Abstract: Various approaches allow for shared transportation reservations and related actions to be made based on a user's proximity to a shared transportation station. Data is received concerning the user's computing device, such as a vehicle, a mobile device, an internet-connected device, or a cellular network-connected device. Approaches can use a variety of data formats, including image data, location coordinates, or wirelessly-transmitted data. Based on such data and a criterion such as a threshold proximity to the station, the user's account status is determined and access to same authorized. Via the account, numerous actions may be taken, including automatically triggering a transport mode reservation or return, prompting the user to reserve or return the shared transportation, or debiting the user per a fee associated with the transportation reservation. Further, neural network techniques may be applied to train a model for generating related recommendations.

Abstract: An intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability indications based on historical data and user proximity. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may include how many bicycles are checked at out a given time and how many remain at a given location. In some embodiments, an indication may be provided to the user as the user approaches the vehicle sharing system. The indication may provide information regarding the availability of bicycles.

Abstract: Various approaches allow for shared transportation reservations and related actions to be made based on a user's proximity to a shared transportation station. Data is received concerning the user's computing device, such as a vehicle, a mobile device, an internet-connected device, or a cellular network-connected device. Approaches can use a variety of data formats, including image data, location coordinates, or wirelessly-transmitted data. Based on such data and a criterion such as a threshold proximity to the station, the user's account status is determined and access to same authorized. Via the account, numerous actions may be taken, including automatically triggering a transport mode reservation or return, prompting the user to reserve or return the shared transportation, or debiting the user per a fee associated with the transportation reservation. Further, neural network techniques may be applied to train a model for generating related recommendations.

Abstract: An intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability indications based on historical data and user proximity. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may include how many bicycles are checked at out a given time and how many remain at a given location. In some embodiments, an indication may be provided to the user as the user approaches the vehicle sharing system. The indication may provide information regarding the availability of bicycles.

Abstract: An intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability predictions based on historical data, including various utilization statistics. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may include the number of available bicycles at various locations and times, as well as contextual data associated with the locations and times. Contextual data may include data regarding the weather, local events, season, day of the week or year, news events, among other environmental factors that may or may not influence bicycle utilization. In some embodiments, a model, such as a machine learning model (e.g., neural network) may be trained using the historical data as training data such that the model can predict bicycle availability for a certain future time and location.