Abstract: An embodiment for dynamically assigning vehicle parking is provided. The embodiment may include receiving one or more preferences regarding parking. The embodiment may also include in response to determining a detector vehicle detects a vacant parking spot, creating a network of vehicles within a pre-defined threshold of the vacant parking spot. The embodiment may further include notifying each vehicle in the network about the vacant parking spot and receiving one or more requests for the vacant parking spot from one or more requestor vehicles. The embodiment may also include identifying real-time information associated with roads within the pre-defined threshold of the vacant parking spot. The embodiment may further include assigning the vacant parking spot to a particular requestor vehicle in the network of vehicles. The embodiment may also include displaying an indicator placed adjacent to the particular requestor vehicle that is assigned the vacant parking spot.

Abstract: An embodiment for dynamically assigning vehicle parking is provided. The embodiment may include receiving one or more preferences regarding parking. The embodiment may also include in response to determining a detector vehicle detects a vacant parking spot, creating a network of vehicles within a pre-defined threshold of the vacant parking spot. The embodiment may further include notifying each vehicle in the network about the vacant parking spot and receiving one or more requests for the vacant parking spot from one or more requestor vehicles. The embodiment may also include identifying real-time information associated with roads within the pre-defined threshold of the vacant parking spot. The embodiment may further include assigning the vacant parking spot to a particular requestor vehicle in the network of vehicles. The embodiment may also include displaying an indicator placed adjacent to the particular requestor vehicle that is assigned the vacant parking spot.

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 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: 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: 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: 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: A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.

Abstract: A method for forecasting time delays added to a scheduled start time and a scheduled end time of a task includes generating a stochastic model of the task and resources affecting the task, the stochastic model includes a reactionary delay component that is a function of previous task end times and a root cause delay component that is an independent random process at a specific time. The method further includes: calculating a probability distribution of time delays added to the scheduled start time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of start times; and calculating a probability distribution of time delays added to the scheduled end time as a combination of the reactionary delay component and the root cause delay component using the stochastic model to provide a probability distribution of end times.

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.

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 monitoring risk associated with operating a vehicle by a processor. One or more behavior parameters of an operator of a vehicle may be learned in relation to the vehicle, one or more alternative vehicles, or a combination thereof using one or more sensing devices for a journey. A risk associated with the one or more learned behavior parameters for the journey may be assessed.

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: Techniques for mixed-binary constrained optimization facilitated by quantum computers are provided. In one example, a system includes a quantum processor and a classical processor. The quantum processor performs a binary unconstrained optimization process for data associated with a decision-making problem. The classical processor performs a convex constrained optimization process for the data.

Abstract: A system and method for learning and predicting personalized risk associated with a journey for a user are presented. Contextual data may be gathered and analyzed from a plurality of data sources relating to a journey of a user. A risk associated with the journey may be learned for the user according to the contextual data. One or more risk models may be generated according to the learned risks. One or more potential risks associated with a subsequent journey may be predicted using the one or more risk models.

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.