JuMP.jl

It's not mentioned in the article, but Julia was used to solve the huge optimization problem for this project—specifically, the JuMP optimization package [1]. This SIAM article has more technical detail: https://sinews.siam.org/Details-Page/a-school-bus-trip-to-th.... This seems to be a preprint of the paper that may describe the algorithm used in Boston, although it seems to talk more about the well-known New York taxi data set: http://web.mit.edu/~jaillet/www/general/travel-time-18.pdf. [1] https://github.com/JuliaOpt/JuMP.jl [2] http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtm...

I think sometimes people are distracted by Julia's performance, according to https://github.com/JuliaLang/Microbenchmarks Julia is not the fastest language/compiler (maybe LuaJIT is). Julia is not only good for its performance, but also its multiple dispatch, its type system and more. Because of those features we have https://github.com/JuliaGPU/CUDAnative.jl, more elegant package interface, like https://github.com/JuliaOpt/JuMP.jl and there is more. I still believe there is no silver bullet, if you want something Python then you get another Python. But think about this: is the full Python's dynamic feature really what we need in works like simulation, HPC, etc.? Probably no. I think Julia is kinda of balance for the related field (like for scientific computing). Just like in the old days, people start using FORTRAN, MATLAB, Lisp as the m