Urban Mobility

Smartphone Apps, Real-time Data, Big Data, and Data Sharing

Smartphone Apps, Real-time Data, Big Data, and Data Sharing: Three ways public agencies can leverage big data to support multi-modal travel

 

Earlier this year, we wrote about Smart Cities and the Future of Transportation. Today, demographic shifts, improvements in computing power and location-based services, the use of cloud computing, changes in wireless communication, concerns about congestion, and increased awareness about the environment and climate change are changing the way people travel and having a transformative effect on cities. Increasingly, mobility consumers are turning to smartphone apps and mobile websites for a wide array of transportation activities including: vehicle routing, real-time data on congestion, information regarding roadway incidents and construction, parking availability, and real-time transit arrival predictions.

 

In a broad sense, technology and more specifically big data is a multi-modal multiplier in today’s transportation network. Technology dramatically reduces a number of barriers (such as trip planning complexities) and allows the entire transportation network to more efficiently and effectively serve even more riders, trips, and miles with fewer resources than before. Digitization of the transportation network – from real-time analytics, mobile applications, sensors, and satellite navigation – is commodifying the transportation network by allowing travelers to make dynamic and informed transportation decisions based on wait-time, journey-time, price, convenience, and a variety of other factors. “Real-time,” “big,” and “shared” data are making this happen. More than ever, leveraging data and real-time analytics at all stages of the traveler process is key for both mobility operators and transportation network managers alike. 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, cloud technologies, and big data are transforming transportation in a number of ways.

 

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First, mobility consumers are turning to mobile websites and apps for a variety of transportation functions, such as: vehicle routing and parking, real-time information services, trip planning, and fare payment. Today, four types of mobile services are having an impact on the transportation network:

Mobility services that 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) that 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 that 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 that 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.

 

For more information on the types of smartphone applications impacting transportation, please see: U.S. Department of Transportation’s Smartphone Applications to Influence Travel Choices and UCCONNECT’s Mobile Apps and Transportation: A Review of Smartphone Apps and A Study of User Response to Multimodal Traveler Information.

 

Second, real-time data analytics and algorithms are being used in many ways to:

– Improve traveler experiences (such as reducing wait times and the number of connections);

– Enhance operations (by enabling efficient dispatch operations and crowdsourced routing);

– Leverage predictive analytics (to more accurately forecast and respond to demand, such as pre-position transportation vehicles);

– Improve operational responses to natural or manmade hazards to keep mobility consumers moving as seamlessly as possible; and

– Identify service gaps and aid in long-term strategic, information technology, and capital planning.

 

Third, the combination of apps, mobile websites, real-time data, and algorithmic information are creating vast amounts of data that enable machine learning and artificial intelligence in data mining to identify patterns and trends. This can allow transportation service providers and public agencies to identify patterns and hot spots in transportation behavior; develop decision trees that help service providers predict modal choice and describe how different factors impact that choice; develop mathematical models that predict modal supply and demand; and predict which modes and services will be used together. Big data combined with data sharing can also allow transportation planners and network managers to identify service gaps and dynamically respond through immediate service adjustments and longer-term service enhancements (e.g., new service, more frequent service, capital projects, and digital infrastructure). This will allow transportation providers and public agencies to more effectively serve mobility consumers and allow travelers to make smarter, more informed, and efficient transportation choices.

 

Third, the combination of apps, mobile websites, real-time data, and algorithmic information are creating vast amounts of data that enable machine learning and artificial intelligence in data mining to identify patterns and trends. This can allow transportation service providers and public agencies to identify patterns and hot spots in transportation behavior; develop decision trees that help service providers predict modal choice and describe how different factors impact that choice; develop mathematical models that predict modal supply and demand; and predict which modes and services will be used together. Big data combined with data sharing can also allow transportation planners and network managers to identify service gaps and dynamically respond through immediate service adjustments and longer-term service enhancements (e.g., new service, more frequent service, capital projects, and digital infrastructure). This will allow transportation providers and public agencies to more effectively serve mobility consumers and allow travelers to make smarter, more informed, and efficient transportation choices.

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Leveraging smartphone apps and big data to encourage multi-modal travel represents a key opportunity for public agencies. Three ways that public agencies can leverage big data to support multi-modal travel include:

Enhance Multi-Modal Payment Mechanisms and Integrated Fare Accounts: With a growing array of modes, trip planning and ticketing apps, and payment options, fare payment is becoming an increasingly fragmented and complex process for multi-modal travelers. An end user may be able to plan an entire public transit trip on a single app, but generally these multi-modal connections require multiple fare payments. Big data offer an opportunity to integrate fare payment into a unified, single process where a user not only uses a single app to plan and execute an entire journey but also a single point-of-sale for the entire trip. Big data also enable multiple payment options to be tied to a single fare account (e.g., a smartphone app linked to a smart card), allowing for multiple payment options (e.g., in case a user’s smartphone battery dies).

Expand Commuter Benefits: Expanding commuter benefits to incentivize multi-modal trips could encourage multi-modal travel by enabling: 1) integrated fare accounts that link to pre-tax commuter accounts (e.g., journeys could be paid for by using pre-tax payroll deductions); 2) employer provided use (e.g., mechanisms that allow employers to pay for commute expenses billed directly to a fare account); and 3) app-based commuter incentives to be linked to a user’s modal choice (e.g., incentives for carpooling or riding public transit, calculated and awarded based on a person’s fare account).

Support Open Data: Providing open data allows public agencies the ability to offer real-time transportation information to mobility consumers, without the cost or responsibility of developing or maintaining mobile services themselves. Public agencies can support open data in five ways:

1) Ensuring data are in an open format that can be downloaded, indexed, searchable, and machine-readable to allow automated processing;

2) Ensuring data are available to the public through open licenses;

3) Establishing data standards that are consistent with industry standards and other public agencies;

4) Developing conditions for acceptable use, including, but not limited to, provisions that protect user privacy and proprietary information of service providers and vendors; and

5) Establishing data commons that serve as a repository for public and private sector data sets.

Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.