A personal account of travel-time estimation, spatial memory and experience-based judgment | October 2026
What I discovered
After approximately six years working in delivery jobs across Greater Montréal, I began noticing something surprising about
six months ago: I could often predict the travel time Google Maps would display before opening its detailed directions. My
predictions were frequently within about one minute of the app's estimate, and sometimes matched it exactly. This report
describes what I actually do, why it may work and what science can - and cannot - conclude from my experience.
The experience behind the skill
My delivery work has taken me through downtown Montréal, Saint-Laurent, Montréal-Nord, the West Island, Laval and the
North Shore, and the South Shore. Repeated trips have made many streets, highway entrances, exits, bridges and
neighbourhoods familiar. I have also learned how long certain sections usually take at different times of day. I do not need to
remember a particular delivery address to use that knowledge.
How I make a prediction
I enter an address and examine the blue route line on Google Maps. I recognise the familiar sections, divide the journey into
parts, recall typical travel times for those parts and add them together. For example:
Route section Starting point to highway Highway entrance to the required exit Exit to final destination Estimated time
7 minutes
4 minutes
6 minutes
Total prediction 17 minutes
This is not a random guess or a calculation based only on distance. I retrieve remembered journey times - for example, that a
particular highway section often takes four minutes - and combine them. I can do this even for a new destination when much
of its route is familiar.
What exactly am I predicting?
The target is Google Maps' estimated travel time, not necessarily my own eventual driving time. Google Maps considers
road networks, current and historical traffic and other factors. My prediction draws on my memory of driving those roads.
Actual journeys can be faster or slower because of traffic lights, congestion, weather, delays and other conditions. A close
match to the app therefore shows agreement between two estimates; it does not by itself prove the actual trip will take that
long.
1 / 2Why this ability makes sense scientifically
An explanation of the mental processes involved, with comparable examples and a way to test accuracy
Four processes working together
1. Cognitive maps. The brain builds an internal understanding of how places connect. Recognising a highway, bridge or
neighbourhood on the blue line helps me identify familiar sections of a journey. Spatial memory and navigation involve the
hippocampus and related brain systems.
2. Chunking. Instead of considering every turn and traffic light separately, I treat a known stretch of road as one meaningful
unit, such as “the four-minute highway section.” This makes a complex route easier to estimate.
3. Long-term memory retrieval. Repeated journeys create practical knowledge of usual travel times. When I see a familiar
segment, I can retrieve an approximate duration quickly, without consciously recalling a specific past trip.
4. Experience-based judgment. Years of repeated driving and feedback can improve pattern recognition. I also account for
the time of day and likely road conditions. This resembles expert intuition, although my method includes a deliberate step: I
add the segment estimates together.
A related skill: estimating weight
I have also noticed that I can sometimes hold a fish, a piece of meat or another object, think for roughly three seconds and
make a close estimate of its weight. Occasionally I hesitate between two numbers before deciding. The nervous system
senses lifting force and movement, while experience can help connect those sensations with known weights.
Proprioception contributes to sensing body position and movement; force perception and learned comparisons also matter.
I have tested weight estimates less often than route estimates, so I cannot claim the same degree of accuracy.
Comparable skills in everyday life
Experienced taxi drivers may anticipate journey times; warehouse workers may estimate package weights; chefs may judge
cooking progress from appearance and texture; and mechanics may recognise familiar faults from sounds. These examples
share a common principle: repeated exposure can turn complicated information into recognisable patterns. Their accuracy
still depends on the conditions and the person's experience.
How accurate is the skill really?
My impression is that my travel-time estimates are correct or very close most of the time. However, a percentage such as
99% would need systematic testing. A useful test would record 50-100 predictions before opening directions, alongside
the Google Maps estimate, time of day, route familiarity and traffic conditions. Then calculate the average absolute difference
in minutes and the percentage within one or two minutes. Recording every attempt, including misses, would make the result
more reliable.
Conclusion
What I initially thought was unusually good guessing appears to be a practical skill built through years of navigating Montréal:
recognising routes, recalling typical travel times, dividing journeys into manageable sections and combining the
results. The ability is meaningful and useful, even without proving it is exceptional compared with other experienced drivers.
It shows how everyday work can quietly develop specialised knowledge that becomes visible only when we stop to examine.
