Understanding public transit patterns with open geodemographics to facilitate public transport planning
technology - passenger information, ridership - behaviour, planning - methods
Personalised smart card data, transport planning, latent Dirichlet allocation modelling, travel pattern analysis, geodemographics
Plentiful studies have discussed the potential applications of contactless smart card from understanding interchange patterns to transit network analysis and user classifications. However, the incomplete and anonymous nature of the smart card data inherently limit the interpretations and understanding of the findings, which further limit planning implementations. Geodemographics, as ‘an analysis of people by where they live’, can be utilised as a promising supplement to provide contextual information to transport planning. This paper develops a methodological framework that conjointly integrates personalised smart card data with open geodemographics so as to pursue a better understanding of the traveller’s behaviours. It adopts a text mining technology, latent Dirichlet allocation modelling, to extract the transit patterns from the personalised smart card data and then use the open geodemographics derived from census data to enhance the interpretation of the patterns. Moreover, it presents night tube as an example to illustrate its potential usefulness in public transport planning.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Liu, Y., & Cheng, T. (2020). Understanding public transit patterns with open geodemographics to facilitate public transport planning. Transportmetrica A: Transport Science, Vol. 16, pp. 76-103.