Abstract: Techniques for automatically detecting when a ride requester has requested a ride-share ride on behalf of a guest rider using some or all of the communications between the driver and ride requester are described herein. For example, a server can obtain chat logs between a ride requester and a driver and process the chat logs to identify whether the ride requester has requested a ride on behalf of a guest rider. In particular, the server can train an artificial intelligence model (e.g., a machine learning model) to predict potential guest rider behavior. Once trained, the server can obtain chat logs comprising chat messages sent between a driver and a ride requester, and apply a representation of the chat logs as an input to the trained artificial intelligence model to determine whether guest rider behavior is detected.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a first trip order and a trip whitelist. The first trip order comprises a first passenger and first points of interest (POIs). In response to either the first POIs or the first passenger being in the trip whitelist, dispatching a driver to the first trip order. In response to neither the first POIs nor the first passenger being in the trip whitelist, a determination is made whether to add the first POIs or the first passenger to the trip whitelist. In response to a determination not to add the first POIs or the first passenger to the trip whitelist, a trip risk score is determined for the first trip order based on a trip-risk evaluation machine learning model, and a driver is dispatched to the first trip order based on the trip risk score.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a trip order comprising a driver assigned to the trip order, a passenger of the trip order, and information about the trip order. Driver features associated with the driver, passenger features associated with the passenger, and trip order features extracted from the information about the trip order can be obtained. A driver-score is determined by a driver-evaluation machine learning model based on the driver features. A risk score for the trip order is determined using a risk-evaluation machine learning model based on the driver-score, the passenger features, and the trip order features. An alert notification is sent to a computing device of the driver based on the risk score.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a trip order and a driver pool, the driver pool comprising a plurality of drivers. A trip risk category selected among a plurality of trip risk categories can be assigned to the trip order. One or more dispatch rules learned from a trained dispatch machine learning model can be obtained. Based on the one or more dispatch rules, the driver pool can be filtered to obtain a qualified driver pool for the trip order. The qualified driver pool is fed to a dispatch engine which assigns a driver in the qualified driver pool to the trip order.

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.

Abstract: Embodiments of the disclosure provide systems and methods for reviewing performance of computer models for safety analysis in transportation services. The exemplary system includes a communication interface configured to receive log data associated with at least one reported safety event. The log data includes one or more computer models used for predicting the reported safety event and associated with a first feature pattern and a first model performance. The system further includes at least one processor configured to extract features from the log data and determine a second feature pattern and a second model performance of the computer models. The at least one processor is also configured to detect a change in feature pattern based on the first feature pattern and the second feature pattern or a performance degradation based on the first model performance and the second model performance, and to generate an alert to upgrade the computer models.

Abstract: Mismatched drivers may be identified based on passenger evaluations. A set of evaluations may be obtained. Each evaluation in the set of evaluations may be based on whether a current driver matches a registered driver. A convergence point number may be obtained. The convergence point number may be a number of evaluations required for accurate identification. An identification rate may be computed based on the set of evaluations and the convergence point number. The current driver may be verified based on a comparison of the identification rate to a threshold match rate. If the identification rate is greater than a threshold match rate, it may be determined that the identity of the current driver is valid. If the identification rate is not greater than a threshold match rate, the current driver may be identified as a mismatched driver.

Abstract: Negative driver behaviors may be captured based on passenger comments. A set of comments from a set of first users may be obtained. A set of preprocessed words may be generated based on the set of comments. A numerical vector may be generated based on the set of words. A sparse matrix may be generated based on the numerical vector. The sparse matrix may be input into a trained model. A second user may be classified based on an output of the trained model.

Abstract: Mismatched drivers may be identified based on passenger evaluations. A set of evaluations may be obtained. Each evaluation in the set of evaluations may be based on whether a current driver matches a registered driver. A convergence point number may be obtained. The convergence point number may be a number of evaluations required for accurate identification. An identification rate may be computed based on the set of evaluations and the convergence point number. The current driver may be verified based on a comparison of the identification rate to a threshold match rate. If the identification rate is greater than a threshold match rate, it may be determined that the identity of the current driver is valid. If the identification rate is not greater than a threshold match rate, the current driver may be identified as a mismatched driver.

Abstract: After obtaining a dictionary comprising mappings of names to genders generated from frequencies of genders of the names, a name (e.g., a user name, a name, or a middle name) of a passenger can be received and a gender of the passenger can be determined. The gender of the passenger can be determined based on: (i) the gender of a mapping of the name of the passenger to a gender, or (ii) the frequencies of the genders for one or more names within a threshold distance of the name of the passenger.