Successful demand management, however, goes beyond merely predicting levels of demand and developing strategies to deal with instances of high or low capacity. It can also be used to seek alternative ways of meeting customer needs, and even to ‘nudge’ the customer to behave in a way that is beneficial to both the individual passenger and the network as a whole.
For the rail industry, this might involve having a more even distribution in a day, moving away from the sharp division between peak and off-peak travel, or optimising seat occupation. A successful smoothing of demand would allow the network to be used more efficiently, and give passengers the chance to travel more comfortably during peak-time, or at lower fares for those with greater flexibility.
In the past, this has often been a case of ‘easier said than done’, but a host of new technologies, the availability of new generation data and a gradual shift in our approach to mobility promises to give the rail sector much greater control over demand in the near future.
Digital, personal, adaptable transport
Other blogs in this series have already set out how the travelling public will benefit from New Mobility Services (NMS) and Mobility as a Service (MaaS) – the integrated, customer-focused transport system of the future. But these new approaches, and the technologies that underpin them, will also equip the rail industry with the tools it needs to really fine-tune demand management.
MaaS will enable passengers to choose from a full range of personal preferences – ranging from their preferred mode of transport to price sensitivities and accessibility requirements – which will then allow them to receive a shortlist of the most relevant transport options.
As explained in the previous blog on data’s untapped potential, this customer-generated data can be anonymised and combined with industry-generated data to track aggregated passenger movements and forecast congestion risks or areas which should be targeted to improve the service to users. Apps and other appropriate passenger information tools can then be built upon this information in order to better manage peak demand by, for example, encouraging users to take alternative routes or choose to start their journey later.