Understanding the behaviour of public transport users is of paramount importance for public transport planners and operators. The acquisition of information on the usage of public transport services has traditionally been based on interviews combined with counts of boarding and alighting flows at railway and metro stations and bus stops. In addition to being expensive and time-consuming, these methods provide a limited picture of demand and are unable to capture the variability of travel behaviour over time.
The widespread implementation of vehicle location systems, automatic passenger counters and automated fare collection systems (e.g., smart cards) has enabled new ways of analysing public transport demand. New data sources offer the opportunity to obtain high-quality information over any time period at a very low cost, but they also pose a number of challenges. Particularly relevant is the correct estimation of trip destination: since most smart card systems only record boarding information, there is a need to estimate passenger alighting stops. Simplistic approaches, such as assuming that the most likely alighting station is the boarding station of the next journey, do not reflect the complex reality of public transport usage patterns, which makes it necessary to develop more sophisticated methods for identifying trips and trip stages.
Leveraging the potential of smart payment data
Over the past years, Nommon has developed a solution that integrates data from smart payment systems, vehicle location systems and automatic passenger counters and blends them with other data sources, such as trip matrices obtained from mobile location data and access times to public transport stations, to provide transport planning authorities and public transport operators with an accurate, reliable and permanently updated picture of public transport demand, allowing them to plan and adjust services accordingly.