Utilizing data in the transportation industry

The transportation industry is reliant on large amounts of data to effectively route billions of people every day. In this article, we’ll examine how data is used to fuel navigation apps, ensuring that travelers receive up-to-date information to get to their destinations on time. We’ll then look at how data allows public transportation analysts to gather valuable information about how transit is working for different communities, supporting the goal of increasing transit equity for all. 

How Navigation and Transit Systems Use Data

In order to achieve effective transportation insights, large amounts of data on traveler behavior are needed. This data can come in multiple forms. In the earlier years of collecting this data, road sensors played a larger role, but over time, data gathering shifted more toward the user.

Navigation apps like Google Maps, Apple Maps, or Waze gather information from their users to develop an accurate picture of current traffic, and then use that information to make recommendations based on observed conditions.

This information is also used to identify patterns over time and can help answer questions like “When does traffic typically build up on this road?” or “How long does it take to get between two locations during rush hour?” These two pillars of collected data allow platforms to provide a consistently accurate experience, responding to current conditions while factoring in past trends.

This lets users get predictive directions for how long their commute might take, based on historical patterns. Both Google and Apple are secretive about the fine details of how their systems operate, but it’s clear that crowdsourced data forms the backbone of these services.

Broadly speaking, services that capture data on user location fall into the category of geospatial service APIs. These APIs can support other use cases, like optimizing resource usage in public transit systems. For example, data can help urban transit planners determine where traffic congestion typically occurs, and make changes to transit routes to support commuters. 

Ride-share apps like Uber and Lyft also use these APIs to support their business. Using external resources to manage routing removes the need to build such infrastructure internally, which would be a major resource commitment.

Beyond ride-share, these APIs help companies manage the transportation of goods in cities. Increasing delivery route efficiency not only allows companies to use fewer drivers and reduce delivery times, but also helps them work toward sustainability goals. By using a system that can plot delivery routes around customer needs and traffic conditions, companies can see noticeable operational improvements. 

Using Data to Achieve Equity 

In public transit, data enables analysis of how systems are functioning for users. For instance, researchers examined how Boston’s transit system serves disadvantaged populations. 

To measure transit convenience, researchers used data automatically collected by the MBTA (Boston’s transit agency). While the fare system only records where riders tap in, the MBTA uses a tool called the ODX algorithm to estimate where riders get off and whether they transfer. This algorithm combines tap-in data with GPS tracking and automated passenger counters to reconstruct full trips. 

Each trip record includes the rider’s origin, destination, and any transfers. From this, researchers calculated metrics like time on board, number of transfers, and wait time between vehicles. These trip-level details were then connected to MBTA’s network maps and demographic data to examine how access varies across communities. 

The data showed that economically disadvantaged communities had less convenient access to transit. In this case, that meant more transfers, longer wait times, and longer total travel durations. 

While data alone doesn’t solve these issues, it gives transit officials the tools to make informed decisions. Solutions like creating more direct routes, optimizing transfer windows, or expanding bus lane infrastructure become easier to design and implement when data is part of the process. 

Sample Job Posting 

To get a clearer picture of what data science can look like in transportation, here’s a real example of a full-time role at a national lab focused on mobility systems and energy infrastructure. (Some of the original wording has been adjusted for clarity and formatting.) 

• Collaborates with engineers, scientists, and government stakeholders to analyze large-scale transportation and mobility datasets, with the goal of improving the reliability, security, and sustainability of U.S. infrastructure. 
• Designs and tests algorithms and analytics tools using methods from machine learning, statistical modeling, and operations research—often applying these on high-performance computing platforms or cloud environments. 
• Works with data from connected vehicles, charging infrastructure, and mobility systems to simulate future demand and support scenario-based planning. 
• Builds partnerships across government, academia, and private industry to develop new tools for analyzing vehicle/grid integration, fueling networks, and mobility behavior. 
• Communicates data quality challenges, curates datasets, and develops repeatable analysis workflows that produce trustworthy, policy-relevant insights. 
• Publishes research findings in peer-reviewed journals and conference proceedings, and contributes to writing grant proposals to secure project funding. 

This role is a strong example of how data science is being used to shape the future of transportation. It involves working closely with domain experts, using a mix of traditional and cutting-edge techniques, and contributing to research that impacts national infrastructure. While advanced machine learning is part of some projects, much of the work centers on structured analysis, modeling, and clear communication. 

Conclusion 

Data lies at the heart of the transportation industry. Whether it’s delivering accurate navigation or identifying inequities in public transit, its impact reaches far and wide. 

If you’re thinking about a career at the intersection of data science and transportation, consider talking to someone in the field to learn more about their path. If you’re curious about how to get started, or how your background might apply, consider connecting with CAPD. We’re here to help you explore your next steps.