It's an interesting problem because there are so many factors and even the goal isn't constant from day to day.
Goals
- get from A to B as quickly as possible
- get from A to B as safely as possible
- get from A to B as enjoyably as possible
Factors
- fitness of rider
- type of bike (Riding a fixed gear bike up a steep hill is bad for enjoyment, but riding a mountain bike up a steep hill is not)
- weather (headwinds, tailwinds affect time, affects safety of certain routes as well - don't tell me to ride down a steep windy hill when there is a possibility of ice)
- time of day (affects safety due to light levels)
- traffic volume
- number of lights/stop signs (this affects fast riders more than slow ones because of the effort wasted accelerating and braking is greater)
- number of turns (turns make the rider slow down and wait for traffic and also increase the complexity of following a route)
- scenery/environmental factors (eg. ride through an industrial area or through a waterfront park)
And it's also not said that all these factors have a linear impact on the Figure of Merit. I guess that this might be a nice application for a machine learning classifier, to rate a route's ride quality depending on all of these inputs (and more.)
Goals
- get from A to B as quickly as possible
- get from A to B as safely as possible
- get from A to B as enjoyably as possible
Factors
- fitness of rider
- type of bike (Riding a fixed gear bike up a steep hill is bad for enjoyment, but riding a mountain bike up a steep hill is not)
- weather (headwinds, tailwinds affect time, affects safety of certain routes as well - don't tell me to ride down a steep windy hill when there is a possibility of ice)
- time of day (affects safety due to light levels)
- traffic volume
- number of lights/stop signs (this affects fast riders more than slow ones because of the effort wasted accelerating and braking is greater)
- number of turns (turns make the rider slow down and wait for traffic and also increase the complexity of following a route)
- scenery/environmental factors (eg. ride through an industrial area or through a waterfront park)