Abstract: An approach is provided for providing an estimated time of arrival (ETA) with a uncertain starting location. The approach, for example, involves determining an uncertainty time window that spans from a timestamp of a location point of a sparse location data feed to a time of interest. The approach also involves determining a speed of the device at the location point based on the location data feed. The approach further involves processing map data based on the speed to predict possible locations to which the device may have traveled during the uncertainty time window and to determine one or more respective probabilities of the device has traveled to the possible locations. The approach further involves determining respective ETA at a destination from the possible locations. The approach further involves calculating a total estimated time of arrival based on the respective estimated times of arrival and the respective probabilities.

Abstract: An approach is provided for performing ride-sharing functions based on joint motion using multiple sensor data. The approach, for example, involves retrieving a joint motion prediction indicating whether at least two devices are traveling in a same transportation vehicle. The joint motion prediction is computed based on sensor data collected from the at least two devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves initiating a ride-sharing function for respective users of the at least two devices based on the joint motion prediction.

Abstract: An approach is provided for detecting joint motion using multiple sensor data. The approach, for example, involves retrieving sensor data at least two devices. The sensor data, for instance, is collected using at least one sensor type from among a plurality of sensor types and wherein, and each sensor type of the plurality of sensor types is associated with a respective joint motion classifier. The approach also involves processing the sensor data using the respective joint motion classifier for said each sensor type of the least one sensor type to compute a respective sensor-type joint motion prediction. The approach further involves processing the respective sensor-type joint motion prediction for said each sensor type using a unified classifier to compute a unified joint motion prediction for the at least two devices. The approach further involves providing the unified joint motion prediction as an output.

Abstract: An approach is provided for performing social-networking functions based on joint motion using multiple sensor data. The approach, for example, involves determining a co-ride event between at least two users based on a joint motion prediction computed using sensor data collected from respective devices associated with the at least two users. The joint motion prediction is computed based on sensor data collected from the respective devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves determining a latent social network between the at least two users based on the co-ride event. The approach further involves providing the latent social network as an output.

Abstract: An approach is provided for performing ride-sharing functions based on joint motion using multiple sensor data. The approach, for example, involves retrieving a joint motion prediction indicating whether at least two devices are traveling in a same transportation vehicle. The joint motion prediction is computed based on sensor data collected from the at least two devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves initiating a ride-sharing function for respective users of the at least two devices based on the joint motion prediction.

Abstract: An approach is provided for detecting joint motion using multiple sensor data. The approach, for example, involves retrieving sensor data at least two devices. The sensor data, for instance, is collected using at least one sensor type from among a plurality of sensor types and wherein, and each sensor type of the plurality of sensor types is associated with a respective joint motion classifier. The approach also involves processing the sensor data using the respective joint motion classifier for said each sensor type of the least one sensor type to compute a respective sensor-type joint motion prediction. The approach further involves processing the respective sensor-type joint motion prediction for said each sensor type using a unified classifier to compute a unified joint motion prediction for the at least two devices. The approach further involves providing the unified joint motion prediction as an output.

Abstract: An approach is provided for detecting and classifying points of interest based on joint motion using multiple sensor data. The approach, for example, involves determining co-ride data for at least two users based on a joint motion prediction computed using sensor data collected from respective devices associated with the at least two users. The joint motion prediction is computed based on sensor data collected from the respective devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves processing the co-ride data to perform a detection, a classification, or a combination thereof of one or more locations associated with the joint motion prediction.