Case study

Smart Urban Mobility Platform

We’re helping to improve an IoT-enabled urban mobility platform, offering exceptional transit experiences to daily commuters and tourists

Key features

  • Ingest online timetables and live location and arrival feeds in GTFS format

    Ingest online timetables and live location and arrival feeds in GTFS format

  • Supply real-time transit navigation data

    Supply real-time transit navigation data

  • Provide advanced routing features for trip planning

    Provide advanced routing features for trip planning

Location-based services, IoT
Amsterdam, the Netherlands
Team size:
3 members
2016 – present

AWS / Flask / Jenkins / PostgreSQL / Python

About the client

Our Dutch client leverages the power of their enterprise mobility platform, petabytes of multi-sourced location data, and robust tools for custom app development to deliver the world’s best location-based data services and solutions.

For governments and all industry verticals, our client offers the most precise and comprehensive map data on the market collected within their custom mobility platform solution. Coupled with services like traffic data, transit, geodata visualization, geocoding, routing, and POIs, this data can be used to extract valuable insights and build sophisticated location intelligence software.

For the automotive industry, the company provides compatibility and interoperability of map data, flexible map updates, 3D mapping, extended driver assistance features relying on accurate traffic, road, and weather data, support for various distribution channels, and more.

For mobile app developers, the company delivers tools for building Android and iOS navigation-powered apps with rich map data, ample offline capabilities, and support for multiple touchscreen gestures.

Smart Urban Mobility Platform

Our client came with the challenge

A modern public transportation system is the lifeblood of any city. But an abundance of bus, tram, trolley, and underground routes and stations presents a mind-bending riddle for urban commuters, especially for visitors to large metropolitan areas. For years, our client has been promoting innovative urban mobility experiences by broadcasting accurate public transit information through their B2C mapping channels.

In 2016, our client identified a new niche in the public transit navigation domain. They decided to set up a separate B2B service to create a custom mobility platform to deliver rich transit navigation data based on online timetables and real-time location and arrival feeds. But their existing data pipeline and the routing engine feeding on the pipeline’s output were built around the commercial HaCon HAFAS transit technology. This placed one big limitation on licensing – our client was unable to sell their transit data directly to businesses and enterprises.

Our client decided to rebuild their data processing facilities to avoid any restrictions imposed by licensed third-party technologies. Since 2015, Intellias had already been collaborating with this client on several automotive and mobile navigation workstreams. So we took up this new challenge along with the others to deliver mobility platform development services.

Intellias is developing a smart urban mobility platform

The project began by setting up an Intellias transportation team. This was a challenge in itself, given our client’s strict selection criteria in reference to candidates’ professional skills and experience. Intellias scaled our client’s engineering capacity with a team of lead and senior Python developers.

The Intellias public transportation team is gradually rebuilding the legacy data processing pipeline to ingest raw data from vendors in the common General Transit Feeds Specification (GTFS). The Jenkins-based solution accepts public transportation timetables and real-time tracking data from global transit agencies. The data goes through a complex 19-step processing flow, during which it’s extended, optimized, merged, concatenated, normalized, and validated. The pipeline functions in continuous data delivery mode.

Meanwhile, the client’s development team is reworking the proprietary routing engine so that it no longer uses any external licensed components. This will soon free our client from any license dependencies related to further monetization of their transit data service.

To support this transition, Intellias engineers implemented a mechanism that converts transit data between the HAFAS and GTFS formats. This enables us to sequentially migrate individual pipeline components to GTFS without hampering existing production processes. Intellias engineers also implemented various pipeline plugins for fetching real-time location and arrival data for many modes of public transportation. This will allow for the display of correct arrival and departure times, for example on bus and train station screens. Currently, our client’s service delivers real-time position data for Ottawa, Melbourne, Naples, Miami, and other large cities.

The Intellias team is contributing to the development of new features and components as well. For instance, we spent about a year coming up with a fully functional geometry editor. This UI tool belongs to the pipeline management suite and allows transportation data engineers to manually edit the geometry of public transit routes on a real city map. This tool was needed because the pipeline was sometimes unable to automatically build proper route lines based on incoming data. Once route lines are corrected, the transportation data engineer can reprocess data for a certain region.

As far as performance is concerned, Intellias engineers made considerable improvements to the pipeline’s workings. Specifically, we managed to reduce the execution time of the stop-matching algorithm several times over. This complicated algorithm takes the IDs of transit stops from different agencies and matches them to the system’s internal stop IDs. Our contribution improved the entire compilation process. Additionally, we developed a mechanism that merges inconsistent information on public transport stops from different transit agencies. For example, when a stop lies on both a bus route and a tram route, bus and tram operators can present this stop’s GPS position with slight deviations. Our algorithm detects these inconsistencies and merges the stops into a single physical transit stop on a city’s public transport map.

We’re achieving great results together

After almost three years of cooperation, Intellias has proven its status as a reliable and competent outsourcing partner. Our contributions to the enterprise mobility platform so far have helped spark interest from potential data service customers – municipalities and recognized enterprises in the automotive, transportation, and other domains.

Our collaboration is constantly growing. The Intellias project team is now in the process of hiring additional engineers required to cover a new feature set for the future transit service. This new transit service will power advanced trip planner applications, public transport service analytics, transit-savvy mobile and in-dash navigation systems, and more.

The solution Intellias is helping to develop offers the following to urban commuters:

  • Comprehensive public transit data for over 1,000 cities worldwide (and counting)
  • Guided navigation packed with great online and offline capabilities
  • Integration with all possible modes of urban transit including car sharing hubs and cycling paths
  • Bicycle route calculations with detailed consideration of cycling infrastructure
  • Easy navigation to nearby taxi stands and parking lots
  • Highly localized cost and travel time estimates for multiple transit modes

Thank you for your message.
We will get back to you shortly.