Rideshare and delivery platforms log an enormous amount of GPS data, far more than most passengers or attorneys realize. Every trip generates a stream of location points showing where a vehicle was, how fast it was moving, and whether it deviated from an expected route. In litigation involving driver conduct or passenger safety, that data can be some of the most objective evidence in the case, if it’s read correctly.
This is one of the more frequent case types in my practice. I’ve reviewed many rideshare litigation GPS records for multiple cases. The questions range from wanting to discern if a vehicle was moving or stopped at a given time, did it deviate from the expected route, how long did it remain at a particular location, and how reliable is the record.
What Rideshare GPS Data Actually Contains
Rideshare platforms typically log location data at frequent intervals throughout a trip, not just at pickup and drop-off. The data can show a vehicle’s path, its speed at various points, and any stops or detours along the way. The precision and frequency of that data can vary by platform and device. From this data, I create route maps, timelines, and route animations.
Movement vs. Stopped Time
One of the most common questions in rideshare litigation is whether a vehicle was moving or stationary during a specific window. GPS data can usually answer this with reasonable confidence. It’s important to have an engineer with ample education, training, and experience in GPS principles, design, operation, and data analysis interpret the GPS evidence in your case.
Route Deviation
Whether a vehicle followed its expected route, or deviated from it, and for how long, is a recurring question in these cases. A short deviation might be explained by traffic or navigation error; a longer or more unusual one raises different questions. Distinguishing between the two requires comparing the actual GPS record against what a typical route for that trip would look like.
Dwell Time at a Location
How long a vehicle remained stopped at a particular location is often central to these cases. GPS data can establish this with more precision than witness recollection alone, provided the analysis accounts for GPS drift, a phenomenon inherent to many GPS receivers where a stationary receiver, like the one in your phone, can appear to “wander” slightly even when it hasn’t moved.
Where This Data Gets Misread
A common mistake I see in rideshare GPS analysis is treating raw location points as if they were perfectly precise, when GPS data always carries some margin of error. A device that appears to have moved fifteen feet in an urban environment surrounded by tall buildings may not have moved at all; that’s a well-documented GPS accuracy limitation in urban settings, and may or may not be evidence of movement. Getting this distinction right, and being able to explain it clearly, is often the difference between an analysis that holds up and one that falls apart on cross-examination.
Another common mistake occurs when a route includes back-tracking over previously traveled roads. It’s difficult to show this type of movement on a static route map. I create animations that clearly show the route traveled over time, regardless of how complicated the route was.
Why This Requires an Engineering Background, Not Just a Map
Reading a GPS track accurately requires understanding how GPS technology actually works: satellite geometry, signal degradation in urban canyons, and the accuracy limitations inherent in consumer-grade GPS chips versus dedicated survey equipment. I bring a graduate-level telecommunications and wireless engineering background to this analysis, along with a Six Sigma Master Black Belt in the quality sciences, which means I can quantify the uncertainty in a GPS track rather than simply asserting a conclusion.
How I Approach These Cases
- Data acquisition and review. I request and review the full GPS log and other related materials for the trip in question.
- Accuracy assessment. I evaluate the precision of the underlying data, including the device type, logging frequency, and environmental conditions that affect GPS accuracy.
- Mapping and timeline construction. I build a clear visual timeline showing the vehicle’s movement, speed, and any stops.
- Report and testimony. A report written for readers without a technical background, and testimony that holds up because the underlying methodology is sound.
Why Attorneys Bring Me These Cases
- High case volume in this specific area. Rideshare and app-based GPS litigation is one of the more frequent case types in my practice, not an occasional sideline.
- I don’t work for rideshare platforms. I have no conflict of interest — I’m retained on these cases by the passengers’ or claimants’ attorneys.
- I’ve never had an opinion limited or excluded. No Daubert challenge has ever succeeded against me; my GPS methodology has held up consistently across contested matters.
- Rideshare testimony experience. I’ve testified in depositions and in court on GPS location records in many rideshare cases on behalf of plaintiffs.
- Decades of experience. I’ve been analyzing and mapping GPS location data since the early 2000s, long before GPS was built into mobile phones and other devices.
Frequently Asked Questions
How accurate is GPS data from a rideshare app?
It can vary by platform, device, and environment. Consumer GPS is generally accurate to within several meters under open-sky conditions, but that accuracy can degrade near bridges, mountains, in cities, tunnels, and indoor environments. Any analysis needs to account for that variability rather than treating every data point as equally precise.
Can GPS data prove a vehicle was stopped versus just moving slowly?
Usually, yes, with the right analysis. Distinguishing genuine slow movement from GPS drift on a stationary vehicle requires looking at the pattern of points over time, not just their raw coordinates. When speeds aren’t provided in the records, I compute them based on the distance between adjacent GPS points, using the Haversine formula, and the time difference between them. When the platform does provide speeds, I verify them and report any relevant discrepancies.
Do rideshare platforms retain this data for very long?
Retention periods vary by platform and are worth confirming early in a case, since the underlying data needs to be requested and preserved before it’s no longer available.
What if the GPS data has gaps?
Gaps happen, and an honest analysis says so rather than filling them in with assumptions about what occurred between logged points. I flag data gaps explicitly in every report.
If your case involves GPS data from a rideshare or delivery platform, I’d welcome a direct conversation about what the data can and can’t establish.
Ready for an expert opinion? Get in touch today!
Mark McFarland, PE, is a forensic electrical and telecommunications engineer in Boulder, Colorado. He spent twenty-one years at the federal telecom research lab before founding Discovery Engineering in 2015, and is retained nationwide by plaintiff and defense counsel in civil and criminal matters.
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