Contents
Every day, millions of businesses and end-users rely on location matching to make effective decisions.
There are several types of location matching including spatial matching, data alignment, address alignment, and contextual alignment.
Spatial matching is central to the success of online marketplaces that need to connect locations together, for example connecting a job seeker to a job opportunity, a property seeker to their new home, or connecting people to local services.
However, most applications today still rely on outdated geospatial methods that introduce significant inaccuracies when matching locations.
Traditional location-matching techniques—such as geoqueries and distance-based calculations—fail to reflect how people travel through the world. This results in inaccurate spatial insights, poor search relevance and results matching, and lower business performance.
Why do traditional methods fall short and how can APIs like TravelTime deliver high-accuracy location matching at scale—without sacrificing performance or cost-effectiveness?
Why Traditional Geospatial Matching is Inaccurate
Today, the most common approaches to spatial location matching can be categorised into geoqueries: database location retrieval, and routing APIs: travel distance or time calculations.
While commonly used across online marketplaces, both methods fall short when it comes to accuracy and performance.
Geoqueries: Fast but Inaccurate
Geoqueries are functions inside of an existing database that rely on abstractions—points on a flat grid—to store and retrieve locations. This simplification means that geoqueries can be fast and scalable, but they ignore real-world transport networks like roads, railways, or bodies of water, and therefore cannot reflect how people really travel, resulting in low accuracy.
Common geoquery types include:
Bounding Box Queries
Finds all locations within a specified rectangular region, for example finding all of the theatres within a given area, updating search results as a user pans or zooms a map.
Distance Queries
Finds all locations within a radial distance of a given point and can support the ranking of locations by that distance . For example, “Show me all job listings within 5 miles of my address.”
Polygon Queries
Polygons find all of the locations within a given polygonal shape, for example using a zipcode, postal code, or the boundaries of a city or area like “Show me all properties within the boundaries of Williamsburg.”
Point-to-Point
Calculates how far apart two specific location points are, for example “The nearest park is 4 kilometres from this property.”
While geoqueries can offer speed and scale, they assume a flat world where distance equals connectivity—leading to poor search results and an inaccurate understanding of location.
This outdated approach to location matching at the data source degrades the quality of results and threatens business performance.
Routing APIs: Accurate but Costly and Slow
Routing APIs calculate actual travel routes and can provide much higher accuracy than geoqueries. They can be effective for use cases like factory logistics or on-demand delivery, when the impact of higher accuracy is directly valuable to the business.
However, many solutions become inefficient or costly when handling high volumes of locations.
Scale
Most routing APIs can only process small numbers of locations at once without compromising on performance.
Performance
Each request requires real-time calculations, limiting the speed in which they’re delivered.
Cost
Pricing is based on per-request usage, making large-scale location matching costly.
Licensing
Many routing APIs prevent data storage and caching results, limiting application flexibility.
Trade-off Between Performance, Scale, and Accuracy
Companies relying on traditional geoqueries and routing APIs are forced to make difficult compromises.
How can we quickly process location queries at scale, that reflect real-world travel behaviour, while controlling our data and remaining cost-effective?
Too often, prioritising one factor comes at the expense of another: Focusing on speed and scale means reducing accuracy, while focusing on accuracy means slower performance and higher costs.
This constant trade-off prevents you from achieving optimal location matching—until now.
Fixing Location Matching with TravelTime
TravelTime eliminates the trade-off between performance, scale, and accuracy.
Unlike geoqueries and routing APIs, TravelTime calculates real-world travel times and distances between multiple locations in milliseconds.
How TravelTime Works
At TravelTime, we eliminate the trade-off between performance, scale, and accuracy in location matching.
Unlike geoqueries and routing APIs, TravelTime calculates real-world journey times between multiple locations in milliseconds—delivering fast, scalable, and precise location insights to support quality matches.
Business Impact of Accurate Location Matching
With TravelTime, businesses no longer have to compromise on performance, scale, or accuracy when it comes to location matching.
For online marketplaces, this means delivering highly relevant search results, improving match quality, reducing bounce rates, and unlocking new revenue opportunities. All while enhancing the user experience.
By prioritising accurate, time-based spatial matching, organisations eliminate the need for costly workarounds and manual fixes, freeing up valuable time and resources. Instead of troubleshooting accuracy issues, your teams can focus on innovation, growth, and delivering smarter location-based solutions.
The Future of Location Matching
Location matching in 2025 demands a better approach: one that balances speed, scale, and accuracy without unnecessary compromises.
By replacing outdated geoqueries and routing API limitations, TravelTime is setting a new standard for location matching and a better approach to geospatial and location intelligence at the source.
Want to see how TravelTime can improve your location-based application?