Abstract: A ride-sharing system can automatically and autonomously determine whether a rider is an account holder associated with an account of a ride-sharing service that requested a ride or a guest rider. A guest rider detection system can use geolocation data to determine whether a rider is a guest rider or an account holder. The system can obtain location data for an account holder that requests a ride using a ride sharing application. The location data of the account holder can be compared with one or more locations (e.g., pickup or drop off location) associated with the requested ride. Based at least in part on the comparisons between the location data of the account holder and the one or more locations associated with the requested ride, the system can determine with a particular degree of certainty whether the rider is the guest rider or the account holder.

Abstract: A ride-sharing system can automatically and autonomously determine whether a rider is an account holder associated with an account of a ride-sharing service that requested a ride or a guest rider. A guest rider detection system can use geolocation data to determine whether a rider is a guest rider or an account holder. The system can obtain location data for an account holder that requests a ride using a ride sharing application. The location data of the account holder can be compared with one or more locations (e.g., pickup or drop off location) associated with the requested ride. Based at least in part on the comparisons between the location data of the account holder and the one or more locations associated with the requested ride, the system can determine with a particular degree of certainty whether the rider is the guest rider or the account holder.

Abstract: A ride-sharing system can automatically and autonomously determine whether a rider is an account holder associated with an account of a ride-sharing service that requested a ride or a guest rider. A guest rider detection system can use geolocation data to determine whether a rider is a guest rider or an account holder. The system can obtain location data for an account holder that requests a ride using a ride sharing application. The location data of the account holder can be compared with one or more locations (e.g., pickup or drop off location) associated with the requested ride. Based at least in part on the comparisons between the location data of the account holder and the one or more locations associated with the requested ride, the system can determine with a particular degree of certainty whether the rider is the guest rider or the account holder.

Abstract: Embodiments of the disclosure provide systems and methods for blocking a ride-hailing order. The method may include receiving incident data associated with a plurality of incidents. The incident data indicates a time and a location of each incident. The method may also include determining, by a processor, an area encompassing the locations of the plurality of incidents. The method may further include determining, by the processor, a plurality of time intervals each encompassing the time of at least one incident. The method may further include determining, by the processor, a time slot by merging the plurality of time intervals based on a time gap between every two neighboring time intervals. The method may further include designating, by the processor, a combination of the area and the time slot as a block zone and blocking an order generated from the area during the time slot of the block zone.

Abstract: Embodiments of the disclosure provide systems and methods for processing a transportation service request. An exemplary system may include a communication interface configured to receive the transportation service request from a terminal device. The system may further include at least one processor. The at least one processor may be configured to generate a passenger score based on the received transportation service request using a first machine learning model trained with sample passenger data associated with past impacted drivers. The at least one processor may further be configured to generate a trip score based on the generated passenger score and the received transportation service request using a second machine learning model trained with sample trip data associated with the past impacted drivers. The at least one processor may also be configured to allow or block the received transportation service request based on the generated trip score.