#Instructions

System Requirements

The server must have at least 2G of RAM. This means that at least a small instance is required on AWS.

To install and deploy on a local server.

1. npm install --production
2. ./bin/updateGTFSData.js
3. node siri_server.js

To deploy on AWS

1. Create a base Ubuntu 12.04/14.04 LTS Server
2. In the home directory, as the regular user, run wget https://raw.githubusercontent.com/availabs/MTA_Subway_SIRI_Server/master/aws/init-script.sh
3. sh ./init-script.sh
4. cd code/MTA_Subway_SIRI_Server
5. node siri_server.js

To test:

1. www.exampleserver.com/api/siri/vehicle-monitoring.json?key=a2aef3dc-3a02-4823-96e1-3347b535fe1a
2. www.exampleserver.com/api/siri/vehicle-monitoring.xml?key=a2aef3dc-3a02-4823-96e1-3347b535fe1a

Port Forwarding with Apache

1. sudo apt-get install apache2
2. a2enmod proxy_http
3. create vhost config in /etc/apache2/sites-available (see apache2-api-example.conf)
4. ln -s /etc/apache2/sites-available/apache2-api-example.conf /etc/apache2/sites-enabled/
5. sudo service apache2 restart

The Admin Console

An Administrator Console is provided for convenience. To access the console, visit www.exampleserver.com/console?key=adminKey The adminKey is set in config/server.json. You should change this key from the default value.

Analysis

The analysis/ directory contains code to perform analysis on the input GTFS-Realtime feed and/or the output Siri feed. Achived GTFS-Realtime data is fed through the converter and, using the internal data structures of the converter, detailed analysis of the transit system can be conducted.

The analysis/feedReplay/code/MockConverterService.js class provides access to all the internal data structures used in the GTFS-Realtime to Siri conversion process. These include:

• The indexed GTFS static data.
• The current GTFS-Realtime message JSON
• The internal train tracking data.

The analysis/feedReplay/code/scrapeState.js script uses this server's admin/get/server/state route to archive the GTFS-realtime feed to MongoDB. Currently, the scaper and the analysis code use docker-compose to create a MongoDB container. analysis/feedReplay/code/MockGTFS-Realtime_Feed.js pulls from the MongoDB archive to mock a GTFS-Feed that outputs messages as fast as possible.

Two examples of analysis code are provided in this repository:

• analysis/feedReplay/code/ExpectedArrivalTimeReliabiltyAnalysis.js
• analysis/feedReplay/code/LocationTrackingAnalysis.js

(Note: The admin/get/server/state route and the fact that the MTA_Subway_GTFS-Realtime_to_SIRI_Converter/lib/caching/ConverterCache.js exposes the converter's internal data structures makes realtime analysis tools a not-yet-implemented opportunity.)

Associated Projects

SIRI_Cache_Server

https://github.com/availabs/SIRI_Cache_Server

This project provides a Node server that will visualize the locations ov vehicles in a Siri feed. It keeps the locations data for the past 24 hours. See the README for deployment instructions.

sol-bot

https://github.com/availabs/sol-bot

This project will continuously make a high volume of requests to an MTA_Subway_SIRI_Server instance and logs problems. It will optionally send notifications to Slack if it detects problems, if a Slack token is provided. See the README for deployment instructions.

mta-transit-node-app

https://github.com/availabs/mta-transit-node-app

This Node/Sails app was used for data exploration of the MTA Bus Siri feed and the MTA Subway GTFS-Realtime feed. It also was used to ensure the fidelity of the MTA GTFS-Realtime to Siri conversion server. See the README for deployment instructions.