[ishaym]

Hi, I am actually an algorithm dev at Optibus, so I can provide a few insights. 1) Naturally, mass transit operators optimize for highest payoff - so they would try to use the least number of buses, least number of drivers' work hours, least deadheads etc. However, the government rightfully enforces both driver work regulations (enough rest, coffee breaks etc) and highly penalize them for service unreliability - each city is different, but in general - if a bus is late for more than X minutes\doesn't show up, the operator will get a heavy fine. Also - if consumer satisfaction is low, this operator will not get to operate the city's buses again, so they try to keep them reasonably high. 2) for these reasons, service reliability (or dependability) is animportant factor. in general - schedulers should have a few reserve vehicles and drivers, so that in case of a delayed bus - they can replace it with their reserve. Here, at Optibus, we have started a pilot where after looking at historical data, we use AI to predict which trips are likely to be delayed. and by how much. With our predictions we first warn the operator that the estimate for this trip is questionable, and more importantly, build the schedule accordingly - give more time spread between these trips and the next scheduleued trip on this vehicle, so that this delay will not affect subsequeent trips and cause a cascade of delays. This way - we make the schedule more robust. so far it looks promising