I understand it may not be recommended in complex situations, such as you described, but I think @steve_g's idea may be interesting for some scenarios.
I work in automatic train traffic planning, mainly for heavy-haul railways [1]. Recently, we've been working on a regression model to predict train sectional running times based on historical data.
As our tool is used during real time operation, we can't risk the model outputting an infeasible value. So we're thinking about defining possible speed intervals, e.g. (0km/h, 80km/h] for ore trains, and falling back to a default value if the predicted running time causes the speed to fall out of this range.
I work in automatic train traffic planning, mainly for heavy-haul railways [1]. Recently, we've been working on a regression model to predict train sectional running times based on historical data.
As our tool is used during real time operation, we can't risk the model outputting an infeasible value. So we're thinking about defining possible speed intervals, e.g. (0km/h, 80km/h] for ore trains, and falling back to a default value if the predicted running time causes the speed to fall out of this range.
[1] https://railmp.com/en/our-solution/