How big data and m2m is helping optimize public transit
Imagine a public transit system that learned what routes were most popular based on passenger movement or flexible public transport that modified routes to reflect where, when and how passengers would travel at any given time. These scenarios are becoming a reality as big data analytics and M2M improve public transit.
The cornerstone of these projects is the huge amount of data gathered by public transit systems. Buses are tracked with GPS, door sensors count passengers and smart cards identify passengers. More information comes from ticket machines, fare validators, automatic vehicle monitoring systems while even more is gathered by asset management, scheduling systems and mobile applications.
breaking down the silos
The challenge for operators is to gain insight from this deluge of data from incompatible systems and formats. Several companies including recently launched consultancy, Urban Insights, are working to help them meet this challenge. Urban Insights collates this incompatible information, reformats it and integrates it with other data before pushing it through cloud-based Apache Hadoop systems for analysis.
At present it works with San Diego's Municipal Transportation System to analyse the data to understand whether its routes and schedules actually meet passenger need. That project requires that it collate lots of data to piece together the journeys people take, because while the transit system records the different steps of a journey, it doesn't connect them together to understand the whole trip.
Another company, Bridj in Boston, US, is attempting to create flexible transport systems. Bridj has its own fleet of Wi-Fi-enabled shuttle buses for which it dynamically chooses the best routes using big data analysis of passenger need. In this model, traditional bus stops become flexible and to reach a destination a passenger will consult a smartphone app to find the closest available stop at the time of travel. The flexible network assesses route speed, popularity and predicted demand to get passengers around town more efficiently.
M2M and big data also improve train travel. Urban Insights' parent company, Cubic Transportation Systems, offers the Clipper card-fare payment system. When customers enter a Bay Area Rapid Transit (BART) they find that combining Clipper data with information from the scheduling and vehicle location systems enables BART to determine which trains are overcrowded in order to ease such congestion.
A company called Urban Engines has also built a system that takes data (mostly from commuter transit cards) and uses algorithms to figure out how commuters and the transportation flow are behaving. It can figure out delays, waiting time and traffic flow across the whole network.
integrating social media
Another useful source of data to inform public transit is social media. A three-year RMIT University project (Integrated and Real-time Passenger Travel and Public Transport Service Information System) is attempting to pool live information from public transit providers, weather information, historic data, social data and personalized user information such as items on their to do lists through a smartphone app to improve transit.
For example, a passenger may need to shop for an item and also get a train. The app may notice the train is delayed and tell the traveler they now have time to reach the shop before they catch the train, enabling passengers to use time more efficiently than waiting for transport.
In theory making social information available to public transit management systems may even enable transit systems to automatically prepare for one-off traffic events in which large number of people are preparing to travel to a defined location simultaneously. Transit should then be able to ensure sufficient transport is available at that time while rerouting general traffic flow to minimize delays.
These are just some of the promises of big data and M2M have for public transit systems in a connected world. They should boost customer satisfaction and service quality, reduce costs, and make transit systems more efficient, convenient and accessible.
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