In one embodiment, a system can be used for providing a taxi service. The system includes an integrated terminal configured to communicate with an operating server that provides a response to a request for a service received from a user terminal through a network. The integrated terminal is configured to determine whether to identify vehicle distribution call information by using an operation state of a vehicle and information acquired by associating a boarding location of a user and heading direction information of the vehicle, calculate an operation fee by using GPS information or wheel speed information of the vehicle, and determine whether an output screen of the vehicle is changed based on gear state information, wheel speed information, or information received from an electronic control unit of the vehicle.

In one embodiment, a system can be used for providing a taxi service. The system includes an integrated terminal configured to communicate with an operating server that provides a response to a request for a service received from a user terminal through a network. The integrated terminal is configured to determine whether to identify vehicle distribution call information by using an operation state of a vehicle and information acquired by associating a boarding location of a user and heading direction information of the vehicle, calculate an operation fee by using GPS information or wheel speed information of the vehicle, and determine whether an output screen of the vehicle is changed based on gear state information, wheel speed information, or information received from an electronic control unit of the vehicle.

This disclosure describes one or more embodiments of methods, non-transitory computer-readable media, and systems that utilize computing models to intelligently match provider devices and requester devices in response to context-aware transportation requests. In particular, in one or more embodiments, the disclosed systems utilize a contextual characterization model to identify contextual characteristics of a requester associated with a transportation matching system and provide the requester a selectable option to prioritize transportation matches with providers sharing the identified contextual characteristics. In response to detecting interaction with the selectable option, the disclosed systems can receive and generate contextual transportation matches between the requester device and provider devices associated with providers sharing the identified contextual characteristics.

This disclosure describes one or more embodiments of methods, non-transitory computer-readable media, and systems that utilize computing models to intelligently match provider devices and requester devices in response to context-aware transportation requests. In particular, in one or more embodiments, the disclosed systems utilize a contextual characterization model to identify contextual characteristics of a requester associated with a transportation matching system and provide the requester a selectable option to prioritize transportation matches with providers sharing the identified contextual characteristics. In response to detecting interaction with the selectable option, the disclosed systems can receive and generate contextual transportation matches between the requester device and provider devices associated with providers sharing the identified contextual characteristics.

Disclosed are embodiments for facilitating autonomous vehicle (AV) pullover clustering prevention. In some aspects, an embodiment includes receiving identification of an origin location and a destination location corresponding to a transportation trip request for an AV; determining a set of pullover locations comprising pickup locations for the origin location and drop-off locations for the destination location; for each pullover location of the set of pullover locations: determining an estimated time of arrival (ETA) time window for the AV at the pullover location; determining a number of other AVs expected to be at the pullover location during the ETA time window; and responsive to the number of other AVs expected to be at the pullover location during the ETA time window exceeding an AV pullover crowding metric, removing the pullover location from the set of pullover locations to produce a revised set of pullover locations.

Disclosed are embodiments for facilitating autonomous vehicle (AV) pullover clustering prevention. In some aspects, an embodiment includes receiving identification of an origin location and a destination location corresponding to a transportation trip request for an AV; determining a set of pullover locations comprising pickup locations for the origin location and drop-off locations for the destination location; for each pullover location of the set of pullover locations: determining an estimated time of arrival (ETA) time window for the AV at the pullover location; determining a number of other AVs expected to be at the pullover location during the ETA time window; and responsive to the number of other AVs expected to be at the pullover location during the ETA time window exceeding an AV pullover crowding metric, removing the pullover location from the set of pullover locations to produce a revised set of pullover locations.

A method for managing a hive-based carpool system may comprise establishing a plurality of households and a hive representing a communication channel for a group of users. The hive may include at least one ride recipient user and all ride provider users in the same household. The method may involve affiliating ride provider users with ride recipient users, receiving a ride request from a user in the hive, transmitting the request to authorized drivers, receiving an acceptance, tracking the driver's location during the ride, and updating ride status in real-time for hive members. The communication channel may enable real-time chat functionality and restrict membership to individuals authorized to provide rides to ride recipients.

A method for managing a hive-based carpool system may comprise establishing a plurality of households and a hive representing a communication channel for a group of users. The hive may include at least one ride recipient user and all ride provider users in the same household. The method may involve affiliating ride provider users with ride recipient users, receiving a ride request from a user in the hive, transmitting the request to authorized drivers, receiving an acceptance, tracking the driver's location during the ride, and updating ride status in real-time for hive members. The communication channel may enable real-time chat functionality and restrict membership to individuals authorized to provide rides to ride recipients.

A network system operates to receive, over one or more networks, a transport request from a computing device of a service requester. The network system generates and stores, in a data store, a job order for the transport request. Further, the job order is assigned to a first service provider based on a current location of the first service provider, where the current location is determined based on location information received from a computing device of the first service provider. The network system monitors an activity of the first service provider during a time interval that follows when the first service provider is assigned to the job order. Based on the monitored activity, the network system determines an intent of the first service provider and based on the determination, reassigns the job order.

A network system operates to receive, over one or more networks, a transport request from a computing device of a service requester. The network system generates and stores, in a data store, a job order for the transport request. Further, the job order is assigned to a first service provider based on a current location of the first service provider, where the current location is determined based on location information received from a computing device of the first service provider. The network system monitors an activity of the first service provider during a time interval that follows when the first service provider is assigned to the job order. Based on the monitored activity, the network system determines an intent of the first service provider and based on the determination, reassigns the job order.