ActivityNET: Neural networks to predict trip purposes in public transport from individual smart card data and POIs
Authors/Creators
- 1. PhD Candidate
- 2. Professor
- 3. Teaching fellow
- 4. Post-doc researcher
Description
The data are used for this study as follows: The first one is travel journey data (Oyster card data) labelled by volunteers (start/end time of the activity, location(station name), activity duration, trip purposes), which can be downloaded here https://data.gov.uk/dataset/c5b74d3f-8bf1-443c-8f2d-bd307720737f/underground-stations. The second one is London stations data (London station with coordinates) (https://api-portal.tfl.gov.uk/). And the last one is Foursquare_POIs (POI_ID, user counts, check-ins, categories name(type of activity)) and Foursquare_time (POI_ID, opening/closing hours) (https://developer.foursquare.com/docs/places-api/endpoints/)
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