PUBLIC TRANSPORT DATA Analytics
THE CITY WHOSE RESIDENTS RAVE ABOUT THEIR PUBLIC TRANSPORTATION
January 19, 2021
What can public transportation authorities and operators do to get more people using public transport? Is there a solution that brings; population data, public transportation data and residents activity data together to help public transport decision-makers? Yes, we propose arlas.city, a geo-analytic SaaS.
Toulouse tram and metro lines displayed on arlas.city, composed from Tisséo’s GTFS data.
But first, let us look at the challenges in public transportation data analysis.
“A developed country is not a place where the poor have cars. It’s where the rich use public transportation.”
This statement by Enrique Penalosa can’t be truer. In other words, using public transport should be a choice and not a need.
A recent study by the European Commission on ‘quality of life’ in cities surveyed nearly 60,000 European city residents with a section on how public transportation affected residents. It revealed that:
- Men, people with a higher income and families were less likely to use public transport.
- Retired people, younger folks – between 15 and 24 years old, and those with only secondary education – frequently used public transportation.
- Big-city residents like London, Prague and Paris were more likely to use public transportation than their counterparts in smaller cities.
But why would cities experience less public transportation uptake when data indicates that with each passing decade, cities have become even denser? And what would it take to move private transport users to public transportation? One of the many answers is; intermodalism.
Intermodalism not only gives users multiple options to reach their destination, but it also allows public transportation authority to quickly adapt services to growing needs. Creating transfer zones where frequent transport lines intersect. This allows people to reach more destinations faster.
In the European Commission study, it was no surprise to find that there was a strong correlation between efficient public transport and happy residents.
What was surprising was that the cost of public transportation did not have a significant impact on resident’s happiness.
Residents want efficient and reliable transportation.
To promise these, public transportation authorities and operators mix up different modes of travel, ensuring that travellers get the best available option for each stretch of the journey.
Choosing the right mode highly depends on staying connected to the ever-changing needs of the users. This means constantly looking at data to offer seamless mobility.
But if there is an industry that churns out volumes of data every day, then public transportation would definitely be amongst the top. Big data presents a lot of opportunities which of course come coupled with challenges. Challenges that mostly come from data collection that precedes its uses and is often stored in different silos.
Public transportation authorities and Operators now find themselves looking at multiple lakes of data, swimming with diverse dimensions, which then leads to time wasted in decision making. And, sometimes the decisions, arrive when the original scenarios have changed.
Thanks to the advancement in location intelligence, going back in time and referencing volumes of historical data, merging data in silos and aligning it is now possible.
Arlas.city presents public transportation authorities and operators with a robust solution that scales with their data. They are able to easily and quickly dive into their aligned data for a unique analytical perspective of their entire network.
Visual display of ‘Segments speed’ on a network’s lines: bus, metro and tram.
Not only, that, but public transportation data analytics can also now be plugged into the cities’ population data and residents’ activities, so that authorities and operators can really respond to present events and projected trends.
Population density data created and visualised in arlas.city
Arlas.city, a geo-analytic platform for public transportation that can facilitate all this.
Bordeaux Métropole transport network on arlas.city, composed from Keolis GTFS data.
Arlas.city which is designed for public transportation authorities and operators gives decision-makers the opportunity to make timely data-backed decisions, to meet residents’ needs and desires of efficient public transportation.
Aligning data from all modes with other dimensions would save lots of time. But the ability to quickly visualise it and stay ahead of potential roadblocks would result in happier residents.
These residents only want:
- efficiency – are public transportation services reliable?
- accessibility – are the services where people want them?
- comfort – do they meet people’s needs?
Another European Union analysis on transport and mobility found that in 2017, Europe residents spent an average of 29 hours annually in traffic congestion. UK residents were the most unfortunate with an average of about 46 hours annually, up by five hours in just two years where they spent an average of 41 hours annually in 2015. France’s residents, while not recording a significant increase, also went up from 29 hours in 2015 to 30 hours annually in 2017.
Most travellers just want to go from one place to another. They want to take the shortest time getting there. They are willing to pay a fair price for it. Including isochrons in public transportation analysis helps decision-makers to provide multiple short routes to diverse destinations.
Isochrons’ mapping of travel time in minutes from one point to different destinations.
Travellers choose the services that bring them the closest to their destination for either the lowest cost or in the least amount of time. In the era of smart cities, residents expect their public transportation services to be even more effective, dependable and convenient.
Setting up public transportation services for thousands (let alone) millions of users cannot be easy. What with continuous changes in needs like; a new school in a neighbourhood, a shopping mall or even unpredicted global events like COVID-19 where working and other social habits are affected. By analysing:
- ticket validation data
- scheduled vs achieved trips
- delays
- population
- residents’ movement
Decision-makers can understand residents’ past trends to respond to current events and even predict future needs.
Get in touch with us if you want to see arlas.city’s application on your data. We are happy to organise a live demonstration and to help you meet your travellers’ diverse needs for public transportation.
