Traffic Monitoring System with IoT data analytics
- #Big data
- #Cloud
- #Data analytics
- #IoT
- #Web development
About the Client
The municipal public transport department at one of the biggest cities in Central America.
Business Challenge
Timely transportation is essential for all cities. Especially when we talk about areas as densely populated as Mexico City, with a population of more than 20 million people, including the suburbs.
The Mexican government licenses private bus companies to provide quality transportation services to the capital’s citizens and guests, so the local public transport department signs a Service Level Agreement (SLA) with each vendor. The SLA specifies time intervals between buses, route duration and more.
The client contacted us to develop a system that allows automatically controlling SLA agreement to make public transportation in Mexico City more efficient.
Solution Overview
Each public bus in the city has a WiFi device with a set ID to track its position on the route.
The Traffic Monitoring System we built uses the IoT infrastructure with the WiFi device registration sensors that are installed on public roads.
It provides the operational transport control center with easy-to-use visual information about the SLA requirements met for each route.
Our Traffic Monitoring System included:
- Bus routes and schedules builder (more than 400 routes)
- Control panel with information about the SLA requirements met
Project Description
Our biggest challenge was to process large amounts of data from the sensors: individual sensors transmit new data every 10 seconds, and we had to work on a month’s time span. So, big data storage was developed to consolidate all data from road sensors. The SLA terms were interpreted with numbers.
Data representation was another challenge. Its amounts were larger than a human can supervise, so we implemented hierarchical data representation. It allowed going from a top-level view for months deeper into the details of a selected week or day.
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Technological Details
Our team used Python and Django for back-end development, while the front end was developed with HTML5, CSS and JavaScript. Bootstrap allowed us to create a customized user interface. Other technologies we worked with included PostgreSQL, Leaflet and Open Street Map. On top of that, we used the MongoDB aggregation framework to build a solution similar to the OLAP cube we used down the pipeline for further data processing.
