Runs a simulation of people traveling in a transport network specified as GTFS feed. Computes "total time spent in transit" for a provided travel profile. Effectively evaluates "performance" of the transportation network and allows to compare 2 networks in a fair way.
Tool is composed of 2 parts:
transform.py- Python script that transforms GTFS feed data into low-level schedule data.sim- Routing simulator written in C++ that uses schedule data and travel profile to calculate resulting performance metric.
This work enables evaluation of the current transit and checking whether "proposed" transit is better or worse. Whenever creating a proposal it's important to control for the infrastructural "cost" (eg. number of physical buses, route feasibility, etc).
It's an open question how to make transit better, but this tool allows to check this hypothesis. One obvious way would be to run genetic algorithm to randomly modify feed and evaluate against the same profile. Reinforcement Learning approach can be considered as well.
File that contains a sample(or aggregate) of trips that people are making throughout the day.
Format is a simple CSV that follows this schema:
| Column | Type | Description |
|---|---|---|
| From | String | Start stop |
| To | String | Final stop |
| When | Timestamp (UNIX) | Start time |
TODO: Consider adding a field for number of people that travelled. Allows for more compact representation of aggregate information.
Unfortunately, there's no publicly available people travel profile. There are potential privacy issues associated with releasing even de-anonymized aggregate information. Differential-privacy is very hard to achive for this use-case.
Having said that, many public transportation providers require people to use company-issued cards (eg. ORCA, Clipper cards). It's fairly easy to design a way to collect an aggregate data internaly based on the information tracked by these cards.
There's a huge body of psychological research done explaining population travel patterns based on social behavior. As an example we've generated 10K random trips between uniformly sampled stops from the GTFS feed for Linz, Austria public transportation.
See profile.csv in the current repo:
$ head profile.csv
at:44:41339:0,at:44:41307:0,1669863600
at:44:41357:0,at:44:41150:0,1669935600
at:44:41474:0,at:44:41143:0,1669878000
at:44:40144:0,at:44:41156:0,1669863600
at:44:41470:0,at:44:41003:0,1669903200
at:44:41073:0,at:44:42598:0,1669863600
at:44:41162:0,at:44:41185:0,1669888800
at:44:41852:0,at:44:40252:0,1669870800
at:44:41234:0,at:44:41110:0,1669852800
at:44:41164:0,at:44:41056:0,1669878000
Transformation tool consumes a valid GTFS feed and produced a schedule. Which is a low-level joined representation of all the edges in the dynamic routing graph.
Here are all the flags supported by the transformation tool:
usage: transform.py [-h] [--gtfs GTFS] [-o OUTPUT]
options:
-h, --help show this help message and exit
--gtfs GTFS path to the folder containing GTFS feed data
-o OUTPUT, --output OUTPUT
where to put output schedule
Output is a CSV file that follows a simple schema:
| Column | Type | Description |
|---|---|---|
| From | String | Start stop |
| To | String | Final stop |
| Departure | Timestamp (UNIX) | Departure time |
| Arrival | Timestamp (UNIX) | Arrival time |
As an example we'll use a GTFS feed for public transportation in
Linz, Austria. It is checked in as part of this repo (see linz).
$ ./transform.py --gtfs=linz -o sched.csv
$ head sched.csv
at:44:40002:0,at:44:41126:0,1669868160,1669868220
at:44:40002:0,at:44:41126:0,1669869900,1669869960
at:44:40002:0,at:44:41126:0,1669870020,1669870080
at:44:40002:0,at:44:41126:0,1669870260,1669870320
at:44:40002:0,at:44:41126:0,1669870860,1669870920
at:44:40002:0,at:44:41126:0,1669872180,1669872240
at:44:40002:0,at:44:41126:0,1669873020,1669873080
at:44:40002:0,at:44:41126:0,1669873620,1669873680
at:44:40002:0,at:44:41126:0,1669873680,1669873740
at:44:40002:0,at:44:41126:0,1669874400,1669874520
We've implemented a simple routing engine based on the Dijkstra's algorithm that works on the dynamic(time-based) routing graph.
Building simulator is as simple as running cd sim && make.
Here's how to run a simulation against a given schedule and travel profile.
$ sim/run sched.csv profile.csv
3y 7m 1w 2d 20h 31m 00s
We can use this to see how much worse the transit will become if we randomly remove half of the edges in this graph. Keep in mind that this is not a very realistic scenario as it doesn't account for the fact that canceling a connection usually means that subsequent legs have to be canceled as well.
$ shuf sched.csv | head -n 40000 > half_sched.csv
$ sim/run half_sched.csv profile.csv
6y 11m 3w 3d 18h 35m 30s
As we can see this lead to doubling total time in transit for people.