Mapped interactions per hour

Weather measurements a minute

Road measurements a minute

Train movements per hour

UK Transport interactions

  • Received and mapped in real time
  • All stored and processed
  • All available in real time
  • All used for predictive modelling

Zipabout IMaaS Platform

We process and normalise billions of pieces of data covering anything that can affect, or is affected by, an entire transport network

Core platform

  • Highly scalable, real-time data processing platform
  • Engineered specifically for use with Intelligent Mobility and MaaS applications
  • Can be deployed globally
  • Experienced team with background in developing large-scale data systems for online advertising

Our Data

  • Real time, multi-modal model of entire transportation system
  • Operational, environmental and behavioural data for road, rail, bus, human and more
  • Processed using advanced technologies e.g. Graph, NoSQL, Machine Learning and Stream Processing
  • Platform generates unique passenger and service level data insight
  • All data stored for analysis

Platform approach

  • Platform approach allows for integration with existing systems and new MaaS services
  • Standard web services interfaces integrates with numerous journey information systems (e.g. Google Transit)
  • Compatible with CRM and BI tools, adding unique insights to existing processes and systems

 

The Zipabout MAAS platform is a cloud-based set of highly scalable web services, engineered specifically for use in the rapidly developing Intelligent Mobility sector. Our team of in-house software engineers are led by a management team with considerable experience in developing large scale data systems for the online advertising sector, and are bringing a unique approach to solving some of the distinctive challenges in the sector.

Core Platform

At its core, the Zipabout MAAS platform is a large scale, real time data processing platform which is capable of consuming live streams of operational, environmental and passenger level behavioural data across an entire transportation network. All of this data is processed, normalised and made available both in real-time and historically. Additionally, we utilise advanced Machine Learning techniques to identify patterns in the data, allowing us to make statistically significant predictions around future performance of the network and its effect on Customer Experience.

Some examples of UK specific data sources that we currently process are:

  • Real-time Signal Berth data from Network Rail
  • Real-time bus movement data from Traveline and various LA’s
  • Metro, bus and taxi movement and occupancy data for the Singapore Land Transit Authority
  • Social Media data from the Twitter Firehose and Facebook Topic data
  • High resolution weather modelling data from the UK Met Office
  • Real-time FVD and ANPR data for the UK strategic road network
  • Granular inputs from region specific sensors, such as car park and air quality
  • Disruption event data for UK Rail (DARWIN) and UK Strategic Road network (Traffic England)

We also consume numerous commercial data sources relating to sporting events, concerts and other events affecting the transport network.


What do we do with all this data?

We utilise our detailed, real-time model of the transportation system to deliver highly relevant, truly multi-modal information for both passengers and operational staff around current and predicted journey performance. Utilising techniques developed by members of our team over the last 10 years in the online-advertising sector, along with some of the latest technology such as Graph databases, NoSQL and Stream Processing systems, we are able to deliver highly targeted, personalised and truly relevant information in real time, and at massive scale. In addition, we track and monitor detailed user behaviour allowing us to understand how individual passengers plan, access and use the transport network, and allowing us to engage, on the transport operators behalf, with users on an individual basis.

Our platform’s unique capabilities allow us to understand:

  • Passenger intent – what regular journeys do individuals make and how do they actually use the transport network on a day-to-day basis
  • Network demand – using data generated from user interactions with the platform, we are able to generate real time models of demand on individual services, interchanges and the wider transport network
  • Customer experience – by engaging with passengers directly and unobtrusively as part of their existing routine
  • Causes of disruption and asset failure – what are the triggers that cause disruption and how can they be predicted and avoided

Platform Approach

The architecture of the Zipabout MaaS platform allows us to provide flexible access to any part of our platform through standardised web service interfaces. We are already licensing access to our underlying data-sets for research purposes to various organisations including the DfT, Transport Systems Catapult and other commercial partners. However this is just the start – potential uses for our platform include:

  • Integration with existing journey information systems, journey planners and retailing systems in the industry
  • Highly targeted promotions relevant to users of the transport network
  • Business Intelligence for operational decision makers at Transport Operating and Infrastructure businesses
  • Transport modelling for Local Authorities and other government agencies
  • Housing and commercial development planning

That's nothing.

If you want to see what we can really do, drop us a line.

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