Abstract: Embodiments for implementing intelligent risk detection and mitigation in a transport network by a processor. Data gathered from a plurality of data sources relating to an entity and a selected region of interest may be analyzed. Behavior of an entity, in relation to a risk event, may be learned and interpreted based on a plurality of identified contextual factors, geographical data, current data, historical data, a learned risk event model, or a combination thereof. One or more mitigation actions may be performed to mitigate risk of occurrence or a possible negative impact of the risk event caused at least in part by the behavior of the entity.

Abstract: Embodiments for deep learning for partial differential equation (PDE)-based models by a processor. A trained forecasting model and consistency constraints may be generated using a PDE-based model, a discretization of the PDE-based model, historical inputs the of the PDE-based model, and a representation of consistency constraints to generate a predictive output.

Abstract: Embodiments for establishing trust in commerce negotiations are provided. Information associated with negotiation actions of each of a plurality of entities is stored in a shared ledger. A trustworthiness score is calculated for at least one of the plurality of entities based on the information associated with the negotiation actions of the at least one of the plurality of entities. The calculated trustworthiness score is provided to at least one other of the plurality of entities.

Abstract: Distributing and rebalancing crewless vehicles (CV) by receiving a CV demand-availability gap, identifying candidate CV to relocate to close the CV demand-availability gap, identifying a candidate transport to relocate the CV to close the CV demand-availability gap, generating a candidate CV relocation plan, optimizing an overall CV relocation plan, and executing the overall CV relocation plan.

Abstract: Embodiments for providing intelligent transportation service management in a transportation system by a processor. Transportation service requests may be assigned amongst multiple transportation service providers according to one or more transportation service request distribution models and various parameters and preferences for each user. The transportation service request distribution models may protect information relation to each of the transportation service providers and suggests a selected order for distributing the plurality of transportation service requests.

Abstract: Generating parking area statistics for identifying parking areas within a geographic region. Navigation support for reaching a top-ranked parking area according to collected parking area data, user preferences, parking feedback, and real-time traffic conditions. Selection of a top-ranked parking area and monitoring progress during a parking event improve future identification and ranking of parking areas.

Abstract: Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

Abstract: A computer-implemented method, a computer program product, and a computer system for optimizing vehicle mobility, communication networks, and required computing resources for a connected vehicle. A computer applies user-defined settings to configure associated optimization algorithms. The computer aggregates data structures related to traveling distances, mobility metrics, and expected levels of Quality of Service (QoS) for one or more applications in a connected vehicle. The computer calculates an optimal route, given imposed constraints including points of interest and QoS requirements of the one or more applications. The computer prepares expected QoS of the one or more applications, recommended configurations of the one or more applications, and recommended configurations of one or more networks along the optimal route.

Abstract: Embodiments for implementing an intelligent health recommendation service by a processor. A health state of a user may be learned according to user behavior, physical conditions of the user, activities of daily living (ADL) and associated context of daily living (CDL) of the user, or a combination thereof. One or more applications, one or more application components, one or more internet of things (IoT) devices, or a combination thereof may be identified and recommended to minimize one or more possible negative impacts upon the health state of the user.

Abstract: In an approach for determining the illumination necessary along a vehicle's planned trajectory, a processor receives information relevant to the trajectory of a vehicle. A processor estimates the position of the vehicle at the particular time on the trajectory of the vehicle, using the data from the sensors. A processor estimates an illumination of the trajectory of the vehicle, using the model of the illumination of the road network and the data from the sensors, wherein a threshold is employed. A processor determines the threshold is exceeded. A processor changes a setting of the at least one headlight on the vehicle. A processor determines the threshold is exceeded. A processor alerts the user of the vehicle when adjustments to the at least one headlight that are necessary to illuminate the trajectory of the vehicle exceed physical limitations of the at least one headlight.

Abstract: A system and method for cognitive journey monitoring are presented. Embodiments comprise journey prediction, parsing of data sources, risk assessment and mitigation, and natural-language user interaction by a cognitive processor. Data is gathered from a plurality of data sources and analyzed in the context of one or more of the user's intention(s). A dialogue with the user, in natural language, aims to provide and select one or more suggestions relating to the one or more user intention(s) such that the risk(s) relating to the one or more user's intention(s) is reduced. During the dialogue, cognitive reasoning may be performed, wherein the cognitive reasoning includes the ability to justify each suggestion and the ability to infer information from the interaction such as, for example, data obtained in a dialogue may inform subsequent inferences. The embodiments may use speech synthesis and speech recognition in an interactive spoken dialogue.

Abstract: Generating parking area statistics for identifying parking areas within a geographic region. Navigation support for reaching a top-ranked parking area according to collected parking area data, user preferences, parking feedback, and real-time traffic conditions. Selection of a top-ranked parking area and monitoring progress during a parking event improve future identification and ranking of parking areas.

Abstract: In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

Abstract: Embodiments for planning vehicular driving actions in the presence of non-recurrent events by a processor. One or more dynamics of non-recurrent events in a transport network may be learned according to one or more contextual factors. One or more vehicle-specific factors may be learned in relation to a historical journey and current journey of a vehicle. One or more action responses associated with the one or more non-recurrent events and the one or more vehicle-specific factors may be generated.

Abstract: Embodiments for relationship graph expansion and extraction from a collection of unstructured text data by a processor. A query relating to one or more concepts may be received. The query may be expanded according to a logical reasoning operation and a domain ontology having a set of logical rules. A relationship graph between one or more concepts from a plurality of unstructured text data may be extracted based on an expanded query according to a domain ontology and the set of logical rules.

Abstract: Various embodiments are provided for planning computational unit (e.g., virtual machine (VM)) migration based on vehicle mobility prediction in a computing environment by a processor. A computational unit handover operation may be dynamically planned associated with a vehicle using one or more predicted routes according to global positioning satellite (“GPS”) data, contextual data, historical data, a maximum latency requirement for the vehicle, one or more load balancing requirements for an edge computing server.

Abstract: Distributing and rebalancing crewless vehicles (CV) by receiving a CV demand—availability gap, identifying candidate CV to relocate to close the CV demand—availability gap, identifying a candidate transport to relocate the CV to close the CV demand—availability gap, generating a candidate CV relocation plan, optimizing an overall CV relocation plan, and executing the overall CV relocation plan.

Abstract: Embodiments for utilizing vehicles to investigate events are provided. Spatiotemporal information is received from each of a plurality of vehicles. Each of the plurality of vehicles includes an onboard sensor. Information associated with an event is received. At least some of the plurality of vehicles are selected based on the information associated with the event and the spatiotemporal information from each of a plurality of vehicles. A request is caused to be transmitted to each of the selected at least some of the plurality of vehicles for data detected by the respective onboard sensor.

Abstract: Embodiments for testing embedded systems and their applications in an Internet of Things (IoT) environment by a processor, denoted as a Hardware-in-the-Loop as a Service (HiLaaS). In a simulated environment, one or more simulated entities and one or more real entities in a networked system may be tested in real-time according to received control parameters, for a price. The price is estimated by the system, based on other parameters, and offered to the user to accept or reject. Alternatively, the user may specify the price, the system estimates control parameters, and the user can accept or reject the control parameters. One or more properties of the one or more entities, the network system, or combination thereof may be estimated based on the testing of the one or more simulated entities, when the price and control parameters are accepted.

Abstract: In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.