Mobility Data Analytics:
For Whom, When, and Why?

Recently, we discussed the differences and similarities between data analytics and data science. Today, we’re going to investigate the first one in more detail, focusing on its use in the context of mobility. Who uses data analytics solutions, what are the core use cases, and what aspects to consider when applying analytics to mobility? Check out this introduction to our 3-part series on data analytics SaaS.

In 1959, Chicago Daily Tribune prophesized the arrival of flying cars within the next few decades. Sixty-three years later, we’re yet to see airborne vehicles coming to fruition (even though the recent Jetson ONE flying car launch seems to suggest that D-Day is finally approaching). Their commercial availability will undeniably be a milestone in the transport and mobility industries. Still, breakthroughs like this wouldn’t be possible without enabling technological advances that are unraveling in our eyes.

So, while we are as excited as you might be about the vision of hovering around in flying automobiles, let’s concentrate on here and now for a moment, breaking down an existing tech innovation—mobility data analytics. We’ll learn how it makes mobility and transport increasingly safer, more affordable, and efficient, naming the most common use cases it resolves for individuals, entire industries, and communities today.

Jetson one

Airborne vehicles are likely to materialize soon, but until that happens, let’s zoom in on advances that already power modern mobility


What you'll learn

What is data analytics?

We have covered data analytics in detail previously. Thus, to avoid repetition, we will briefly recall its definition and key facts before we move on to practical examples of how data analytics addresses common mobility issues.

For review purposes, here’s how we can define data analytics:

Data analytics refers to analyzing data sets to conclude, solve problems, anticipate trends, predict the course of action, answer questions, unlock decision-making, and identify opportunities. 

Depending on the project, the work of a data analyst may include tasks such as gathering information, evaluating, cleaning, and preparing data, aggregating it into data sets to analyze, and drawing and visualizing insights. 

Another crucial thing to remember is that there are various approaches to data analytics, each with its own set of applications. Big data sets can be explored to reflect on reality (descriptive analytics), identify the root cause of an issue (diagnostic analytics), predict future events (predictive analytics), and even suggest solutions to anticipated issues (prescriptive analytics).  

How is data analytics used in transport and mobility?

Data analytics is omnipresent in modern vehicles, transport networks, and logistics systems. From AI-driven engines informing car design and enhancing automobile production lines, through route optimization algorithms used by public and private transport companies, to data-powered smart infrastructures, data is the lifeblood of mobility in all shapes and forms. 

However, while the reactive data approach, which uses information to respond to events that have already occurred, is commonplace, proactive mobility data analytics is still relatively underutilized. We’ll illustrate that with an example.

Reactive vs. proactive mobility data analytics

Think onboard vehicle diagnostic (OBD) systems, which are mounted in vehicles to capture information about the car’s location, speed, mileage, and braking. This data-capturing solution has been legally mandated in many countries for years (the USA mandated the use of OBD in cars in 1996, while the European Union followed suit a few years later). 

Simple mobility analytics software can read the information from these telematics devices and provide useful insights into the accident or breakage cause, for example. However, it cannot think forward based on the telematics knowledge enhanced with contextual data about the trip, driver, road & weather conditions, etc. Reactive data analytics systems act retrospectively, analyzing historical data, concluding about the past, and providing diagnoses, but not solutions. 

Now, let’s take it to another level. 

By applying advanced AI algorithms, mobility data analysts can move the needle from reactive to proactive solutions, taking the same data sets and augmenting them with more context. Enriching the ODB readings with information about weather and topology, dash cam footage, and the driver’s behavior patterns, for instance, opens a whole new opportunity to derive conclusions and predict and prevent certain events. 

Analytical insights from mobility data can be used to understand the impact of driver behavior and trip context. This knowledge is invaluable, for example, for fleet managers, who can coach drivers to use less fuel or reach the destination faster. Insurance companies can also use it to build attractive loyalty and reward programs for their customers and offer them competitive, tailored rates. 

And these are just two examples of industries that can thrive on sophisticated mobility data analytics. We will cover more verticals later, but first, let’s see what kind of data AI-driven mobility software can leverage.    

Proactive vs. reactive data analytics in mobility


Digs into data to anticipate and prevent potential issues and foresee future events.

Uses intricate statistical algorithms and advanced AI and machine learning techniques.

An emerging trend that is gaining traction among transport, delivery, logistics, and supply chain companies.

Examples: optimizing estimated arrival times, mitigating accident risks, preventing running out of fuel/battery, anticipating traffic flows, unlocking predictive maintenance.


Uses data to respond to events that have already occurred/are happening now.

Applies less complex statistical and computational methods and basic business intelligence (BI) techniques.

A traditional, well-established approach that is commonly used across the mobility sector.

Examples: location and geospatial tracking, detection of idle times, identifying accident causes, providing real-time information on the vehicle’s status and existing faults


Is more data better for mobility analytics?

While it might seem intuitive that the more data, the better outcome, as is often the case, quality is more important than quantity in mobility analytics. 

Of course, mobility data analytic platforms operate on large data volumes to provide actionable insights. However, depending on the use case and goal, valuable information can be pulled from just GPS data enhanced with context information (like weather or road conditions). Thus, even small mobility companies can benefit from the data analytics magic (or rather, the application of computer science and statistical algorithms). An experienced data analyst will be able to turn relatively small data sets into powerful outcomes.

For instance, combining simple GPS data with information about weather, road, and environmental conditions, plus driving patterns (acceleration, braking, cornering, etc.), can help infer the wear of individual vehicle components in specific circumstances. 

Another example may be assessing or optimizing the transition to electro-mobility. By applying specific algorithms, data analysts can evaluate the driver’s/fleet eligibility to switch to electric vehicles based on information about the length of trips, daily distance covered, driving patterns, and charging stations distribution.

What data can be used for data analytics in mobility?

Depending on the use case, mobility analytics systems can use a few dozen to a few hundred information elements to develop actionable data insights. While it is impossible to list them all, they can be grouped into several key categories. 

Together, these elements analyzed provide enhancement insights into the driver’s safety, eco-efficiency, fuel-efficiency, risk behaviors, fleet composition, and vehicle wear and maintenance, among others.

Categories of data used for mobility analytics include:

  • General trip information – such as trip distance and duration
  • Vehicle information – make, age, type, specification, assistance systems, engine, etc.
  • Road environment – road type (rural, urban, highway), surface type, surface condition 
  • Road topology and signage – intersections, traffic lights, pedestrian crossings, stop/yield signs, elevation, turns, tunnels, hard shoulders, etc.
  • Traffic information – congestion, road events, maintenance works, detours
  • Weather information – dazzling sun, snow/ice, visibility, wind, rainfall
  • Points of interest – landmarks, speed checks, gas stations, parking lots, charging stations
  • Driver’s characteristics – speeding, accelerating, braking, resting, complying patters
  • Fleet composition – gas/diesel/electric/hybrid cars, vehicle condition, makes, age,

Who uses data analytics to optimize transportation?

As seen above, diverse data elements can be collected by analytics platforms to inform modern mobility solutions. But what about their beneficiaries? 

We already mentioned two industries that take advantage of data-driven mobility software, fleet managers and motor insurance companies. Many more verticals can benefit, including but not limited to:

Courier and delivery services

Logistics operators, including shipping and last-mile delivery companies, can leverage data-driven analytics to streamline their operations and increase profitability. For example, by analyzing trip and driver information, they can optimize routes to reduce drop-offs and shorten the time and distance to deliver parcels.

Another interesting scenario for shipping service providers to consider is to use insights from their fleets to adjust delivery charges, e.g., in the areas where couriers spend the most time (for instance, due to heavy traffic or topology that affects delivery times). They can also tweak marketing and promotions initiatives based on trip analyses to focus on underserved areas or increase services on the most profitable routes. 

Municipalities and road operators

Data is essential to inform the effective design, development, and maintenance of urban and rural transport networks. Using various data sets that provide insights into transport modes and uses, combined with contextual information, local authorities and road operators can drive optimizations to make public and private transport more convenient and less congested. 

For example, by integrating data from traffic detectors, telematics devices, traffic events databases, and weather reports, they can develop real-time traffic predictions in the short and long term and apply preventive actions. Advanced mobility algorithms help transport providers understand where and when the heaviest traffic occurs, which road sections are specifically accident-prone, and the main root causes of road events and accidents. 

Identifying transport bottlenecks also helps adjust road networks to mitigate risks, reduce congestion, and implement measures for safer, more sustainable traffic outcomes. Finally, thanks to predictive analytics, local authorities can roll out the EV charging infrastructure where gaps occur or consider adding more parking spaces in the city as they observe increasing traffic trends.

Car-sharing and carpooling organizations

Car-sharing companies use valuable insights into the performance of their fleets to optimize their business in numerous ways. For example, analyzing traffic patterns makes it easier for them to balance supply and demand. Or, insights into how customers use and treat their fleets help adjust pricing for greater profitability.

Like other fleet-managing organizations, carpooling service providers can also leverage data insights to optimize fleet composition, evaluate the transition to EV fleets, or improve maintenance proactively to decrease costs and boost the residual value of their cars.

The police

The police may leverage insights about road type, speed categories, road topology, accident history and severity, and other elements to increase safety in the transport network. By working out the correlation between road event severity and other factors, they can apply corrective measures and adjust the speed limits, suggest road topology and organization enhancements, tailor citizen campaigns, and become more effective in enforcing road safety.

Before you go: Top 10 use cases of mobility data analytics

Innovation is part of the human experience, and we’ve always looked for ways to improve and excel. (Mind you, the dreams of flying precede Da Vinci’s ornithopter design; just think Icarus and Daedalus or Biblical prophet Elijah’s chariots of fire). So, whenever a new concept or idea emerges, people continuously enhance it to make their lives, work, and leisure more efficient, pleasant, easier, or more profitable. The same goes for mobility data science.

People have been analyzing data since the beginning of human existence, but it’s only in the 20th century that this practice has become a scientific discipline. Over the years, data analytics has gradually evolved, addressing more and more cases, and resolving new issues. Now, we are experiencing a shift from reactive to proactive data solutions, becoming an essential element of growth for forward-looking transport and mobility companies and organizations. How? Here are the ten most common instances:

  1. Accident prevention
  2. Predictive maintenance
  3. Decreasing fuel/energy consumption
  4. Route optimization
  5. Boosting residual vehicle value
  6. Increasing passenger safety
  7. Launching driver coaching programs
  8. Enabling price elasticity (car rental/insurance)
  9. Unlocking faster claim settlement
  10. Reducing vehicle tear & wear

In truth, the scope of mobility data analytics use cases has no limits. It only depends on your imagination, combined with the data scientist’s capabilities and the quality of data collected. So, if you have some ideas you would like to run by data experts, validate them immediately by reaching out to us

And to learn more about how to empower your business with data, stay tuned for the upcoming articles in this series, where we will delve into predictive analytics and optimizing residual vehicle value using data analytics SaaS.

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