In the late 20th century, a person would estimate the length of a trip, primarily based on experience. For a trip from New Jersey to Florida, the mileage would be calculated, and a straight-through trip would be estimated based on meal times, known speed traps, etc. For an around-town trip, experience and time of day would dictate whether to allow 15 minutes or 30. If 15 minutes had been allowed, and the trip seemed to be taking longer, the driver may attempt to make-up time by driving a little faster. For a couple mile trip, however, this was unlikely to matter appreciably – except that the driver had a sense of control.
In the last fifteen years, automation has begun to change this. Even the earliest GPSs calculated how far a destination was. Based on the known speed limits and distance, reasonable estimations could be made regarding how long a trip should take. These calculations would include reasonable estimates for lights and stop signs. They would not include “actual highway speeds” whether those speeds were over the speed limit (speeding) or under the speed limit (volume, construction, accidents.)
In the last several years, live data has been incorporated into both the trip time, and the suggested route. This allows users to be routed away from traffic. Some applications can even allow you to enter a proposed departure time, and will offer an estimated destination time based on historical traffic patterns and planned construction. For example, if I’m planning on going from LA to San Diego at 7:00 AM I’ll get a far different destination time than if I plan on leaving at 10:00 PM. This is one of the most prevalent automation features currently available. One of the weaknesses in this predictive nature lies in human drivers. In part of a response to the question how does Google Maps calculate your ETA, a former Google engineer had this to say:
Calculating ETAs is a future-prediction problem, and traffic, while it follows certain patterns, is inherently unpredictable. Even if you had complete knowledge of current traffic conditions and known changes (eg roadworks starting or a football match finishing), there’s nothing that can predict a crash or a slow truck changing route.
In an autonomous world, a passenger may very well enter a destination location and time into an app. I anticipate that the app will have a setting about the tolerance for being late. This app will then tell the passenger when to get into the car based on the travel time and lateness tolerances.
This is likely to effect, and affect, the formation of platoons. If a number of passengers need to arrive simultaneously, they will likely arrive in a platoon together. This will mean that a swarm of cars will show up for a sports event having platooned together. Additionally, if other cars happen to be travelling near the platoon(s) they will be able to join it for as long as is convenient.
There will be a competing concern: fuel efficiency. If a driver does not need to be at a location at a certain time, or even if that time is far in the future or has some leeway, the car will take fuel efficiency into account. In a previous post I touched on this with regards to traffic lights in an earlier post.
Currently, without regenerative breaking, stopping is the biggest impediment to high gas mileage. If vehicles can avoid stopping, then traveling at an ideal speed will bring about the best gas mileage. I fully expect that, like air travel currently does toady , a passenger may travel at a “cruise speed” unless there is an unexpected delay. In that case, they will burn more fuel and travel in the fast lane to get to the destination. This means that it may be that a slower route through the country may end up being more fuel efficient.
This combination could have some pretty drastic effects on the perception of travel. First of all, when a person enters in the destination and the preferred arrival time, there may be several different departure times listed. Each departure time will have a different cost associated with it.
Car, Automated 