In many cities of the world the public transport isn’t as popular as it could be. The main problems that people are complaining about are related to one common source: difficulties of navigation and high degree of complexity. In large cities the number of bus or tram routes can easily reach several hundred – for example, London has over 700 different bus routes and around 19000 bus stops, which makes it quite a challenge to pick the right route at the right time, especially when available time is very limited and decision to board a particular mean of transportation has to be made quickly.
Of course, the largest cities are the busiest as well, but if you travel to an unknown city for the first time you could certainly face similar navigation problems even when the number of routes is considerably lower than in London, New York or Madrid. When you include additional factors into equation, such as transport delays, rerouting due to traffic jams or construction works, getting to the point of destination may become tricky.
Luckily, engineers from all over the world are trying to fix this problem by making use of mobile information systems. Many tools have been developed already, including online and mobile public transport planning sites and apps. Using these tools you can easily obtain information about how long will the trip last, which routes will be required to reach the destination and what alternatives are available.
However, there is still lots of space for improvement, argue the researchers from the Open University, England, and the University of Duisburg-Essen, Germany. According to their opinion, users often require more detailed information to deal with so-called micro-navigation decisions. For example, traveler may need information confirming if the bus that just arrived is the right one for the planned trip, is he (or she) on the right bus, how many minutes are left till the required stop, etc. Sometimes more extensive user support could be required, including providing information related to the locations of the nearest public services or advises on how to resume trip if a wrong bus was taken or the right stop was missed.
“Micro-navigation decisions are highly contextual. They depend not just on time and location but also on the user’s current transport mode (standing outside of bus, riding on a bus etc.) and the concrete situation of a passenger (waiting for bus to arrive, riding on the correct bus, riding on the wrong bus, getting near the getting-off point etc.)”, explain the researchers in their article. If a passenger has some degree of sight/hearing/mobility disadvantage the need for the micro-navigation solutions becomes even more relevant.
In order to improve the public transport experience the authors of the research article developed an Urban Bus Navigator (UBN) system. This system is a prototype micro-navigation tool for bus passengers built using the concept of internet-of-things. As the researchers note, their development provides continuous, just-in-time, end-to-end guidance for bus passengers via smartphones which connect to the online infrastructure using the network of WiFi-enabled buses.
Currently, the system is already adapted for all stages of journey: before a user gets on the bus, during the ride, and when user gets off the bus. Trip planning, trip monitoring, transport mode detection tools are integrated with the route computation framework (as shown in the diagram above). Additional information related to user location can be also collected using GPS.
According to the article, the Urban Bus Navigator system has already been deployed and tested during a half-year long trial in Madrid. The researchers collected feedback from a team of recruited bus users who rated their overall trip experience during 140 bus trips. Later their reports were used to identify issues related to the usability of the UBN system.
The results of the experiment showed that some technical issues had to be solved before the system could be fully implemented. The main problems were related to inaccurate bus ride detection and some inconsistencies between the data provided by the UBN app and the real world, note the authors. These problems were later solved by implementing an improved version of the bus ride detection, in which a more precise user movement speed inference was used to reduce the number of false positives.
Users also reported several positive experiences, including reduced uncertainties and more relaxed travelling, better visibility and accessibility of travel information comparing to existing mobile transport apps, and effective support for cognitive tasks required for bus journeys. “The design of UBN is generic so that it can be adapted to any city that has a digital urban transport infrastructure similar to Madrid”, the team concluded.
Written by Alius Noreika