Abstract: The present application provides a touch sensing assembly and a touch sensing display device. The touch sensing assembly includes a touch sensing layer. By disposing the first connecting branch electrode between the second branch electrode and the second main electrode, transmission channels of current in the second direction in each of the touch sensing units is increased, thereby reducing the impedance of the second touch electrode in each touch unit. In addition, each of the second branch electrodes surrounds one of the first branch electrodes to increase the mutual capacitance between the first branch electrode and the second branch electrode.

Abstract: A touch control component and a touch control display device are provided. The present disclosure can improve touch control sensitivity of the touch control component by at least one second branch electrode surrounding at least one first branch electrode corresponding thereto, by at least one third branch electrode adjacent to the at least one first branch electrode surrounding the at least one second branch electrode positioned between the at least one first branch electrode and the at least one third branch electrode, and by two adjacent third branch electrodes respectively positioned in two of touch control units adjacent to each other in a first direction being connected to each other on one end away from the third branch electrodes connected to a first main stem electrode.

Abstract: A system and method of determining a policy to prevent fading drivers is described. The system and method creates virtual trajectories of incentives such as coupons offered to drivers in a transportation hailing system and corresponding states of drivers in response to the incentives. A joint policy simulator is created from an incentive policy, a confounding incentive policy, and an incentive object policy to generate the simulated actions of drivers in response to different incentives. The rewards of the simulated actions of the drivers is determined by a discriminator. The incentive policy for preventing fading drivers is optimized by reinforcement learning based on the virtual trajectories generated by the joint policy simulator and discriminator.

Abstract: Systems and methods are provided for ride order dispatching. Such method may comprise obtaining information on a location of a vehicle and a time to input into a trained neural network algorithm; and based on a policy generated from the trained neural network algorithm, obtaining action information for the vehicle, the action information comprising: staying at a current position of the vehicle, re-positioning the vehicle, or accepting a ride order.

Abstract: A system and method to optimize the distribution of incentives for a transportation hailing service is disclosed. A database stores state data and action data received from a client devices and transportation devices. The state data is associated with the utilization of the transportation hailing service and the action data is associated with different incentives to passengers to engage the transportation hailing service. A Q-value determination engine is trained to determine rewards associated with incentive actions from a set of virtual trajectories of states, incentive actions, and rewards, based on a history of the action data and associated state data from the database. Passengers are ordered according to V-values. An incentive policy including selected incentive actions based on the determined rewards and passengers is created. An incentive server communicates selected incentives to at least some of the client devices according to the determined incentive policy.

Abstract: Systems configured to dispatch transportation resources and related methods are described. The system including one or more digital devices configured to receive a request for a price for a transportation to a destination; receive destination information; and receive origin information. The system configured to in response to the request for the price, generate a price quote based on a price strategy and a dispatch strategy. The system configured to in response to the generated price quote, generate a response to the request for the price. And, the system configured to transmit the price quote over a network.

Abstract: A system and method of determining a policy to prevent fading drivers is described. The system and method creates virtual trajectories of incentives such as coupons offered to drivers in a transportation hailing system and corresponding states of drivers in response to the incentives. A joint policy simulator is created from an incentive policy, a confounding incentive policy, and an incentive object policy to generate the simulated actions of drivers in response to different incentives. The rewards of the simulated actions of the drivers is determined by a discriminator. The incentive policy for preventing fading drivers is optimized by reinforcement learning based on the virtual trajectories generated by the joint policy simulator and discriminator.

Abstract: A system for evaluating order dispatching policy includes a first computing device, at least one processor, and a memory. The first computing device is configured to generate historical driver data associated with a driver. The at least one processor is configured to store instructions. When executed by the at least one processor, the instructions cause the at least one processor to perform operations. The operations performed by the at least one processor includes obtaining the generated historical driver data associated with the driver. Based at least in part on the obtained historical driver data, a value function is estimated. The value function is associated with a plurality of order dispatching policies. An optimal order dispatching policy is then determined. The optimal order dispatching policy is associated with an estimated maximum value of the value function.

Abstract: A system for evaluating order dispatching policy includes a first computing device, at least one processor, and a memory. The first computing device is configured to generate historical driver data associated with a driver. The at least one processor is configured to store instructions. When executed by the at least one processor, the instructions cause the at least one processor to perform operations. The operations performed by the at least one processor includes obtaining the generated historical driver data associated with the driver. Based at least in part on the obtained historical driver data, a value function is estimated. The value function is associated with a plurality of order dispatching policies. An optimal order dispatching policy is then determined. The optimal order dispatching policy is associated with an estimated maximum value of the value function. The estimation of the value function applies a cerebellar model arithmetic controller.

Abstract: Deep reinforcement learning may be used for vehicle repositioning on mobility-on-demand platforms. Information may be obtained. The information may include a current location of a vehicle on a ride-sharing platform. A set of paths originated from the current location of the vehicle may be obtained. Each of the set of paths may have a length less than a preset maximum path length. A set of expected cumulative rewards along the set of paths may be obtained based on a trained deep value-network. A best path from the set of paths may be selected based on a heuristic tree search of the set of expected cumulative rewards. A next step along the best path may be recommended as a reposition action for the vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing a virtual environment for a ride-hailing platform are disclosed.

Abstract: The present disclosure provides a method, an apparatus, an electronic device, and a computer-readable storage medium for distributing orders. The method may include obtaining attribute information of a service provider and order information of associated orders received by the service provider; obtaining a degree of association between the service provider and each of the associated orders by inputting the attribute information and the order information of the associated orders into an order distribution strategy network; and determining a distribution order for the service provider based on the degrees of association. According to the embodiments of the present disclosure, response rates of the service provider to the orders may be improved.

Abstract: A method comprises: obtaining historical vehicle service data in an area for a time period including historical locations of passenger-seeking vehicles with respect to time, historical locations of passenger orders with respect to time, and historical trip fares with respect to pick-up locations and time; discretizing the area into a plurality of zones and discretizing the time period into a plurality of time segments; aggregating the historical vehicle service data according the zones and time segments; obtaining an expected reward for a passenger-seeking vehicle to move from zone A to each neighboring zone of the zone A based on the aggregated historical vehicle service data; and obtaining a probability of a passenger-seeking vehicle moving from zone A to a neighboring zone B based on the expected reward for the passenger-seeking vehicle to move from zone A to the each neighboring zone of the zone A.

Abstract: Systems and methods are provided for ride order dispatching. Such method may comprise obtaining information on a location of a vehicle and a time to input into a trained neural network algorithm; and based on a policy generated from the trained neural network algorithm, obtaining action information for the vehicle, the action information comprising: staying at a current position of the vehicle, re-positioning the vehicle, or accepting a ride order.

Abstract: The present disclosure relates to systems and methods for determining an estimated time of arrival (ETA) for a route, based on a model of ETA. The systems may perform the methods to obtain a first electrical signal associated with at least one route having at least one road section; generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal; generate second structured data of a model of ETA by training the model based on the at least one global feature vector and the at least one historical duration; and save the second structured data of the model of ETA in the at least one non-transitory computer-readable storage medium.

Abstract: A method comprises: obtaining historical vehicle service data in an area for a time period including historical locations of passenger-seeking vehicles with respect to time, historical locations of passenger orders with respect to time, and historical trip fares with respect to pick-up locations and time; discretizing the area into a plurality of zones and discretizing the time period into a plurality of time segments; aggregating the historical vehicle service data according the zones and time segments; obtaining an expected reward for a passenger-seeking vehicle to move from zone A to each neighboring zone of the zone A based on the aggregated historical vehicle service data; and obtaining a probability of a passenger-seeking vehicle moving from zone A to a neighboring zone B based on the expected reward for the passenger-seeking vehicle to move from zone A to the each neighboring zone of the zone A.

Abstract: A method for operating a ride-share-enabled vehicle includes determining a target location of the ride-share-enabled vehicle, determining a ride-sharing policy algorithm to determine a behavior of the ride-share-enabled vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, if any, based on the determined target location of the ride-share-enabled vehicle, determining a behavior of the ride-share-enabled vehicle based on a current location of the ride-share-enabled vehicle and the determined ride-sharing policy algorithm, and causing the ride-share-enabled vehicle to be operated according to the determined behavior of the ride-share-enabled vehicle.

Abstract: The present disclosure relates to systems and methods for determining an estimated time of arrival (ETA) for a route, based on a model of ETA. The systems may perform the methods to obtain a first electrical signal associated with at least one route having at least one road section; generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal; generate second structured data of a model of ETA by training the model based on the at least one global feature vector and the at least one historical duration; and save the second structured data of the model of ETA in the at least one non-transitory computer-readable storage medium.

Abstract: The present disclosure relates to a method and system for estimating an estimated time of arrival for an intended trip. The system includes at least one storage medium configured to store a machine learning model trained using historical trip data. The system further includes a processing engine configured to receive road-related data associated with an intended route of the intended trip. The intended route includes a plurality of road sections. The processing engine is further configured to determine global features of the intended route based on the road-related data. At least one global feature reflects a relationship between at least two road sections among the plurality of road sections. The processing engine is also configured to determine the estimated time of arrival based on the machine learning model and the global features of the intended route and provide the estimated time of arrival for display.

Abstract: The present disclosure relates to systems and methods for determining an estimated time of arrival (ETA) for a route, based on a model of ETA. The systems may perform the methods to obtain a first electrical signal associated with at least one route having at least one road section; generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal; generate second structured data of a model of ETA by training the model based on the at least one global feature vector and the at least one historical duration; and save the second structured data of the model of ETA in the at least one non-transitory computer-readable storage medium.