Inferring Modal Split from Mobile Phones: Principles, Issues and Policy Recommendations
This paper describes methods to identify trip details, including the mode of transport for each trip, from smartphone app data and from mobile network data. Use cases include travel demand surveys, travel behaviour gamification, mobility-as-a-service and automated ticketing. In the context of transp...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2021
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| Schriftenreihe: | International Transport Forum Discussion Papers
no.2021/07 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This paper describes methods to identify trip details, including the mode of transport for each trip, from smartphone app data and from mobile network data. Use cases include travel demand surveys, travel behaviour gamification, mobility-as-a-service and automated ticketing. In the context of transport planning, the paper examines solutions to protect privacy and to enhance the representativeness of mobile phone data samples. It makes recommendations to overcome the many obstacles involved, in particular the scarcity of annotated training data. |
| Beschreibung: | 1 Online-Ressource (20 p.) |
| DOI: | 10.1787/c752ca76-en |
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| spelling | Brändle, Norbert VerfasserIn aut Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations Norbert, Brändle Paris OECD Publishing 2021 1 Online-Ressource (20 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier International Transport Forum Discussion Papers no.2021/07 This paper describes methods to identify trip details, including the mode of transport for each trip, from smartphone app data and from mobile network data. Use cases include travel demand surveys, travel behaviour gamification, mobility-as-a-service and automated ticketing. In the context of transport planning, the paper examines solutions to protect privacy and to enhance the representativeness of mobile phone data samples. It makes recommendations to overcome the many obstacles involved, in particular the scarcity of annotated training data. Transport FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/c752ca76-en Volltext |
| spellingShingle | Brändle, Norbert Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations Transport |
| title | Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations |
| title_auth | Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations |
| title_exact_search | Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations |
| title_full | Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations Norbert, Brändle |
| title_fullStr | Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations Norbert, Brändle |
| title_full_unstemmed | Inferring Modal Split from Mobile Phones Principles, Issues and Policy Recommendations Norbert, Brändle |
| title_short | Inferring Modal Split from Mobile Phones |
| title_sort | inferring modal split from mobile phones principles issues and policy recommendations |
| title_sub | Principles, Issues and Policy Recommendations |
| topic | Transport |
| topic_facet | Transport |
| url | https://doi.org/10.1787/c752ca76-en |
| work_keys_str_mv | AT brandlenorbert inferringmodalsplitfrommobilephonesprinciplesissuesandpolicyrecommendations |