Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

Abstract: Embodiments for network reconstruction from message data by a processor. A digital map may be created using one or more messages of a plurality of vehicles obtained at a plurality of control points of a route network. The digital map may be analyzed to estimate a feasibility of simultaneous trajectories of the plurality of vehicles between selected locations in the route network.

Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

Abstract: Embodiments for network reconstruction from message data by a processor. A digital map may be created using one or more messages of a plurality of vehicles obtained at a plurality of control points of a route network. The digital map may be analyzed to estimate a feasibility of simultaneous trajectories of the plurality of vehicles between selected locations in the route network.

Abstract: Embodiments relate to generating a route plan. A method of generating a route plan is provided. The method receives a route planning request that includes a starting location, a destination location, a desired arrival time, and a set of user preferences. The method obtains transport service information that includes schedules of a plurality of transport services provided for a geographic region including the starting location and the destination location. The method determines a set of transport services for each of a plurality of possible intermediate locations between the starting and destination locations. The sets of transportation services provide a maximum probability of arriving at the destination location before the desired arrival time as long as a user takes a first-arriving transportation among the set of transport services at each intermediate location while traveling from the starting location to the destination location.

Abstract: A method for routing in a network including a plurality of nodes and links between nodes, includes the steps of: setting a start node and a destination node; for each node, assigning a waiting time distribution for at least one means of transport for at least one intermediate node between the start node and the destination node; and providing a list of alternative means of transport linking the intermediate node to a subsequent node as a function of the waiting time distribution assigned to the at least one means of transport at the intermediate node.

Abstract: Embodiments relate to generating a route plan. A method of generating a route plan is provided. The method receives a route planning request that includes a starting location, a destination location, a desired arrival time, and a set of user preferences. The method obtains transport service information that includes schedules of a plurality of transport services provided for a geographic region including the starting location and the destination location. The method determines a set of transport services for each of a plurality of possible intermediate locations between the starting and destination locations. The sets of transportation services provide a maximum probability of arriving at the destination location before the desired arrival time as long as a user takes a first-arriving transportation among the set of transport services at each intermediate location while traveling from the starting location to the destination location.

Abstract: According to an exemplary embodiment, a computer-implemented method for attempting to optimize a supply chain network (SCN) includes forecasting demand in the SCN based on a set of demand data. One or more time-dependent reorder points (ROPs) deemed to optimize the SCN are generated by a computer processor, based on the demand forecast, where each time-dependent ROP represents an ROP that changes over time. A simulation of operations of the SCN is performed, using the time-dependent ROPs.

Abstract: According to an exemplary embodiment, a computer-implemented method for attempting to optimize a supply chain network (SCN) includes forecasting demand in the SCN based on a set of demand data. One or more time-dependent reorder points (ROPs) deemed to optimize the SCN are generated by a computer processor, based on the demand forecast, where each time-dependent ROP represents an ROP that changes over time. A simulation of operations of the SCN is performed, using the time-dependent ROPs.