Abstract image showing blue, white and yellow lights

Planners must make data-driven recommendations for new transportation infrastructure or road network improvements with a comprehensive understanding of vehicle movements in their regions. To facilitate this, data must be highly thorough and accurate in order to run effective traffic analyses. But does the vehicle data collection method make a difference in the analysis?

When it comes to collecting vehicle data, there are two generally accepted methods. One is connected vehicle data (CV data) from a telemetry source (like Geotab). This type of data uses actual engine-level data that is granular. It will contain contextual information like vehicle classification (Class 1-8), engine type (ICE, EV, PHEV) and more accurate GPS location information. It is very precise because connected car technology lets equipped vehicles send their location and other data directly to a manufacturer or fleet center.

The second data collection method is location-based service data (LBS data). Generally built on mobile data from cell phones and other mobile applications, LBS does not provide visibility into the contextual vehicle information described above with CV data. LBS data will instead infer the type of vehicle (i.e., it could be a heavy-duty truck based on how it moves) but lacks a definitive method to confirm the details.

LBS provides planners with enough insight to know that a vehicle drove down a particular road at a specific time but does not provide insight into vehicle class, engine type, etc. LBS is also more susceptible to GPS drift, a term describing a discrepancy between your actual location and the location recorded by a GPS receiver. LBS can be prone to over-counting biases or capturing duplicates if multiple connected phones are inside a vehicle. 

Here’s a breakdown comparing the two data collection methods:

Connected vehicle Data Location-based services data
Generated by vehicles equipped with various sensors or telematics systems
Derived from mobile devices equipped with GPS or other positioning technologies.
Includes data collected from the vehicle’s internal systems, such as classification, speed, acceleration, fuel consumption and engine diagnostics
Includes data related to the user’s location using GPS
Demonstrates movement patterns and paths of vehicles over time, which supports additional contextual information for analysis (e.g., machine learning for vehicle vocation, identifying vehicle’s home base and understanding the true origin and destination of vehicles)
Trips are identified individually, meaning any historical LBS data cannot be linked together to show vehicle patterns.
Real-time communication between vehicles and external networks or cloud platforms enables continuous data transmission
Data is collected and stored locally on the device or transmitted intermittently when network access is available.
95% location accuracy using antennas for consistent reception within 3 meters
Connectivity issues can surface, resulting in locational errors, especially if a mobile device is kept inside a pocket or plugged into OBD ports

Simply put, all vehicle location data are not created equal. 

The contextual advantage- why planners need the whole picture

To solve complex transportation management issues, analysts need to have all the relevant information at hand. Connected vehicle data provides tremendous granularity into vehicle behavior and captures many data parameters for analysts to slice and dice the information and identify patterns and traffic trends. Transportation planners can start to understand the performance of vehicles and the efficiency of their local transportation networks on a more precise level.

While LBS data provides surface-level insights for traffic counts at a particular time, it does not tell the whole story. CV data can show which types of vehicles are on the road, their purpose for being on specific routes, where they are starting from, and where their destination is. This type of contextual information allows public and private transportation agencies to improve their road networks’ safety, efficiency, sustainability and profitability.

Connected vehicle data analytics use cases

Connected vehicle data offers a comprehensive perspective that is crucial for transportation planners regarding infrastructure management, traffic optimization and safety considerations. Consider these real-world examples of where the added contextual information of CV data, like vehicle class and type, will play an integral role.

  1. Creating safer streets: Tapping into speed and travel-time metrics along popular road segments can give planners visibility into priority areas for traffic-calming strategies. This type of contextual, connected vehicle data gave the New York City Department of Transportation a micro and macro picture of road traffic flow and a way forward to analyze the impact of their infrastructure changes.
  2. Placing charging infrastructure along traffic corridors: Planners are rolling out electric vehicle charging stations for heavy-duty trucks across U.S. highways. Armed with information about where most Class 6-8 trucks are driving and stopping will help support the strategic positioning of charging infrastructure. Geotab ITS teamed up with The Ray to aid in this very venture.
  3. State freight planning: In order to make infrastructure decisions that increase the efficiency of commodity flows and the overall profitability of a region’s road networks, planners need to build informed state freight plans. The most effective plans consider the most heavily traversed routes, how traffic moves through the region for demand forecasting, and where goods are moving to and from using origin and destination information. Geotab ITS partnered with S&P Global to assist WSP’s planning project for Rhode Island’s freight plan by adding contextual data on commercial truck activity across the state.
  4. Studying truck parking: Departments of Transportation must conduct heavy-duty truck parking studies in their states to understand demand and where parking infrastructure gaps exist. Connected vehicle data can be specifically filtered to heavy-duty trucks, so analysts can easily understand stop duration and the amount of driving that occurs pre and post-stop. Read the full truck parking study conducted by Geotab ITS here.
  5. Readying the grid for mass electrification: Utilities and other government agencies are currently trying to determine which areas are more likely to electrify based on existing transportation movement patterns. With more contextual CV data, planners can access historical data to distinguish non-residential vehicles and identify where their home base is located. This information, coupled with vehicle movement patterns, gives analysts a clear picture of the impact on the grid and where electrification needs should be prioritized in the future.
  6. Managing curbside usage: Booms in last-mile delivery have municipalities eager to better manage their valuable curbside space. Connected vehicle data provides insight into what types of vehicles are parking where, when and for how long. Geotab ITS rolled out Stop Analytics to provide added vocation and vehicle class information. Now planners can easily see which vehicles are used door-to-door, like in last-mile deliveries, or with hub and spoke driving patterns of on-demand deliveries like a pizza shop. 


Having CV data makes completing necessary analyses, like the examples above, more accurate and provides better quality data, making the decisions more relevant and timely. This data provides the most user flexibility, with filtering capabilities to get an accurate picture of what kind of traffic is moving and how fast, to where, and for how long.  

In addition to using connected vehicle data, analysts should look for data vendors that are their own data source vs. a third-party aggregation provider. This reduces the risk of loss of access to data. Geotab is the source for all of the data used in the Geotab ITS Altitude platform, with over 3.6M connected vehicles and 55 billion data points.

For more information about the connected vehicle data analytics available in the Altitude platform, visit our webpage.

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