The challenge for practical implementations is that there are lots and lots and lots of optimization factors that are very hard to account for. Often, you don't even know what they are until you try to automate the process.
For example: preferring small buses on side streets, as the article mentions. Or, knowing about a major work project in an area (to avoid for the coming year), or specific left turns that are tough for a bus. Also, highly optimized schedules usually have (by definition) much less "slack" in the overall system, and are less resilient to real world changes and variations.
My point: in my experience, there can often be a significant gap between enthusiastic technologists (myself included) and real-world implementations.
Anyhow, to add to your point, you can model all you want - but some drunk is going to reverse the wrong way down a one way street.
One of these days, maybe I'll get around to putting my experiences into giant walls of text.
It was a pretty wild ride. I have a Ph.D. in Applied Mathematics and actually did my research with vehicular traffic because the date was free. I actually expected to remain in academia, but was given the chance to contract with a government agency, even before I'd done my defense.
Basically, I helped take the art of traffic modeling to the computer age. I say helped because I stood on the shoulders of giants.
I'd eventually be able to hire competent programmers and a real IT staff. Eventually we'd model pedestrian traffic, in addition to vehicular traffic. We would also sell solutions that companies could implement on their own.
The recession hit. The US gov announced they were going to invest billions in highway infrastructure. This made the company quite valuable and I was given an exceptionally good offer. So, I sold it and don't regret that decision at all. I'm happily retired and find lots of things to keep me amused.
There is a whole lot more to the story but that's the gist of it. I'm the same KGIII you may have seen on Slashdot.
What were some of the toughest moments you experienced?
And what advice would you give to people who want to build their own companies in transportation/logistics fields? (I know - may not be exactly your area but there may be some cross-sector knowledge sharing at play here!).
My toughest moments were people related. I never took any fancy business course that taught me how to manage people. I tried to micromanage and had a hard time letting go.
Eventually, we'd have three offices along the East Coast and a satellite office in the middle of the country. We had about 235 employees when i sold. What I learned was to stop trusting vendors.
I learned that I'd hired these people to do things that I could not do. If I could have dne them, I'd have not needed to hire them. Give them the tools they ask for, the room to do what they need, and clear goals.
They were different times. It was a Wild West sort of environment. It was hard enough getting engineers and programmers to wear shoes and a lot like herding cats. But, give them freedom and respect. Remember, you hired them because they were the very best you could find and they can do things you can not.
Advice? Be in the right place, at the right time, with specific skills, and in a position to take risks. Had I been unable to complete my first contract, the penalties were great enough that I'd still be paying them off - slight exaggeration.
It's pithy to say study hard and work hard, but that helps. I worked my ass off, often putting in 16 hour days. I'd continue to do this, even after being the sole custodial parent for my two kids. My ex wife is a bit of a mess. I missed a lot of their youth and they were often home with a girlfriend or a nanny.
But, find something that is missing and do it. Find something that is hard, and do it better. Right now, things look like a more intensive struggle for efficiency. How can you make transportation/logistics more efficient? How can you make it better? What services are lacking?
In my case, there weren't even many traffic engineers back then. The field, as applied by computers, was very young. The existent algorithms didn't scale or translate well to computers. I'm a horrible programmer, but I am a mathematician. I fixed that.
Similar processes applied to fleet management and to pedestrian traffic. Similar processes relate to outflow of people in buildings such as skyscrapers, shopping malls, and arenas. Even certain outdoor events need evacuation plans, for which modeling pedestrian behavior is important. Even your grocery store has probably modeled how you will move through it.
We had more work than we could do. I couldn't hire enough people and we just kept growing. I liked field work, so I tried to get as much time out in the field as I could. It also meant that I was on-site, a lot. I kinda hate dealing with government workers. They mean well, but they are tied to a dysfunctional environment.
Anyhow, soft skills matter. Be nice, polite, and an active listener. Listen for what they need, not what they are telling you they want. Be ready to try to provide both.
Sorry for the verbosity and disjointed post. I don't have words of wisdom, only my own experiences. I hate to admit it, but going to a good school probably helped a great deal initially. I went to MIT and just that was enough to open the doors. DEC literally loaned me equipment, both during grad work and to start the business. MDOT gave me loads of help and even followed my research from a fairly early point.
I'm open to continued responses, but I don't want to derail the thread too much.
You are definitely right about being at the right place at the right time, and making sure you have the skills to do it better than others.
I have a pure software engineering degree (Masters) and been teaching myself deep learning lately. I am currently undertaking the Udacity self-driving car engineer nanodegree as as it is a great way to pick up skills in computer vision, machine learning and the automotive industry space. I am confident those skills will be valuable if I find the right opportunity.
> Be in the right place, at the right time, with specific skills, and in a position to take risks.
So where would you start looking? Or do you think the 90s was a different environment where you could solve a problem on your own. Now to improve efficiency, do you need the support/funding of an established company?
In my case, the data was existent. I'd later collect data. Data is not just throughout, but even contains data from things like a reflectometer, sign frequency, and adjustments for specific locations.
The data collected is, for this sort of thing, paid for by the municipality and can be examined at your local municipality, where applicable. The specific department, and location, will vary but our reports were public information. Your department of transportation has copies, where applicable.
I didn't do much in the way of design. I modeled. This is what will happen if you do this, this is what will happen if you do that, and if you want these results you should consider doing these things. They don't always listen.
We also did modeling for things like disasters. A recent example would be the bridge collapse in Atlanta, GA. Most of the public thought it was going to be a nightmare. I actually had people argue with me and tell me I didn't know what I was talking about. No, they already have plans on file to deal with the expected congestion from just such an occurrence.
Of course, those plans are usually a bit out of date... In their defense, it's not an inexpensive process. Given the ubiquity of compute cycles, many municipalities are taking some of this in-house.
Your local planning board should be a good start, if you want to learn about your specific area.
Note: I have been out of the industry for about a decade. I am contractually barred from entering the industry again, in any capacity. I do try to keep up with the tech, but that's purely out of curiosity. It is no longer a scholarly or professional pursuit.
Also, any reports we filed are almost certainly still around. They are probably buried in a closet, however. I am not actually sure how municipalities function as well as they do. They are, almost without fail, very disorganized.
Someday, I'll tell you about The Font People. Yes, we even quantify fonts as an impact factor. However, Font People are some of the most unusual characters I've ever met - all of them.
I do miss the data collection and research. But, I haven't worn pants all day. So, it's an acceptable trade off.
But, if you have a question - I may know. I have done quite a bit of reading, but no academic research and no publications with regards to rail transport.
Perhaps one day while incredibly bored and procrastinating on the decision of pants or no pants, you could look it over, die laughing and send me an email. My email address is in my profile.
Thank you for replying.
Routing algorithms can come up with extremely efficient routes, which can serve as a starting point for the human route planner to fix (to account for real-world knowledge).
Consider the alternative where the human route planner starts from scratch.
Naively looking at a map, you'd think a roundabout route had potential to be shorter, but not realize the road was gravel.
Surface data in OpenStreetMap is pretty incomplete, but it is easy to add to a road and is well used when present (cycle routing is a popular use of OSM and avoiding soft and uneven surfaces is handy for cycle routing).
If you save a business organization money, they appreciate it.
If you save a government organization money, the next year the "savings" is likely to be deducted from their budget. They don't get to benefit from the savings and thus their motivations aren't what you might expect if you are used to dealing with businesses.
It makes sense but when I encountered this the first time it caught me by surprise. I mean who wouldn't want to save money, right?
So if i spent $500 eating lunch at work two years ago; $600 last year and plan/allocated $610 this year i had a "budget cut?" no. I spent more.
Thats how i think
But this is more about having an expectation of funding at a certain level from whatever source, and knowing that that will be your "budget" (now meaning a spending _limit_), then having that amount come down. You now have a smaller limit.
> In hopes of spending less this year, the school system offered $15,000 in prize money in a contest that challenged competitors to reduce the number of buses.
Is that based on past experience?
Actually most non-VC startup companies are heavily focused on opportunities to reduce their spending so long as it does not effect their ability to grow revenue
As they say, a marginal dollar saved "drops straight to the bottom line" (profits).
They work for the people, after all.
There is also this practice in organizations where money is allocated in "pots". For example, there is probably a pot for hardware/computers and a different pot for software. If you save them money on hardware, they don't automatically get to spend the savings on software.
(I thought you were unfairly down-voted so I up-voted you.)
 The correct term escapes me at the moment.
They should incentivize efficiency. In government, it would be citizen impact per employee. If you have more, you make more money, if you have less, you make less.
Do you think it would actually possible to quantify this in a way that:
1. Accurately represented "reality"?
2. Couldn't/wouldn't be gamed?
Seems challenging to me.
1) How many people are affected by the work of this agency
2) How quickly are peoples' needs handled on average
3) How pleased are people with the work of this agency
I think you could trivially measure each of those.
Choosing metrics to represent practical value, rather than let them be decided by profit-seekers, that's the real trick.
But if you make it annual standard operating procedure, then you are paying people to create spreadsheets and documents rather than, say, do something useful.
And the money will funnel to the people who are awesome at creating spreadsheets and documents rather than the people who are good at doing useful stuff.
Effective oversight requires understanding the domain and being motivated to continually improve things.
As the article notes, they've tried this before and the algorithmically generated route map didn't work well in real life. Assuming the algorithm was good, the issue was probably that it didn't incorporate significant real-life factors.
Perhaps they are getting more sophisticated though, by having the people who have been doing this work by hand review the results and make adjustments (as the article says, e.g., at first the algorithm allowed big busses on narrow streets, which apparently doesn't work well).
Speaking of the people who have been doing the route maps manually... they seem to have been doing an impressive job since the algorithm is expected to be only 4% better.
I say all of this more to make the argument that rather than retrofit old buses, in my opinion it makes more sense to outright replace them. Right now Proterra is the largest domestic producer of pure electric buses. They compete on most contracts with BYD in China, and New Flyer in Canada. Behind BYD and the government, the electric bus market in China has absolutely exploded over the last several years. Proterra is anticipating high growth in the U.S. market as well and just launched a new factory in LA last month .
While aerodynamic teardrops are most efficient that means nothing if the up front cost is prohibitive.
Also, efficiency and a box that's strong enough to appease the "think of the children" types are very much at odds with each other.
For a bus with low mile requirements some boxes to hold batteries and a big DC motor where the engine goes would work fine.
The "scaled up golf cart" architecture works well if the route does not change and is very cheap to implement.
On your second point, a bus doesn't necessarily have to have a heavy steel frame for it to be safe. There are plenty of lighter weight (e.g. composite/carbon fiber) solutions which don't sacrifice safety.
Sure, if the mileage requirements are low then there's definitely a cost-effective retrofit solution. But if you're a company already building a full electric bus and investing a lot of R&D into the pack design anyway, then you could probably just sell battery solutions to school bus companies as another revenue stream. I'd wager, though, that the larger addressable market is customers looking for new electric buses for longer routes.
Buses are designed for many years of service with long-lived and easy to replace parts. On the safety front they are painted yellow and have some lights. That's pretty much it. They don't even have seat belts.
It looks to me like efficiency trumps safety in this case.
"Pursuant to these requirements, federal safety regulations applicable to bus manufacturers have been developed. There are a total of 37 federal motor vehicle safety standards that apply to school buses. Many of these also apply to other types of motor vehicles, but several (among them standards 131, 220, 221, and 222) are written specifically for school buses."
As for seatbelts,
"Large school buses are heavier and distribute crash forces differently than passenger cars and light trucks do. Because of these differences, bus passengers experience much less crash force than those in passenger cars, light trucks and vans."
It seems that even weight is a safety consideration.
In addition, the lifetime of a bus isn't measured in the same way cars are. They are purchased once a decade, never retrofitted, and not every bus is replaced with that purchase. With that in mind it will probably be another 2 sales cycles before we see any.
I'm not saying it can't be done, just that it is harder than it looks.
An extra few thousand pounds would not be a problem for a full sized school bus.
School buses also have secondary uses as evacuation vehicles so relying on electric isn’t a secure option. Hurricane Katrina, for example, used hundreds of buses for evacuations and it would have been extremely difficult to charge a bunch of buses off generators.
Also, school districts are in the business of education, not transportation, so they may not see themselves as the people who should be leading in this area. If someone else (transit agency, airport shuttle company, etc.) tries something and proves that it works long term, they may then feel comfortable following along, but as far as I know, that hasn't happened yet because by definition it takes a long time to prove that.
A typical service day for a school bus can be pretty packed.
What do you think what kinds of algorithm makes schedules for trashbin collection, newspaper delivery, sodapop machines etc.
If you aren't ondemand and there's multiple drivers there is someone who can optimize it to (almost) optimality.
This is a pickup & delivery problem and the algorithms exist and work in practice for decades.
I work within the trash collection industry and we've tried several different optimization schemes that in general don't come close to optimal efficiency. The marketing for a lot of the optimization companies would lead you to believe that they're able to unlock a lot more efficiency than they actually deliver. Our clients that have applied algorithms to optimize their routes quickly reverted to human optimization since the algorithms fail to provide any benefit and typically require tremendous effort to get started.
I would love to discover an algorithm that is able to consistently provide actually optimized collection routes without hand holding!
At that rate, they could save many more millions by switching to Uber or some other ride sharing service.
I feel like it would be an awesome project to dissect a large public institutions funding and to discuss ways that algorithms can help reduce costs without reducing the quality of service and agility of the organization.
The site would not allow me to read the full article, so I might have missed something.
There is only so much efficiency you can squeeze out with an algorithm. But, think scale. This is only in Boston. What if this team offered up the algorithm to all schools and other public transit authorities in the country? Think how many miles driven (less miles = less wear and tear, less opportunity for accidents to occur) and dollars would be saved.
Want to do it as a for profit taking a cut of the savings? Cool. Want to do it as a non-profit funded by the DOE out of a grant? That'd work to. It appears there are hundreds of millions of dollars in savings to be realized out there from this, which is awesome.
We have found with many real-world scenarios, that at a much smaller scale we could save easily up to 40% in driving time and fuel costs. When we studied cases of 20+ vehicles and ~1000 stops, sometimes the savings were up to 60%.
In one scenario we took 8 cars off the road form a fleet of 30.  That's 26% compared to the article's 11.5%. Not to discount its results, dropping 75 bus routes is incredible! Imagine dropping another 75 :)
Note that since this is an NP-complete problem, the larger the size of the problem, the more constraints you add, the harder it is for any human route planner to plan routes efficiently -- so the larger the potential efficiency gains for an algorithm.
Disclaimer/plug: founder of Routific here.
Points below properly define problem above:
* capacitated pickup and delivery with time windows (NP-hard)
* capacity of around 20-60
* every child has to stay maximum T minutes in the bus from the moment it is picked up
* each bus can work simultaneously on delivering to multiple schools (covered by standard p&d algorithms)
* additional routing/map constraints for roads/vehicle constraints - like minibus drivers being constrained to ares with different housing etc.
I'm pretty sure the third point is a tricky one, implementation and speed wise, last one too. I'm skeptical that your API could solve the problem for 600 vehicles off-the-shelf. Not to mention that pickup&delivery slows things down extremely compared to capacitated VRP.
Each optimising step is a venture into heavy graph theory. Or one can use easy heuristics and fail miserably by exploring too little of constrained space.
I'm skeptical of these large savings. I've personally worked with some top logistics people that did amazing things with pencils and rulers. Trumping these methods gained savings of about 10-15%.
Not to mention the optimizing time. It is impossible (if algorithm is not heavily optimized) to search through enough space for these heavily constrained problems in short amount of time (couple of minutes) to get 60% savings. I might be too critical, but I doubt that Common Lisp algorithm can achieve those kinds of speeds.
What if a child was forgotten, how much will the replanning time impact the real world workflow. All sorts of invisible issues.
Depends. If the same routes have been in use for a long time, odds are they are already really optimized. Bus drivers will take the same route year after year, and make suggestions to optimize it (often times by "going off route" now and then, which is very much against the rules, but at times very much needed), and those suggestions will get rolled in next year.
5 or 10 years of incremental improvements add up.