People and their Paths 4: Time and its Mis-perception

A shortened version of this post was adapted for Symposium Magazine‘s article Understanding the Irrational Commuter, which appeared in the September 2013 issue.

Bishop Berkeley said “Esse es Percipe“, meaning “to be is to be perceived”.

Not all is not all time created equal (as discussed yesterday), people systematically misperceive time. This means, sometimes they think places are farther away than they really are, and other times they are closer. Freeways seem to take shorter than they really area, local streets longer. We believe this in part has to do with task complexity, or the “mental transaction costs” involved in traveling.

When I need to make a lot of small driving and navigation decisions, like on a signalized route with lots of turns, I need to focus on driving more times. Each time I am engaging my conscious brain in traveling decisions. More brain-space is occupied by traveling thoughts.

(Other factors include temporal relevance (is the trip important?), temporal expectancies (what do I think the travel time will be?) temporal uncertainty (how reliable is my estimate of travel time?), affective elements (what is the emotional state of the traveler?), absorption and attentional deployment (am I paying attention to the task at hand?) and arousal (how physically activated am I, am I on drugs?).)

When I can drive on an uncongested freeway, I can avoid many such traveling thoughts. Driving is less salient. Time passes faster. As the expression goes, “time flies when you are having fun”

Vierordt’s Law, named for German physician Karl for Vierordt who published “Der Zeitsinn nach Versuchen” — “The experimental study of the time sense” in 1868, says people are more likely to over-estimate short times and under-estimate long times.

Time Perception at Traffic Signals

In our first study considering travel time perception, we wanted to compare how people perceive and value travel time waiting at red lights compared to moving on surface streets. The graduate student working on this, now Professor Xinkai Wu at Cal-Poly Pomona, created a simulation where drivers would see a traffic signal ahead, and get stuck behind a car which waited for the red light. They wait, and they wait, and they wait, and they get annoyed, and they keep waiting; for up to two minutes.

Travel perception experiment at traffic signals
Travel perception experiment at traffic signals

We had a set of scenarios. For instance, in one scenario, they would be waiting 120 seconds on the minor route but then they wouldn’t have any delay at two subsequent traffic signals. In another, they would only be waiting 30 seconds at the first light, but 60 seconds at second traffic light and 60 seconds at a third.

We thus tested (and did not corroborate) Vierordt’s Law. Perceived and actual waiting time were virtually identical for the first 30 seconds. But for times greater than 30 seconds, actual waiting time was higher than perceived waiting time, up to 120 seconds. At 120 seconds, the trend was for perceived time to over-take actual time, but that was the cut-off for the experiment, so perception findings in this situation require more information. However, the annoyance level at 120 seconds of waiting was much higher than the annoyance of waiting 30 seconds. Further people hated stops.

Of course, with all of this, it depends on how you frame the question, what you ask, and what travelers were expecting. Recall that comparing a computer-administered stated preference with one in which travelers were in a driving simulator completely flipped preferences for ramp meters.

In travel surveys we have a common phenomenon of rounding reported times, and times are usually rounded up. So if a trip was 14 minutes, it would be rounded to 15 minutes. If it were 22 minutes, it might be rounded to 25 or even 30 minutes. This makes self-reported times significantly biased in travel analysis. Until recently, that was the only data available. But now with the advent of GPS devices and cheap sensors tracking traffic across networks, we can get much better speed and travel time estimates.

How Network Structure Affects Time Perception

In another study we considered whether network structure affects how people report travel time (which we take to be perceived time, as we have no reason to assume people intentionally lie about  their perceived travel times). Looking at the network in downtown Minneapolis there is a very tight grid of streets, so the block sizes are relatively small compared to, for instance, suburban Woodbury. The older Minneapolis network is a fine-scaled grid while Woodbury is very circuitous and less well connected. Although there is an arterial grid in Woodbury, following the pattern set by the Northwest Ordinance, it’s at a much larger scale (about 1 mile rather than 0.1 mile).

Minneapolis and Woodbury
Minneapolis and Woodbury

We measured network structure along the actual route travelers pursued and compared reported times with our best estimate of measured travel times on their actual routes using GPS data.

We then stratified travelers into two groups, those who underestimated their travel time and those who overestimated their travel time, to see if there were any difference in the network structure experienced by each of these populations.

We measured network continuity (how often you change routes), positing that if you have more discontinuity in your network you’re more likely to overestimate travel time, because you spend more time thinking about.

Similarly, if there is a higher intersection density, more intersections per linear mile, you are more likely to overestimate the time. So each time you have to stop, or think about stopping, because there is potentially oncoming traffic, that’s going to be a mental transaction cost that increases how long you think about traveling, and thus how long you think you are traveling.

However, when your shortest path is along freeways, which have fewer decision points, you are more likely to underestimate travel time.

The accuracy of travel time perception on traffic signal wait duration, network structure, and what kind of route you’re taking. Undoubtedly it depends on many other factors yet to be discovered.


People misperceive the network systematically and we can predict how this misperception works, but of course we can’t predict any one person’s individual perception. On average we can see that in certain conditions some people are more likely to overestimate (or underestimate) their time. We need behaviorally-based route choice procedures. Transportation analysts should think about route choice not (only) as a mathematical problem of how to calculate the shortest path in the network but also about the things that people value, and what are the things that people perceive about the network, both of which will affect individual decisions.