Case study

Mobility Solution for a Seamless Transit Experience

We’re improving an IoT-enabled custom mobility platform to offer faster and more cost-efficient journeys to daily commuters and travelers.

Key features

  • Process online timetables and live location & arrival feeds in GTFS format

    Process online timetables and live location & arrival feeds in GTFS format

  • Supply real-time transit navigation data

    Supply real-time transit navigation data

  • Provide advanced features for route planning and smart parking

    Provide advanced features for route planning and smart parking

Location-based services, IoT
Berlin, Germany
Team size:
5 members
2016 – present

AWS / Flask / Java / Jenkins / Kotlin / PostgreSQL / Python

Business challenge

Our client, one of the world’s leaders in location-based data services and solutions for smart urban mobility, intended to set up a new B2B system to deliver rich navigation data to a variety of transportation services, including for public transport, bike-sharing, car-sharing, taxis, and car rentals.

However, it turned out our client was unable to sell their real-time transit data directly to businesses and enterprises because their existing data pipeline and routing engine were built around HaCon’s commercial HAFAS technology. This placed a big limitation on licensing and called for a major change in the data format.

Our client decided to rebuild their data processing facilities to avoid any restrictions imposed by licensed third-party technologies. This meant recoding every single step of data processing in the pipeline to convert data from the private HAFAS format into the widely accepted GTFS standard.

Since 2015, our client had already been collaborating with Intellias on several automotive and mobile navigation workstreams. This led them to trust our expertise and request a team of lead and senior Python engineers to develop a smart urban mobility platform.

Mobility Solution for a Seamless Transit Experience

Smart urban mobility platform delivered

Intellias set up a mature development team that has scaled our client’s engineering capacity in several respects:

Data pipeline and routing engine

Our primary goal was to rebuild the legacy data processing pipeline to ingest raw data in the common GTFS format. The Jenkins-based smart urban mobility solution we delivered accepts public transportation schedules and real-time tracking data from transit agencies around the globe. This 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.

We also reworked the proprietary routing engine so it no longer uses any licensed third-party components. This freed our client from any licensing restrictions so they could monetize their transit data service.

To support this transition, Intellias engineers implemented a mechanism that converts transit data between the HAFAS and GTFS formats. This enabled us to sequentially migrate individual pipeline components to GTFS without hampering existing production processes.

Our engineers also implemented various pipeline plugins to fetch real-time location and arrival data for many modes of public transportation. This allows the system to display accurate arrival and departure times, for example on bus and train station screens. Currently, our client’s service delivers real-time position data to transportation and travel agencies throughout the world.

Data verification and enrichment

Our team is in charge of processing, testing, refining, and improving low-quality data based on historical data owned by our client. The result is high-quality, credible data that makes our client a reliable go-to source of transit information for global services including Facebook and Amazon.

New features

The Intellias team is constantly updating our client’s software with new components and features as required.

One feature we built is a fully functional geometry editor – a UI tool that belongs to the pipeline management suite and allows transportation data engineers to manually edit the geometry of public transit routes on city maps. This tool was needed because the pipeline is sometimes unable to automatically build proper route lines based on the incoming data. Once routes are corrected, a transportation data engineer can reprocess data for the affected region.

We are now adding transport sharing functionality to the navigation platform-based app to enable access to car-, bike-, and scooter-sharing services in Europe. This will make real-time search for vehicles possible and allow users to see vacant parking spots where they can leave shared vehicles.

Performance improvements

Intellias engineers have been working on the system’s algorithms and have made considerable improvements to the pipeline’s functionality. Specifically, we have sped up execution of the stop matching algorithm by several times. This complicated algorithm takes the IDs of transit stops from different agencies and matches them to the system’s internal stop IDs. We improved the entire compilation process.

Additionally, we have 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.


As building IT infrastructure is extremely capital-intensive and unaffordable for the majority of transport agencies and even municipalities, we came up with the idea to develop tailor-made solutions for businesses. With all the necessary software and algorithms at our disposal, our team created a customized smart urban mobility application to build maps, display schedule data, create points of interest, and work out routes across a city.

With this new applications, users can build routes based on bus, tram, trolley, and underground stations as well as user-defined points of interest. The product also provides information on layovers and walking routes. The existing app works both online and offline, making urban mobility faster, safer, and more comfortable.

Business outcome

After four years of cooperation, Intellias has proven its status as a reliable and competent outsourcing partner. We quickly scaled our client’s engineering capacity and put our client in a strong position to provide mobility as a service.

Our team keeps the whole data processing system up and running. We also support transformation processes within our client’s organization. The smart urban mobility platform we built will now connect to our client’s Open Location Platform (OLP) for data storage. Our team will oversee this shift to provide seamless data transfer and integration.

Intellias contributions to the enterprise mobility platform have already 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 covers new feature sets for emerging transit services that power advanced trip planner applications, public transport service analytics, and transit-savvy mobile and in-dash navigation systems.

The solution we’re developing offers these advantages to urban commuters:

  • Comprehensive public transit data for over 1,000 cities worldwide
  • Guided multimodal navigation packed with great online and offline capabilities
  • Integration with all possible modes of urban transit
  • Bicycle route calculations with a 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

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