Big data analytics can improve public transport with historical data that can be used to predict future behaviour.
Big data analytics can improve public transport with historical data that can be used to predict future behaviour.
( Source: gemeinfrei / Unsplash)

Big Data How big data analytics is improving public transport

Author / Editor: Jamie Thomson / Isabell Page

Almost every company in the public transport sector is faced with the challenges of reducing costs and increasing revenue. Combined with high customer expectations, these challenges need to be addressed in an innovative way.

Big data analytics is the often-complex process of gathering and analysing large volumes of varied data sets to uncover patterns, trends and customer behaviour. The insights garnered from big data can help public transport companies make more informed decisions when it comes to optimising their services and improving the customer experience.

Let’s take a closer look at how big data analytics is improving public transport in Europe:

How public transport companies collect data

According to the Union Internationale des Transports Publics (UITP), 60 billion journeys are made on public transport in Europe every year. Out of the journeys, 32 billion are made by bus, 8 billion by tram and light rail, and 9 billion by metro. All these journeys provide transport companies with high volumes of data through everyday operations.

Technology like Automatic Vehicle Locations (AVL) systems are used to track the location of buses and trains and passenger counting systems are used to record the number of passengers using the service on a daily basis. Among other variables, this information can be used to improve the overall customer experience. For example, if location data shows that vehicles become stuck in traffic at specific times, additional services can be added to ease overcrowding and provide a more reliable service.

Other sources of data generated by public transport companies include financial information collected by fare collection systems and the number of journeys taken by passengers through travel cards. This information can be shared across departments via cloud computing, enabling public transport infrastructure to provide a bigger picture of their service, enabling better decision making. For example, in Florence, their public tram network uses software called ELASTIC to monitor interaction between public vehicles and the city. The data is used to improve traffic flow, reduce accidents and reduce the maintenance costs of vehicles. In turn, the network can guarantee system response times while ensuring that data remains anonymous.

Optimising operating costs and increasing revenue

By tracking when services are busiest, companies can optimise their services to be more cost effective. Knowing exactly how many passengers use specific routes at various points in the day enables companies to predict peak periods more accurately. This type of big data can also be used to anticipate how services may be affected due to events like holidays, public gatherings and even bad weather. In processing this data, companies can plan their operating procedures more efficiently and cost-effectively. They can also use the data to improve the retention rate of customers, who would otherwise seek alternative means of travel during busy periods. By anticipating peak times, companies can ensure that their services are offering convenience and ease.

With growing pressure from the European Commission to reduce carbon emissions, companies face the challenge of making services affordable while continuing to increase their revenue. With the help of big data analytics, companies can use historic data to predict future sales figures, enabling them to optimise their products. For example, loyal customers may be rewarded with cheaper fare prices through season tickets.

Reducing road traffic congestion

Big data enables transport companies to predict when roads will be congested and to implement strategies to ease congestion. For example, additional services could be put in place during peak hours, or in the case of buses, alternate routes could be added to avoid the busiest streets, reducing the burden on commuters.

Because analytics systems can process data in real-time, transport companies can react to unforeseeable events, such as bad weather and traffic accidents, as they happen. Services can be re-routed to ease congestion and speed limits can be restricted in heavy rain or snow. For example, in partnership with tech company IBM, the city of Dublin uses Big Data to identify and resolve sources of traffic congestion in its public transport network, improving mobility for commuters. By integrating data from sensors with geospatial data, the city’s transport sector can monitor and manage traffic in real time.

Improving maintenance and operation of services

Big data is also being used to streamline the maintenance of public transport vehicles. By combining a variety of data sources, companies can do more analysis of their vehicle’s equipment, identifying any repair issues quicker than they would during scheduled maintenance checks.

When data is integrated with new systems like Amazon Web Services (AWS), transport providers can use predictive modeling to evaluate the condition and performance of vehicles. For example, if an analysis was to reveal an issue with a vehicle, the operator can take it out of circulation to avoid costly long-term damage. Predictive modeling can also be used to improve operational efficiency by monitoring and managing employee absenteeism. Historical data about events and weather can help operators predict when a higher percentage of their workforce is likely to be absent. This allows companies to plan for backup operators, minimising service disruptions.

Big data analysis can also be used to show how staff are performing compared to their peers in the same division. This can help companies identify staff training opportunities to improve the overall quality of the service.

Closing thoughts

  • In order to create an efficient public transport system for commuters, companies need to understand how people are using their services.
  • Big data analytics provides transport companies historical data that can be used to predict future behaviour. These insights can be used to improve their service, reducing costs, increasing revenue and improving the customer experience.
  • The more data that public transport companies can gather, the more insights they have at their disposal to come up with innovative ways of solving the challenges posed by modern transport infrastructures.