This is essentially a capstone class for students interested in transportation engineering. You get 3 homework assignments; they're all easy and take about 1 hour each. The lectures were semi-useless because the Professor just reads off the slides. Less than 8 students show up to in-person class because there is a Zoom option.

You start working on the group project from week 1. You choose one of public transit, congestion pricing, autonomous vehicles, and ride-hailing. All groups must use MATSim. If you have strong programming skills, you can finish all the simulations in ~12 hours. Otherwise, the MATSim simulations will take at least 20-30 hours to complete. During the presentations, it was obvious that the software was a living nightmare for half of the class. I recommend having 1 person work on the software while the rest of the group type the essay. Having 2 or more people work on the software may slow things down and take longer.

If you are confident in your computer science skills, then this class will be very easy. Otherwise, you should form your group to include someone who knows programming. Alternatively, you can do everything in Excel, but it will be painful, tedious, and nebulous. The group project is difficult, but it is rewarding for those interested in transportation careers.

I enjoyed this class a lot, and I think it will be a good addition to the transportation engineering curriculum at UCLA. Overall I would recommend this class to anyone interested in transportation engineering, and to planners interested in taking civil courses I would recommend this course over 181 (though I'm not sure how much 180/181 will change with Prof Ma teaching).

This was the first time Prof Ma has taught this class at UCLA, although I believe he taught it elsewhere before. Also teaching some sections was Dr Yueshuai He. Some sections of the class were pretty theoretical and some people complained about this, but mostly it was comprehensible. The textbook however is highly theoretical and basically has the mathematical basis for trip modelling. Sections of the class: discrete choice, how this is calibrated to make models, and a further in-depth look at the framework of models. There were three homeworks (covering those aforementioned sections), one final group project*, and a multi-day final (maybe not multiday once we're out of the rona). The group project is probably the worst part of this, as it's reading the documentation for an existing real-world travel demand model and basically summarizing it (for grad students only: a critical paper too). Maybe this was just because my group's paper was *extraordinarily* badly written, but this wasn't too instructive. Larger-scale modelling (i.e. on computers) wasn't really present in homeworks and we just dealt with toy models since that would take so much calculation time, but there is a lecture devoted to just showing what can be done, and they provide resources for learning on your own.