Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Establishing the context of users of mobile applications is essential to provide the users with relevant information and functionality associated with the user's location or situation. Context aware solutions adapt its behaviour to fit the situation the user is situated in by making certain information or functions available. They are applicable to most kinds of modern systems; public transportation systems are no exception. To achieve intelligent transportation, it is vital to determine contextual information related to travelers, such as which vehicle is currently used and where the travelers boarded or disembarked. This contributes towards more seamless public transport ticketing and provides public transport operators with enriched data. In this paper, we suggest an approach, using machine learning, to determine a traveler's mode of transport using mobile sensor data from the traveler's smartphone. The trained machine learning models can infer the mode of transport with high accuracy using off-the-shelf technology.
Recommended Citation
Skretting, Anders; Grønli, Tor-Morten; Majchrzak, Tim A.; Mateos, Cristian; and Hirsch, Matias, "Enhanced Transport Mode Recognition on Mobile Devices" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 6.
https://aisel.aisnet.org/hicss-57/st/sw_development/6
Enhanced Transport Mode Recognition on Mobile Devices
Hilton Hawaiian Village, Honolulu, Hawaii
Establishing the context of users of mobile applications is essential to provide the users with relevant information and functionality associated with the user's location or situation. Context aware solutions adapt its behaviour to fit the situation the user is situated in by making certain information or functions available. They are applicable to most kinds of modern systems; public transportation systems are no exception. To achieve intelligent transportation, it is vital to determine contextual information related to travelers, such as which vehicle is currently used and where the travelers boarded or disembarked. This contributes towards more seamless public transport ticketing and provides public transport operators with enriched data. In this paper, we suggest an approach, using machine learning, to determine a traveler's mode of transport using mobile sensor data from the traveler's smartphone. The trained machine learning models can infer the mode of transport with high accuracy using off-the-shelf technology.
https://aisel.aisnet.org/hicss-57/st/sw_development/6