andalinmicphew

Nice! I’m always exciting to try a new non-car centric mapping/routing app.

Modemix is solid. Same goal as bikehopper and lots of active dev. We (bikehopper devs) know the modemix dev. Good guy, solid app. I think the Modemix hill data isn’t as detailed as what we use for SF.

The architecture is a static asset server (nginx), basic backend app server (node js), routing engine (Java) which is a fork of graphhopper, fast geocoder named photon (elasticsearch), full OSM places search called Nominatim that feeds photon (postgres) as well as a few asynchronous jobs that pull the latest OSM and transit data then rebuild the search indexes.

This was on a k8s cluster for a bunch of reasons. We eventually scrapped it and put the stack in one box instead of five.

By cut it down I mean, use fewer machines to host it.

Hillmapper falling apart was one of the main reasons we prioritized getting good elevation data into our router. We include steepness indicators for steep blocks and an elevation overview of the full trip and have found that combo to serve us well.

Getting between those two Safeways is rough. Bikehopper fails, it suggests the harry street steps; a non-starter for most. I’ll try to fix it. I definitely agree that grade is non-linear. We use accelerating weighting for grade so a hill that is twice as steep is more than twice as “discouraged” by the routing engine.

For bike routing I think e-bikes need a separate profile from acoustic bikes. Hill matter less and stairs are totally out of the question (this is important for routing in and out of transit centers).

DSM includes buildings and trees. So the DSM from SRTM data makes market street in S.F. look like a series of very steep hills and valleys because some of the datapoints are street level while others are the roofs of skyscrapers. DTM removes the buildings resulting in a map much closer to what we think of from the ground.

We run it on a single coolify box from someone’s house. 6 core, 32GB ddr4 memory box. The actual routing query are compute intensive but only momentarily. More cores and memory means higher concurrency but we’ve never had an issue with load in the Bay Area with a few hundred users a week. We used to run it on a k8s cluster but we cut it down to save on power and infrastructure management overhead.

We’d like too! It’s an open source project that was never designed to scale beyond one regions (bay area, puget sound, NYC, etc…) our hope has always been been that folks in different regions would spin up their own copy. By keeping it regional the data and hardware requirements for running it are minimal. That said, we are working on a new routing engine that we hope can scale to support continent wide routing. Then we can manage running it for many metro’s. If you’re interested in self hosting for Seattle we are happy to help get it set up.

Checkout https://bikehopper.org for solid elevation, bike infrastructure and transit-aware routing around the Bay Area. Some friends and I built this about 5 years ago and continue to maintain it. We use 1m DTM elevation data for SF and 50m for rural areas. DTM is a must for elevation based routing in SF as there are so many sizable buildings, large trees the other models fail hard.

Mobile A/B testing without an engineer!

nice! a/b testing without needing to redeploy.