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Publication Detail
Forecasting Ridership to Plan New Public Transport by Using Smartphone Data
  • Publication Type:
    Conference presentation
  • Publication Sub Type:
  • Authors:
    Cheung Y, Cheng T
  • Name of Conference:
    The second International Conference on Urban Informatics (ICUI 2019)
  • Conference place:
    Hong Kong
  • Conference start date:
  • Conference finish date:
The estimation of ridership to plan public transport systems usually requires large-scale travel surveys. This research proposes a cost-effective methodology to estimate ridership for a developing tube line using smartphone GPS data. It models the entry and exit ridership during weekday morning peak at 10 stations of the central section of the Elizabeth line in London as a case study. To estimate the ridership, this study firstly extracts the travel patterns of potential users of the line by developing an origin-destination (OD) matrix of 10 stations. Then, the probability of shifting from existing travel methods to the Elizabeth line during morning peak hours are estimated by considering the population at work age and the share of public transport modes. The estimated results are compared with the current ridership of London Underground stations. The patterns of the forecasted entry and exit ridership are generally consistent to the Underground stations, though insufficient reference dataset and biases affect the patterns in some stations with the National Rail service. Overall, the research has developed an efficient approach for travel demand modelling in a scale 15 times larger than traditional travel surveys, showing the great potential of using GPS data in travel demand modelling, which can be further applied to different scenarios for transportation management use.
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