Smart Card Data for Multi-modal Network Planning in London: Five Case Studies

Smart Card Data for Multi-modal Network Planning in London: Five Case Studies


C Seaborn, Halcrow Group, UK; N H M Wilson, J Attanucci, Massachusetts Institute of Technology, US



Smart card fare payment technology is being deployed in support of integrated, efficient and more cost-effective public transport services in many European cities. In London, the 'Oyster' smart card has achieved a penetration rate of about 80 percent of daily trips on the bus and Underground network. The resulting data on Underground entry/exit and bus boarding contains a wealth of information on passenger travel patterns with potential applications ranging from assessing aggregate patronage trends to detailed network planning.

This research, sponsored by Transport for London in association with the Massachusetts Institute of Technology, is the first attempt to apply smart card data to route-level bus network planning in London. Using about 8 million records for a representative day, passenger journey stages are identified by their unique smart card ID as well as bus route or entry/exit station, time stamp and ticket type. Data is manipulated using advanced database management software and bespoke queries.

There are very few examples of the application of smart card data to bus network planning. A key area of value-added information from smart card data is the ability to assess how bus passengers interact with different parts of the network. An integrated network is an essential element of successful public transport systems and additional information on how passengers move across modes can support better facilities design and network planning. In this study, transfer time assumptions based on an analysis of the London network are established for Underground-to-bus, bus-to-Underground and bus-to-bus transfers in order to identify complete journeys (including transfers) for each passenger on a sample day. Aggregate data trends are compared favourably to existing survey data on complete journeys.

The main focus of the proposed paper is five brief case study applications that demonstrate the value-added of smart card data to multi-modal network planning. Modal patterns, transfer times and connectivity with other journey stages are compared across three bus routes and two Underground stations in London.

Results of the case studies show that smart card data can inform multi-modal network planning in a cost-effective and comprehensive way, especially when compared to traditional survey tools. New opportunities related to the Automated Vehicle Location system recently deployed on London?s buses will be discussed.


Association for European Transport