COVID-19 has cast a long shadow on public transport, and rail in particular. The rapid embracing of digital meetings, principally due to the convenience and efficiency of working from home, has changed the frequency and reasons of why people travel. Unfortunately, and perhaps critically, the systems and processes to fully comprehend and understand these changes, or indeed to adjust to this changed behaviour, are not in place. For the first time in twenty-five years, rail is a solution looking for a problem. E.g. Most of the UK rail network has the capacity and capability to move large volumes of commuters, unfortunately they have not all returned to the same pattern of travel as before. Critically, if a problem for rail to solve cannot be found, the spiral of decline from the current lost revenue will continue, repeating the challenges of the post war years of 1918-23 and 1946-1968. The changes are exacerbated by the need to adjust to the media savvy expectations of Generation Z and the upcoming Generation Alpha (those born from 2010). So, something needs to be started now, given the long-term planning cycles required, if these groups are to become long-term users of rail to the extent needed to sustain rail services. In short, operators need to offer a better customer proposition regarding communication, removing the friction and stress from rail travel. They need to proactively start creating the Smart Passenger.
If this is to be done, then key questions are "how" and "when" can information shape passengers' decisions? Answering this has the potential to increase the number of positive "travel by rail" decisions. To understand the passenger decision-making process our research sought to examine multiple scenarios associated with the door-to-door rail journey. Simply put, we mapped the passenger's decision-making process at each stage, along with possible consequences of those decisions for the transport networks. We also classified passengers into two types: problem seekers and problem solvers. The problem seeker checks information before leaving home. The earlier passengers obtain information the better – they can choose to stay at home or take another mode of transport, while the transport hub can then collate passenger information to better understand their own capacity. The problem solver receives information after they have left, then aims to solve the problem en route. For instance, when a network failure means all trains are cancelled, the passenger must now decide what and which transport methods to use instead – the information they receive is crucial for that decision. For this to be effective, the rail station would also need to share information with other hubs (taxis, buses, trams), as this will drive demand in other areas.
Clearly, this means that two types of information need to be given to potential travellers at different points in the journey decision process, to account for both sets of needs. There is also a critical opportunity here for operators to "receive" information as well as "send / pulse". The received information being processed to better understand the nature of the issues and "pulse out" more targeted information. This has the potential to create a virtuous cycle of useful information flow.
For this theoretical model to work in practical terms, the parameters of each aspect of the system need to be understood so the problem can be agreed within that context. As an example, if the capacity of a station and the "knock on" impact of delays, of various levels of crowding, are not understood by the operator, poor quality information will be sent to the problem solvers and information available to the problem seekers becomes insufficient for a decision to be made. This therefore creates a negative cycle through the increasing number of hurdles to travel for both groups of potential travellers (the problem seekers and the problem solvers).
From our research the key aspects of decision making were identified as: At Stage 1 (planning and buying tickets in advance), train schedule and ticket offer information were most important; at Stage 2 (travelling to the station) and Stage 3 (at the station), participants identified train status updates as the most important information; during Stage 4 (travelling abroad), participants identified travel route, current location and upcoming stations as the most important information; and for Stage 5 (at the destination), advice on available local bus, taxis, was the most important information.
In order for rail operators and stakeholders to apply this learning the key requirements are: 1. To understand and publish the parameters of operation, e.g. capacity and consequential impact of crowding; 2. To push this information to users; 3. To develop feedback loops to understand how the pushed information is actually altering customer behaviour to refine current and future messages. The era of the Smart Passenger is rapidly approaching – Generation Alpha will have high expectations for appropriate and timely travel communication. Rail stakeholders ignore this at your peril.
Please note: Smart Passenger White Paper available here: https://www.rasic.co.uk/post/smart-passenger-understanding-public-transport-passenger-touch-points
