9 Use Cases of Transportation Analytics

Data-driven decision-making is critical to effective transportation planning, operations, and business growth. But many traditional sources of transportation data are incomplete, often leaving behind geographical and temporal coverage gaps, introducing human bias, or excluding some types of road users entirely (such as cyclists, pedestrians, or trucks). On top of these limitations, common data collection methods like sensors and surveys are often expensive and time-consuming, taking a long time to generate insights. 

But transportation analytics derived from big data sources, processed with advanced machine learning algorithms, and rigorously validated by data scientists and industry experts, are helping to fill these gaps. Agencies, consultants, and businesses are increasingly turning to transportation analytics platforms to get actionable insights across dozens of use cases, from congestion mitigation to supply chain management to EV infrastructure planning. 

In this article, we explore nine common use cases of transportation analytics, along with specific metrics and real-world examples you can use to inform your own data-driven decisions. 

The Most Valuable Transportation Analytics Use Cases (Quick List) 

There are many ways to use transportation analytics to make smarter decisions in both the public and private sectors. Some of the most valuable transportation analytics use cases include: 

• Optimizing key routes and corridor performance 
• Traffic operations and management 
• Road safety planning 
• Transit planning and demand forecasting 
• Multimodal planning and active transportation 
• Electrification planning and EV charger deployment 
• Fleet management and logistics 
• Sustainability and decarbonization efforts 
• Social equity analysis 

Below, we explore each of these major use cases in more detail, including the key mobility metrics that can help inform planning, operations, and business decisions in each of these areas. 

1. Optimize Routes & Corridor Performance

Some roadways play a bigger role than others when it comes to meeting transportation demand. Major thoroughfares including highways, arterials, and other high-demand corridors are responsible for transporting large numbers of drivers efficiently from point A to point B. 

Meanwhile, all that traffic creates a lot of wear and tear on roadways and introduces key challenges for congestion management, road safety, climate resiliency planning, and more. Because of the high demand and high visibility of these routes, transportation planners also face special pressure from the traveling public and political officials to manage them effectively and ensure their long-term performance. 

Often, transportation professionals install specialized sensors on major corridors like these to help gather data on vehicle volumes, speeds, and other conditions impacting traffic flow and safety. But while these sensors can provide key information to planners and engineers, they rarely capture traffic conditions along the full length of the corridor due to their limited geographic and temporal coverage as well as the expense of installation. 

Transportation analytics derived from big data can help transportation agencies fill in the gaps with key metrics like: 

• Vehicle volumes and truck volumes – To understand if corridor capacity can support current and future levels of personal and commercial vehicle demand, measure how the corridor contributes to area emissions, analyze corridor safety, and more. 
• Vehicle speeds – Key to understanding corridor safety and congestion bottlenecks. 
• Vehicle Miles Traveled (VMT) –Helpful in measuring transportation emissions, managing congestion, prioritizing corridor resilience, and more. 
• Pedestrian and bike activity– To understand multimodal demand and safety on key corridors, help justify active transportation investments, inform special event planning, and more. 
• Traffic delays and travel times – Important in identifying, diagnosing, and addressing traffic congestion and roadway incidents. 
• Origin-Destination patterns – To understand regional generators of traffic demand and assist in project prioritization, evacuation planning, and more. 

Agencies, consultants, and businesses frequently use StreetLight’s transportation analytics to study corridor traffic patterns that can impact travel reliability, evacuation plans, freight logistics operations and delivery routes, area emissions, and more. 

For example, the Southwestern Pennsylvania Commission (SPC) used StreetLight’s vehicle speed, trip duration, O-D, and other metrics to study a critical 40-mile commuter route in the Pittsburgh area that had gone 25 years without a comprehensive study due to the complexity, cost, and time-consuming nature of traditional data collection methods. 

2. Traffic Operations & Management With Big Data

Put simply, traffic operations and management is about keeping people and vehicles moving safely, efficiently, and reliably across the road network. It focuses on not only how the transportation system is designed, but also how it performs every day and how agencies can respond when conditions change to improve travel reliability and operational efficiency during planned and unplanned disruptions. 

To achieve this goal, traffic operations and management strategies can include: 

• Quick incident detection and response 
• Signal retiming and coordination 
• Real-time congestion monitoring and management 
• Real-time traveler information and alerts (e.g., digital and temporary signage) 
• Special event traffic management 
• Traffic control (e.g., traffic control persons, cones, barriers, or signs) 
• Work Zone traffic and safety management 
• Detour planning 
• Network and corridor performance monitoring and optimization 

When slowdowns and other disruptions occur on one road, it can easily impact nearby roads too, sending ripples throughout the road network. Meanwhile, understanding the severity of these disruptions and planning ahead to avoid them requires context on historical traffic trends. For these reasons, a network-wide view of current and historical traffic conditions is key to effective traffic operations and management.  

Data collection methods like cameras, sensors, temporary counts, manual field observations, and public complaints are commonly used to provide timely insights. However, these methods have limitations that can impact the efficacy of traffic operations and management efforts: 

• Limited geographic and temporal coverage – Field staff can’t be on all roads at all times, and sensors may only be installed on certain high-demand roads, leaving data gaps especially on collectors or arterials. Meanwhile, temporary counts only capture a snapshot in time and may miss intermittent, seasonal, or real-time conditions. 
• Reactive rather than proactive – Public complaints frequently clue operations teams into ongoing traffic issues, but real-time data across the network offers ongoing visibility that can help spot issues before they cause complaints, enabling quicker resolution when disruptions inevitably occur due to construction, events, or roadway incidents. 
• Safety considerations – Manual counts and field observations can put staff in harm’s way, especially when they are deployed to high-speed and/or high-volume roadways. 
• Financial considerations – Sensor installations and staff hours can both introduce considerable expense to the data collection process, limiting where, when, and how long data can be collected, especially for agencies with limited budgets. 
• Difficult to evaluate before-and-after effects – To understand whether operational decisions like signal timing adjustments are actually improving traffic flow or safety, engineers need to be able to compare conditions from after the change to conditions before the change, but traditional data collection methods often only capture either historical or real-time conditions, not both. 

Transportation data analytics derived from big data can help fill these data gaps and provide more granular insights at a lower cost than traditional methods, offering easily scalable coverage of both real-time and historical traffic patterns across a whole jurisdiction. This allows agencies to continuously monitor network conditions alongside historical context to help spot traffic problems quickly, identify effective solutions, evaluate operational decisions with before-and-after analysis, and anticipate how upcoming special events or construction may impact the road network. 

These metrics are particularly useful for traffic management: 

• Traffic speeds 
• Travel times 
• Historical and real-time traffic 
• Vehicle volumes 
• Queue lengths 
• Congestion measures 
• Typical and atypical traffic flow 

How StreetLight helps with traffic operations and management  

StreetLight’s Traffic Monitor product was designed specifically to help with traffic operations and management efforts, helping agencies monitor and anticipate operational impacts with real-time and historical traffic data. 

Traffic Monitor has several key features that support effective traffic management decisions: 

• Route Monitoring 
• Real-Time Incident Feed 
• Automated emails and alerts 
• Queuing behavior 
• Closure scenario evaluation 

For example, the California Department of Transportation (Caltrans) used Traffic Monitor to stay on top of real-time traffic conditions during highway construction in Sacramento, ensuring detours were working, queues were minimal, and road workers had safe conditions while they completed the project. By monitoring these conditions, Caltrans also discovered opportunities to expand construction windows without jamming traffic, enabling faster road work and a shorter construction timeline. 

3. Safety Analytics With Actionable Insights

Road safety planning and operations is a key area where transportation analytics can make an important impact. Many factors contribute to road safety, including how many cars and trucks are on the road, how fast they’re travelling, the interaction with bicyclists and pedestrians, where people turn, how traffic flows through an intersection, how people brake and accelerate, and more. Understanding these factors can be key to identifying targeted interventions that reduce crashes and make streets safe for all road users, including the most vulnerable road users. 

Traditional data collection methods like sensors can help provide information on certain factors like vehicle volumes and speeds, but may leave gaps in other areas. For example, they typically do not provide insight into other traffic patterns that can impact safety such as turning movements and hard braking or acceleration, and can only deliver information on roadways where they’re installed. 

Traffic data collected through GPS devices, connected vehicles, and other big data sources can not only offer increased geographic coverage, but may also offer insights that traditional methods don’t, such as hard braking and acceleration events. 

Helpful safety metrics include: 

• Vehicle volumes and speeds 
• Truck volumes and speeds 
• Pedestrian and bike activity 
• Hard braking and acceleration events 
• Traveler demographics 

How StreetLight helps with road safety insights 

StreetLight offers all the metrics listed above to power data-driven decision-making on road safety planning and operations. Our safety solutions help with: 

• Identifying dangerous streets 
• Understanding where vulnerable road users are most at risk 
• Diagnosing factors that contribute to crash risk 
• Prioritizing the right safety measures in the right locations 
• Enabling proactive rather than reactive safety investments 
• Evaluating the success of safety improvements with before-and-after analysis 

For example, Clark County, home of Las Vegas and the fifth largest school district in the U.S., partnered with StreetLight to address a rise in school zone crashes. The Clark County Office of Traffic Safety used StreetLight’s data to create a highly targeted High Injury Network highlighting school zone crashes, identify effective countermeasures across 378 different school zones with unique safety profiles, and communicate effectively with the concerned public. 

To learn more about how transportation analytics support road safety efforts, check out our Practitioner’s Guide to Solving Transportation Safety. 

You can also explore real-world examples of data-driven road safety strategies in our Road Safety Playbook. 

4. Transit Planning & Demand Forecasting

To plan effective transit routes and schedules, agencies need a clear understanding of the destinations that drive the most travel demand, the origins where most people come from, and where potential riders would most benefit from public transit options.

To gather origin and destination information, agencies sometimes rely on surveys or license plate studies, but these methods can be time-consuming, expensive, and surveys may be subject to bias.

Big data transportation analytics offer a quicker and more efficient way to understand region-wide origin-destination patterns and vehicle trip trends that help planners spot opportunities to optimize schedules, routes, station locations, bus lanes, and more. These same analytics can also help justify transit investments, evaluate the success of transit projects, and increase transportation efficiency by offering multimodal alternatives to personal vehicle use.

Metrics that provide valuable insights for transit planning include:

• Origin-Destination patterns 
• Top Routes 
• Trip details like travel times and trip distance 
• Aggregated traveler demographics 
• Vehicle volumes and speeds 
• Bike and pedestrian activity 

5. Measuring Multimodal Activity With Analytics Tools

Whether you’re designing safe and effective road networks, planning for a special event, choosing a store location, or reducing emissions, understanding vehicle activity alone may not be enough. For many scenarios, it’s critical to understand the movements of pedestrians, cyclists, motorcyclists, and trucks of various sizes too.

But gathering multimodal traffic data can be difficult, especially when it comes to pedestrian and bike activity, which traditional roadway sensors may be unable to count. Specialized bike and pedestrian sensors or manual counts may help, but these can be expensive, time consuming, and limited in scope.

Multimodal transportation analytics can help fill in the gaps with on-demand information about how all road users move, not just personal vehicles.

How StreetLight helps with multimodal insights 

StreetLight offers the transportation industry’s most trusted repository of multimodal mobility data. That includes: 

• Vehicle data 
• Truck data that can be further segmented by weight class (light-, medium-, and heavy-duty) 
• Pedestrian data 
• Bike data 

Many transportation analytics platforms only offer vehicle metrics, but StreetLight’s emphasis on multimodal insights helps deliver a more complete view of how people move, helping agencies, consultants, and businesses make more informed decisions. 

StreetLight’s many multimodal products contain personal vehicle, truck, bicycle, and pedestrian volumes in easy-to-use dashboards that help users quickly answer questions like: 

• Where is walking and biking activity highest in my area? 
• Where are traffic speeds creating potentially dangerous road conditions? 
• Which streets are most congested in my network? And is it personal vehicles or trucks that are most contributing to the congestion? 
• How is truck traffic impacting my road network? 
• What infrastructure investments should be high-priority right now?

6. EV Charging Planning Using Data Analytics

To encourage electric vehicle adoption, drivers need reliable, convenient access to charging stations. But with limited funding, it’s key for agencies to identify the most high-impact locations for EV chargers. This is doubly important because agencies often partner with private developers and utilities to deploy EV chargers, and if these chargers go underutilized, that can disincentivize future expansions of the charging network.

Understanding where to place EV chargers requires insight into how vehicles move today. Metrics like trip length, dwell time, top routes, and origin-destination patterns can help planners understand where electric vehicles are most likely to stop and charge.

For example, when planners in the Silicon Valley wanted to find optimal locations for over 400 public EV chargers to support California’s goal of having five million EVs on the road by 2030, they worked with StreetLight to analyze hourly origin-destination data, peak parking times, personal vehicle vs. truck activity, and other factors to prioritize the best sites for new chargers.

Utilities can also use transportation data analytics to assist in EV charging planning and ensure the grid is ready for increased demand as EV adoption rises. Eversource, New England’s largest electric utility, partnered with StreetLight to do exactly that, using similar metrics to forecast charging demand through 2050, anticipate where and when infrastructure upgrades would be needed, and coordinate long-term electrification planning with area agencies.

7. Fleet Management & Fuel Usage

There are many ways to use transportation data analytics in the logistics and supply chain industry, including key aspects of fleet management such as planning effective routes, reducing fuel consumption, improving supply chain visibility, and electrifying fleets.

While fleet management professionals may have data on how their own fleet vehicles move (e.g., through GPS data coming directly from fleet vehicles), this data alone typically does not provide a full picture of larger traffic conditions that can impact delivery schedules, fuel usage and charging needs, driver safety, and vehicle upkeep. Industry professionals are increasingly using artificial intelligence-powered transportation analytics to fill in these gaps.

To plan efficient routes that reduce fuel consumption and prioritize the safety of drivers and fleet vehicles, these metrics can be useful:

• Vehicle speeds and travel times: These metrics can help you spot congested corridors, anticipate and avoid delays, and evaluate road safety on specific freight corridors. 
• Truck volumes and travel time reliability: These help you understand which routes truck drivers are already using, when truck activity peaks, and how much variance there is in truck travel times on certain corridors, enabling you to better manage delivery routes and schedules to meet customer expectations. 
• Truck activity by weight class (light-, medium-, and heavy-duty), industry, and route type: These factors can inform freight corridor planning and fleet charging and fueling infrastructure deployment based on the different needs and impacts of different types of freight activity. 
• Truck Origin-Destination patterns, trip lengths, and dwell time: Understanding top origins and destinations for truck trips as well as how far trucks travel and how long they stop at certain locations can help inform parking, fueling, and charging infrastructure development and maintenance. 
• Top routes: See which routes are most used to get from point A to point B and prioritize alternate routes drivers can use when needed. 

For example, electrifying truck fleets can help reduce fuel costs and environmental impact, but to do it successfully, stakeholders must first ensure drivers of electric fleet vehicles have reliable and convenient access to charging infrastructure. That means they need to develop charging hubs in the right locations based on real truck travel patterns. Factors like trip length, dwell time, top routes, and more can impact where and when truckers are most likely to need to stop for a charge.

Agencies are already using StreetLight to inform effective fleet electrification, analyzing metrics like truck activity by weight class and route type as well as segment-level truck volumes to identify high-priority locations for fleet charging hubs.

8. Sustainability & Decarbonization Reporting With Key Metrics

Above, we discussed how traffic data can support effective EV infrastructure planning, but transportation analytics can also support sustainability and decarbonization efforts in other ways. Measuring regional greenhouse gas (GHG) emissions from transportation is one key way agencies and businesses can not only streamline reporting but also identify strategies to reduce emissions.

Some agencies and businesses rely on annual traffic counts (AADT) and Vehicle Miles Traveled (VMT) for a rough estimate of vehicle emissions. But for a precise view of local, regional, or statewide transportation emissions, additional granularity and nuance is needed.

For example, AADT and VMT alone may not account for how many vehicles in the study area are gas-powered vs. electric, commercial vs. personal vehicles, or how long these vehicles spend stuck in traffic. These and other factors can impact the amount of CO2 a vehicle emits per mile traveled.

In addition to AADT and VMT, these metrics can help create more complete measurement of vehicle emissions:

• Traffic speeds and travel times: These help account for factors like congestion and travel delays that can lead to increased emissions per mile traveled. 
• Truck vs. personal vehicle activity: Because trucks generate more emissions than personal vehicles, understanding the mix of vehicles on the road provides more clarity for emissions measurement. For even more granularity, look for a data source that can segment truck traffic by weight class (light-, medium-, and heavy-duty). 
• Electric vehicle activity: The number of EVs on the road can also impact overall vehicle emissions. Identifying high vs. low areas of EV activity can provide more precision for GHG measurement. 
• Origin-Destination patterns: To understand how many emissions are generated by each jurisdiction, it’s important to separate vehicle trips that start and end within a given jurisdiction from those that simply pass through or only start or end in that jurisdiction. Accounting for the origins and destinations of trips also helps inform where to focus emissions reduction strategies and avoid wasted investment in mitigation measures that may not actually impact the real sources of emissions as traffic passes through the area. 

How StreetLight helps with sustainability and decarbonization efforts 

Many agencies are already using StreetLight to measure and mitigate transportation emissions because of the high coverage and granularity of its transportation data, especially when it comes to: 

• Truck vs. personal vehicle activity 
• Origin-Destination patterns 
• Electric vehicle activity 
• GHG measurement 
• Congestion metrics like vehicle speeds and travel times 

For example, the Southern Maine Planning & Development Commission used StreetLight’s Origin-Destination metrics to create hyper-localized GHG measurements that would have been too expensive and computationally intensive to access otherwise. These measurements were then shared with other local agencies to inform where mitigation measures would have the greatest impact. 

Similarly, Boulder, Colorado partnered with StreetLight to understand how commuter behaviors and university traffic impact emissions, helping them identify the most effective tactics to reduce GHGs and meet climate goals. 

9. Social Equity Analysis With Big Data Mobility Metrics

Transportation infrastructure is critical to accessing jobs, healthcare, community, and other essentials. When mobility systems serve some people better than others, it can exacerbate unequal outcomes for disadvantaged populations.

Transportation equity can be difficult to measure, especially because many traditional sources of transportation data can’t capture information about who is driving, walking, biking, and using public transport. For example, if all you know about a roadway is how many vehicles use it daily, that doesn’t provide much insight into whether that roadway is equitably serving people of different races, household sizes, or income levels.

But transportation data analytics platforms that can provide insight into aggregated traveler demographics and Justice 40 info can help agencies bring equity to the forefront of planning projects.

For example, StreetLight used truck data and Justice 40 information to analyze how trucks impact disadvantaged communities (DACs) in New York, finding that DACs may bear the brunt of emissions and air pollution impacts caused by truck travel delays.

Additionally, big data metrics such as pedestrian and bike activity can support equitable transportation systems by helping to reveal infrastructure gaps and safety concerns that impact vulnerable road users. Ensuring that people have safe and convenient access to non-vehicle modes of transportation helps support disadvantaged groups who may not be able to afford a car as well as those who have disabilities that may prevent them from driving.

For example, Pittsburgh used StreetLight’s bike and pedestrian activity data to prioritize safety improvements and improve transportation equity city-wide.

Why StreetLight Data Supports These Transportation Analytics Use Cases

StreetLight offers the deepest and most comprehensive transportation data repository in the marketplace, processing vast amounts of data into metrics that agencies, consultants, and businesses use to make smarter decisions.

StreetLight stands out for its:

• Multimodal data, including active transportation and truck data 
• Road safety insights 
• Traffic operations solutions, including real-time traffic monitoring and the ability to compare historical and real-time traffic 
• EV planning and GHG measurement tools 
• Rigorous data validation, including from independent third parties 
• Convenient ways to access metrics, including a self-serve platform, CSV file delivery, API, and more 

For more information on how StreetLight stacks up against other alternatives in the market, check out our StreetLight competitors article.

To get a personalized demo and see if StreetLight can support your goals, reach out to a team member here.