Aspects concern a method for predicting a path taken by a vehicle for a transport task comprising obtaining training data specifying paths taken, representing a set of valid paths as a Boolean formula, converting the Boolean formula into an OBBD[?] comprising, for each variable of a set of variables on which the Boolean formula operates, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value, augmenting each outgoing edge of each decision node with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements and predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the OBBD[?].

Based on a cost function that estimates a value of a confidence level above a confidence threshold using user information, an autonomous control system can determine that a user of a user device is ready for a pickup, identify a pickup location based on the user information, and determine a trajectory from a current location to the pickup location. A propulsion system is configured to cause motion current location toward the pickup location according to the trajectory.

Based on a cost function that estimates a value of a confidence level above a confidence threshold using user information, an autonomous control system can determine that a user of a user device is ready for a pickup, identify a pickup location based on the user information, and determine a trajectory from a current location to the pickup location. A propulsion system is configured to cause motion current location toward the pickup location according to the trajectory.

A system and method for arranging a transport service is described. A system can receive a request for transport service from a user device. The request can include a requested pickup location data point. The system determines a predetermined location data point associated with the requested pickup location data point. The first predetermined location data point can be different from the requested pickup location data point. The system selects a driver to perform a transport service for the user based, at least in part, on the requested pickup location data point, and transmits the predetermined location data point to a driver device of the selected driver.

Systems and methods are described for generating transactions based upon rideshare data and updating a distributed ledger. The method may include (1) monitoring a distributed ledger for an indication of an accident, the indication of the accident identifying at least a user and a time period; (2) obtaining rideshare data from the distributed ledger, wherein the rideshare data: identifies one or more rideshare companies for which the user offered driving services during the time period, and identifies a phase of a rideshare trip for each rideshare company of the one or more rideshare companies during the time period; (3) obtaining accident data from one or more sensors; and (4) verifying, based upon the rideshare data and the accident data, a controlling party, wherein the controlling party is one of the user or one of the one or more rideshare companies.

Systems and methods of route optimization are disclosed. An adjustment engine receives a plurality of driver-trip pairs each including a driver selected from a plurality of drivers and a trip selected from a plurality of trips and determines a weight for each of the driver-trip pairs. The weight is determined based on at least one driver-trip feature, at least one driver feature, and at least one trip feature. A route optimization engine selects a set of optimal driver-trip pairs based on a bipartite matching process including the weight for each of the driver trip pairs. The set of optimal driver-trip pairs minimizes total estimated time of arrival to a pickup location for each trip in the plurality of trips. Trip data is transmitted to a corresponding device associated with a corresponding driver paired with each trip in the set of optimal driver-trip pairs.

An intermediate system may include: a communication module configured to communicate with a ride-offering system and a delivery system; and at least one processor configured to perform matching on a ride request transmitted to the ride-offering system and a delivery request transmitted to the delivery system, wherein, in response to a delivery request matching the ride request being existent, the at least one processor is configured to change at least one of an origin or a destination of a ride-offering service corresponding to the ride request based on the matching delivery request.

A ride sharing implementation includes a system for logically grouping users. The logical groups may include one or more users designated as riders and one or more users designated as drivers. Notifications for ride requests submitted by or on behalf of riders are transmitted to drivers who can then accept the requests to coordinate transportation of the riders. Example personal monitoring devices and mobile applications for facilitating ride sharing are also provided.

A ride sharing implementation includes a system for logically grouping users. The logical groups may include one or more users designated as riders and one or more users designated as drivers. Notifications for ride requests submitted by or on behalf of riders are transmitted to drivers who can then accept the requests to coordinate transportation of the riders. Example personal monitoring devices and mobile applications for facilitating ride sharing are also provided.

Aspects concern a method for controlling a transport system comprising determining historical data of a multiplicity of transport trips performed by the transport system, training a neural network to perform an invertible mapping of transport trips to latent representations to fulfil, by the distribution of the latent representations of the multiplicity of transport trips, a predetermined fitting criterion with respect to a predetermined latent representation base distribution, sampling a multiplicity of latent representations from the base distribution, mapping each of the sampled latent representations to a respective transport trip by using the trained neural network to perform the inverse of the invertible mapping to generate a multiplicity of synthetic transport trips, determining a control scheme for the transport system using the multiplicity of synthetic transport trips and controlling the transport system using the determined control scheme.