless, and cloud technologies – coupled with the growth of data – has the potential to notably alter our transportation network in a number of ways.
First, end users are employing mobile websites and apps for an array of transportation functions, such as vehicle routing and parking, information services, trip planning, and fare payment. In particular four types of mobile services are having an impact on the transportation network. These include:
- Mobility services, which assist users with routing, booking, and payment of single and multimodal trips. This can include shared mobility (business-to-consumer and peer-to-peer sharing apps), public transit apps, real-time information apps, taxi e-Hail, and multimodal aggregators;
- Courier network services (CNS), which offer for-hire paid delivery services for monetary compensation, use an online application to connect couriers using private vehicles, bicycles, scooters, or other equipment with light cargo;
- Vehicle connectivity services, which provide vehicle diagnostic information and enable remote access and dispatch emergency services (e.g., accident and roadside assistance, unlocking a vehicle); and
- Smart parking services provide information on parking costs and availability. This includes “e-Parking” services to reserve and pay for parking and “e-Valet” services that connect vehicle owners to valet drivers to pick-up, park, charge or refuel, and return vehicles.
Second, invisible to the traveler but impacting the traveler everywhere, real-time data analytics, and algorithms are being used in a variety of ways to improve the traveler experience, enhance operations (such as managing crowdsourced and flexible routing), provide predictive analytics to more accurately forecast and respond to demand, and improve operational responses with natural or manmade hazards impacting usual transportation operations.
Together these transformative trends are creating vast amounts of data that will enable travelers, transportation providers, and public agencies to make smarter, more intelligent, and efficient transportation decisions. While transportation service providers, such as public transit and ridesourcing/TNCs (e.g., Lyft and Uber), use a variety of data and data sources in their modelling and operations, big data coupled with data sharing has the strong potential to enhance transportation planning and traveler services by empowering operators and policymakers to better understand the current state of the transportation network and more accurately identify services gaps and respond through immediate service adjustments and longer-term infrastructure improvements.
For example, during the 2014 World Cup in Rio de Janeiro the local government obtained driver navigation data from Google’s Waze and combined it with information from pedestrians using the public transit app Moovit. The aggregated data provided local authorities with valuable real-time information about the transportation network. The combined Waze and Moovit data feeds allowed local transportation planners and engineers to aggregate data on more than half a million drivers over a month-long period to identify thousands of operational issues, such as network congestion and roadway hazards. In exchange for sharing user data with the government, the government shared its own network data (e.g., sensors, construction information, etc.) with these private services, exemplifying how the public and private sectors can mutually benefit through data partnerships where previously the local transportation department had been solely reliant on road cameras and roadway sensors.
Data sharing and interoperability has and will continue to form the foundation of future transportation innovations. To foster transportation innovation, public agencies should consider establishing open data standards, privacy provisions, and data exchanges to serve as a repository for public and private sector data sets. The public sector can support innovation by:
- Ensuring Data Accessibility: Confirming data made available are in an open format that can be downloaded, indexed, searchable, and machine-readable to allow automated processing.
- Establishing Open Licenses and ensuring data are available to the public for use.
- Creating Data Standards: Determining the format and standards for publishing data sets that are consistent with industry standards and other public entities, as well as addressing interoperability issues.
- Developing Conditions for Use: Developing standards for data dissemination and conditions for acceptable use, including, but not limited to, provisions that protect user privacy and proprietary information of service providers and vendors.
Urban transportation is changing rapidly, and big data are making it happen. Today, data are enabling four emerging and future innovations:
- Automated Vehicles (AVs) and Shared AVs (SAVs);
- Automated Aerial Vehicles (AAVs);
- Drones; and
- Robotic Delivery.
In the design and testing phase of these innovations, sharing data between vehicles, drones, robots, equipment, smart infrastructure, manufacturers, and public agencies will be key to documenting failures, safety incidents, and security breaches. Once operational, extensive data sharing and real-time analytical capability will be needed to ensure interoperability, connectivity, and continuous network monitoring to prevent, respond, and document failures and security breaches.
Automated Vehicles (AVs)
An automated vehicle is a car that is capable of sensing its environment and navigating without human intervention (U.S. Department of Transportation). The U.S. Department of Transportation has defined different levels of automated functionality, ranging from no AV features (Level 0) to full automation (Level 5) (Stocker and Shaheen 2016). At the most advanced stage (Level 5), vehicles are completely self-driving without human controls in all driving environments that can be managed by a human driver.
Shared Automated Vehicles (SAVs)
SAVs are a fleet of shared vehicles used to move passengers or cargo with some level of automation that aims to partially assist or fully replace human control. A number of SAV pilots are underway; however, estimates of Level 5 SAV deployments typically vary from 10 to 30 years based on assumptions involving technological readiness, SAV pricing and business models, and public policies and infrastructure required to enable the mass deployment of fully automated vehicles.
Automated Aerial Vehicles (AAVs)
Today, a number of firms are developing automated aerial vehicle (AAV) prototypes. For example, Airbus is planning to unveil a flying automobile prototype in late 2017. While this first model will be piloted, Airbus says a fully autonomous model is in the works. In China, EHang has developed a quadcopter passenger drone powered by eight propellers that can travel at speeds of up to 60 miles per hour at altitudes up to 1,000 feet. Officials from Dubai’s Roads and Transport Authority will be launching an aerial taxi service using the Ehang184 model in late 2017. UberElevate has hired former NASA engineer Mark Moore to develop a flying taxi proof of concept. The company envisions users dispatching an electric automated aerial taxi via the Uber app, with the capability of flying between 50 to 100 miles on a single charge. Uber previously used drones to advertise its ground-based transportation services in Mexico City.
A delivery drone is a short-range unmanned aerial vehicle that can transport small packages, food, or other goods. A number of firms are testing aerial package delivery, such as Amazon Prime Air, which made its first successful commercial drone delivery to a rural customer in December 2016. Some service providers, such as the United Parcel Service (UPS), are experimenting with pairing drones and truck-based delivery to improve service delivery. UPS’ hybrid system allows a courier to make a truck-based delivery, while simultaneously delivering a second package using a drone from the roof of the courier truck. The hybrid truck-drone delivery system may improve efficiencies, particularly in rural locations, reducing the need to drive long distances between deliveries.
Across the country, grocers, retailers, and food establishments are experimenting with delivery robots. Broadly, these electric delivery vehicles typically use a combination of cameras, sensors and satellite navigation systems to operate. At the point of delivery, a user typically uses a smartphone, pass code, or facial recognition to accept delivery. Earlier this year, a fleet of 20 autonomous 35-pound delivery robots built by Estonian-based Starship Technologies began online deliveries for Postmates in Washington D.C. The same company will soon begin a 12-month pilot to deliver groceries, small parcels, and take-out in Concord, California. Five states (Florida, Idaho, Ohio, Wisconsin, and Virginia) have amended state laws permitting robotic delivery vehicles to operate on local sidewalks. Overseas, Chinese e-commerce retailer Jingdong has recently experimented with robotic delivery. Similar plans are underway to launch a sushi delivery pilot in Japan in the coming months.
While the full impact of these technologies is only just beginning to be recognized, these four transportation innovations could be among the biggest game-changers for urban mobility and goods delivery since the development of the internal combustion engine.
 For more information on shared mobility service models and definitions, please see U.S. Department of Transportation’s Shared Mobility: Current Practices and Guiding Principles.
 For more information on smartphone applications impacting transportation, please see U.S. Department of Transportation’s Smartphone Applications to Influence Travel Choices.
 For more information on data sharing best practices and guiding principles, please refer to the U.S. Department of Transportation’s Smartphone Applications to Influence Travel Choices (Chapter 6).
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