[honorious]

That looks like an interesting problem!

1) What are the metrics that mass transit operators tend to optimize for? Do you think they are in line with improving the service to customers?

2) How much is "dependability" of a mass transit service a metric that operators are interested in?

Intuitively I feel that there are places (e.g. Chicago) where I know that I can get out of the house, get the bus/train, and get to my destination with little variance in arrival time.

Others (like SF), where the variance on the arrival time seems much higher: e.g., generally related to unexpected delays while waiting for train/bus.

Do you think it's possible to optimize on such factors?

[kevinqiu1]

If you're interested in the design/factors in mass transit, I highly recommend Alon Levy's Pedestrian Observations blog.

For example, this is a post about bus branching: https://pedestrianobservations.com/2018/07/14/bus-branching/

[CaliforniaKarl]

I think you should take a look at the documentation behind VTA's "Next Network": https://nextnetwork.vta.org/document-library

It is planning that was done to prepare for when BART service reaches Santa Clara county, and how the bus network will need to be reorganized. In the documents, you can see how they looked at different plans, covering different levels of geography vs. population.

[ebikelaw]

But why would you look at VTA when it's one of the worst-performing systems in the nation? Their ridership is in freefall.

[honorious]

Thanks! The design tradeoff between ridership and coverage is very interesting, and ripe for political discussion.

I wonder if there are opportunities for working with on-demand ride companies for coverage, while focusing infrastructure on high-ridership corridors. Fairness of access for disadvantaged people would be a problem to solve.

[indiesolver]

You may formulate it as a multi-objective problem where you minimize the estimated time of the trip and your uncertainty of this estimate. Often, the two objectives are conflicting. As a user, the software should show you a Pareto set of optimal solutions where each solution has its duration and uncertainty. Then it is up to you which solution to pick. I hope they have this feature at optibus.

[ishaym]

Right you are :) See my reply later on this thread (ishay from optibus)

[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