This is a project implementing a ‘scraper’ to grab and publish TTC subway arrival times against citizen-led performance metrics analyzing service timeliness and reliability. The end goal is to maintain and publish this performance dashboard building consensus on service quality betterment over public transit in Toronto.
We have a Python scraper running on AWS saving predicted subway arrivals since late February 2017. We need to process this data to generate observed station arrival times for each train at each station. There was a couple month hiatus in data scraping because the database was full between August and November, but the scraper is now continuing to hum along nicely.
We're still trying to process the predicted arrival times obtained from the API into reasonably reliable state. This work is happening in a Jupyter Notebook Filtering Observed Arrivals.ipynb; it is mostly in SQL, despite being in a Python notebook.
Take a look at How to Get Involved with your expertise. Feel free to follow along; your informed feedback will surely lead to better data.
Buffer Time - A measure of reliability useful for the rider:
- how much extra time he/she needs to buffer in order for their trip to be on time 1 trip out of 20.
Review the doc/ folder for the Jupyter Notebooks explaining how we explored developing this project and understanding the data. Jupyter notebooks are a way of having code, descriptive text, and output together in one document to present a narrative around data exploration.
If you're interested in a Data Dictionary, click here (it's also in the doc/ folder).
You can watch Raphael give a presentation to CivicTech Toronto January 15, 2019 on this blurry Youtube and the slides are here
The scraper runs every minute during TTC service hours. Each of these runs is termed a polls
and has a start / end time. During one run of the scraper, each station gets its predicted arrivals returned as a request, noting which station it is using stationid and lineid.
For each request, 3 predicted arrivals are recorded for each line and direction at that station. These responses include the train's traindirection and its unique ID trainid, the time until the train's arrival timint and a train_message, whether the train is arriving, at station, or is delayed.
For more info: review the API exploration notebook under doc/API_exploration.ipynb
#transportation channel on Slack. If you think you can help with this, check out issue #59
Historical data is stored in the s3://ttc.scrape bucket, the fetch_s3.py script can be used to automatically fetch and assemble this data. Its usage is
python3 fetch_s3.py --bucket ttc.scrape --start_date 2018-12-02 --end_date 2018-12-05 --output_dir out/If end date is omitted it is taken to be the latest available date. This script can only select by day.
This will generate three CSVs in the output_dir which can be copied into the Postgres database with the following SQL commands
COPY polls FROM '/path/to/polls.csv' DELIMITER ',' CSV HEADER;
COPY requests FROM '/path/to/requests.csv' DELIMITER ',' CSV HEADER;
COPY ntas_data FROM '/path/to/responses.csv' DELIMITER ',' CSV HEADER;There are two ways that the scraper can be automated, via Docker/cron, or via Serverless with AWS Lambda. No matter how you choose to run it the scraper runs through ttc_scraper_api.py
There are two ways the data can be stored once it has been scraped, AWS S3 and Postgres.
AWS S3 stores each scrape in a JSON collected by service day (see Consolidate function). This requires an AWS account. This can be enabled by with the --s3 flag. The advantage of S3 is that it requires no persistant server, and is extremely cheap. Its main disadvantage is that the data is not as easily queryable as an SQL database and some steps are required before it can be queried in SQL (see Analyzing the Data). This storage method is well suited to the AWS Lambda scraping mode.
Postgres requires a running Postgres instance. While the data is immediately queryable, it requires a Postgres server to be always running, which increases the work, risk and cost of the project. This can be selected with the --postgres flag and is most often used with the Docker/Cron scraping mode.
Both modes store the same data, in largely the same structure (nested JSONs vs tables with joinable keys).
There is a mode which will allow scraping via AWS Lambda with logging added to AWS Cloudwatch. This mode uses the Serverless framework.
The Serverless framework is a suite of tooling which allows the easy deployment and management of serverless code.
This allows us to run this code without having to spin up/monitor for an instance manually. And since we only pay for the code when it is running the compute costs are nearly zero.
In addition to installing the Python requirements we need to install the Serverless framework with npm (install instructions for Ubuntu by running npm install in the project root.
On Ubuntu you'll need to add the serverless binary to your PATH variable with PATH="$PATH:/[PATH/TO]/ttc_subway_times/node_modules/serverless/bin/" (replace [PATH/TO] with the absolute path to the repository). This will only be temporary for your session, if you're going to use serverless for other projects, you should probably install it globally with sudo npm install -g.
Tell Serverless which AWS creds you would like to use with:
serverless config credentials --provider aws --key <AWS_ACCESS_KEY> --secret <AWS_SECRET_KEY>
Creating these credentials must be done through your AWS account. A good guide to this process can be found on the Serverless Website
The template serverless.yml is configured to use dev and prod environments. This will create functions with dev- or prod- prepended to them, and will also direct output to the buckets that are defined in the custom section
Move serverless.yml.template to serverless.yml.
Create an S3 bucket. Replace the angle bracketed bucket names (under the custom: properties at the end of the document with the names of the buckets you created.
At the time of writing the schedule line in serverless.yml is set as
rate: cron(* 0-2,5-23 * * ? *)which means that it should run every minute from 5am to 2am every day. More information on this cron line can be found on the AWS documentation, in this documentation references to UTC should be ignored, we use the 'serverless-local-schedule' package which allows us to specify crons in local time rather than UTC (otherwise the behaviour would change during daylight savings time).
You'll need to install docker for this step (Ubuntu install instructions).
Finally deploy the function with
serverless deploy -vThis command will deploy to the dev environment by default, the environment can be specified on the command line with the --stage flag (acceptable values for this project are dev and prod)
Logs are automatically persisted to Cloudwatch.
If you are scraping with the AWS Lambda function with the S3 data destination it will write a JSON to s3://<BUCKET>/<SERVICE DATE>. As the scraper runs once per minute, this results in a very large number of small files, which are inefficient and expensive to store and transfer.
To remedy this there is a second 'consolidate' serverless function which runs at the end of every service day and combines the previous day into a .tar.gz file, storing it at s3://<BUCKET>/<SERVICE_DATE>.tar.gz.
This isn't relevant if you are storing the data in Postgres with the Lambda scraper, but this configuration will require you to modify serverless.yml.
Another way to run the scraper is to run it periodically a Cron in a Docker container. This is useful if you would like to scrape from a computer that is always left on.
Follow the below command to set up a Python 3 environment and install requirements.
Running inside docker
Follow the instructions [here](https://docs.docker.com/compose/install/) to get `docker-compose`.All you need to do is docker-compose run --rm scraper.
This will setup a database container, initialize the tables, and then run the initial scrape.
To have cli access to the data you can use docker-compose exec db psql -U postgres -d ttc.
Commands of interest:
\?: list the help information for all the special commands\h: list all the sql commands that are available\q: quit the console
To use Mac or Linux, add the following to cron. Don't forget to change /path/to/ttc_api_scraper.py.
Note: These commands assume postgres as a data destination.
# m h dom mon dow command
* 5-23 * * 1-5 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --postgres
* 0-1 * * 1-5 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --postgres
* 5-23 * * 6-7 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --postgres
* 0-2 * * 6-7 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --postgresOr to run every 20s while filtering out any "arriving" records
* 5-23 * * 1-5 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres
* 0-1 * * 1-5 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres
* 5-23 * * 6-7 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres
* 0-2 * * 6-7 cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres
* 5-23 * * 1-5 (sleep 20; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)
* 0-1 * * 1-5 (sleep 20; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)
* 5-23 * * 6-7 (sleep 20; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)
* 0-2 * * 6-7 (sleep 20; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)
* 5-23 * * 1-5 (sleep 40; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)
* 0-1 * * 1-5 (sleep 40; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)
* 5-23 * * 6-7 (sleep 40; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered)
* 0-2 * * 6-7 (sleep 40; cd /path/to/repo/ttc_subway_times/ && python3 ttc_api_scraper.py --filter --schemaname filtered --postgres)Use Task Scheduler.
If the above sounds complicated, here's a simple looping script that calls ttc_api_scraper.py every minute during the TTC's operating hours. Just start it in your command line with
python cronic.pyAnd let it collect the data.
If you would like to use Postgres as a data repository, the database engine used to store the data is PostgreSQL. Instructions to obtain the latest and greatest version are here. After setting up your database, you can run the contents of create_tables.sql in a pgAdmin query window (or run it as a sql query).
You will also need to edit db.cfg
[DBSETTINGS]
database=ttc
host=host.ip.address
user=yourusername
password=pwWe discuss the project on CivicTechTO's Slack Team on the #transportation channel. This is probably the best place to introduce yourself and ask how you can participate. There are links in that channel to get access to ~1 month of the raw data in a csv or a PostgreSQL dump. You can also ask about getting read access to the database. Alternatively you can set up the scraper yourself and play with your own archive locally, hack away!
If you're exploring the data, please write up your exploration in a Jupyter Notebook/RMarkdown/Markdown file and place it in the doc/ folder and submit a Pull Request with details on what you explored.
Otherwise have a look at open issues and comment on any thing you think you could contribute to or open your own issue if you notice something to improve upon in the code.
Boldly following in others' footsteps See more on the Resources page
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