Road Network Operations
& Intelligent Transport Systems
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Journey Planning APPLICATIONS

So-called 'Big Data' has opened up a whole world of information that can be accessed on the move. An increasing number of stakeholders (such as Transport for London in the UK) provide access to their real-time data for applications and websites so that it can be utilised in a variety of methods (See Open Data). Google Transit, CityMapper, and others use this data to provide an integrated service covering all aspects of a city's transport. Delays, incidents and other problems can be instantly reported - and changes to pre-planned routes or suggestions for alternative, unaffected routes, can be made to the user.

Journey planning is an important component of traveller services. A location-based journey planner application will automatically detect the location of the user, minimising the additional information that the user needs to input to generate their journey plan. Once the location is detected the user enters their destination and time/date for travel and the journey plan can be produced. Generally, with mobile applications, the journey plan is generated ‘off board’ within a central system - with the results communicated to the mobile device. Location-based journey planners can be single or multi-modal and may or may not include road based journey planning. (See: Journey Planning)

The public transport data needed to support a successful location-based application for journey planning needs toinclude the public transport network, stopping points and schedule information. In Europe, Transmodel is the European Standard reference data model for public transport. 

Data exchange standards generally exist for this public transport schedule information transmission including: VDV-452 in Germany and TranXchange in the UK The public transport access node definitions are also often standardised. Examples include:

  • Identification of Fixed Objects in Public Transport (IFOPT) and
  • NETwork Public transport EXchange (NETEX)

Latest developments in this field include mobile applications presenting the live locations of public transport vehicles. Upcoming developments include ‘augmented reality’ where mobile apps are expected to enable the user to point their mobile device at a public transport stop or vehicle to determine the destinations available from the stop or the vehicle. The challenge for transport stakeholders is to make the data available to support these enhanced services.

Other applications (or 'apps') available, that use this type of data are numerous and offer a range of services for public transport users. One example is Moovit, an Israeli-based application company that has provided a system enabling users to report on the level of crowding on public transport services - in addition to other factors such as the cleanliness, comfort, and the driver’s performance. In the long-term, these reports provide a picture of the busiest services (to be avoided by the space-conscious commuter), which can be integrated into the journey planner. San Francisco’s BART system and the Netherlands’ Rail Network are using similar applications - with the Dutch using historic loading data to indicate the level of over-crowding on some trains.

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