Abstract: A method and apparatus for predicting traffic on a transportation network where real time data points are missing. In one embodiment, the missing data is estimated using a calibration model comprised of historical data that can be periodically updated, from select links constituting a relationship vector. The missing data can be estimated off-line whereafter it can be used to predict traffic for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network. The invention further discloses a method for in-vehicle navigation; and a method for traffic prediction for a single lane.

Abstract: A routing plan for elevator passengers can be developed by a computer system, when there are a plurality of elevator users requesting service from a plurality of elevators. The routing plan can be based on user input that specifies a destination and a current location. Profile data for each user can also be used to develop the routing plan. The input and profile data is correlated with a shared resource in each elevator, and the routing plan is developed from the correlated data. The plan is then communicated to the users.

Abstract: Locations for traffic sensors can be determined by a computer system that identifies a particular segment of a travel path. Traffic flow data from other segments of travel path are accessed based on traffic flow characteristics of the particular path. Using the traffic flow data, parameters for a traffic incident symptom propagation model are generated, and a location of a traffic incident along the segment of the path is determined. Using time-to-detection limits and the incident model, upstream and downstream distances are determined, and the locations of two sensors are identified based on the distances.

Abstract: In an approach to manage parking facility occupancy, a computing device retrieves a customer identification for a customer entering a parking facility and determines whether a customer profile is available for the customer. Upon determining the customer profile is available, the computing device retrieves the customer profile from a database. Additionally, the method includes the computing device determining, based, at least in part, on the customer profile, a customer spending level for the customer. Furthermore, the computing device determines whether an alert is received, the alert indicating a parking facility occupancy level is predicted to attain an alert threshold level. In response to determining the alert is received, the computing device determines a retail incentive for the customer, based, in part, on the customer spending level.

Abstract: In an approach to manage parking facility occupancy, a computing device retrieves a customer identification for a customer entering a parking facility and determines whether a customer profile is available for the customer. Upon determining the customer profile is available, the computing device retrieves the customer profile from a database. Additionally, the method includes the computing device determining, based, at least in part, on the customer profile, a customer spending level for the customer. Furthermore, the computing device determines whether an alert is received, the alert indicating a parking facility occupancy level is predicted to attain an alert threshold level. In response to determining the alert is received, the computing device determines a retail incentive for the customer, based, in part, on the customer spending level.

Abstract: A prediction modeling system and computer program product for implementing forecasting models that involve numerous measurement locations, e.g., urban occupancy traffic data. The system a data volatility reduction technique based on computing a congestion threshold for each prediction location, and using that threshold in a filtering scheme. Through the use of calibration, and by obtaining an extremal or other specified solution (e.g., maximization) of empirical volume-occupancy curves as a function of the occupancy level, significant accuracy gains are achieved and at virtually no loss of important information to the end user. The calibration use quantile regression to deal with the asymmetry and scatter of the empirical data. The argmax of each empirical function is used in a unidimensional projection to essentially filter all fully congested occupancy level and treat them as a single state.

Abstract: A method (and structure) for adjusting control parameters of a traffic management system in a presence of one or more incidents on a network, including representing, using a tree format, a prioritization across network junctions prior to an incident. An incident-affected junction is elevated higher in priority on the tree, depending upon a severity level of the incident. Weights are associated with each junction as a function of its height in the tree. A real-time optimization of the control parameters for the network is solved, using the weights on the junctions.

Abstract: Locations for traffic sensors can be determined by a computer system that identifies a particular segment of a travel path. Traffic flow data from other segments of travel path are accessed based on traffic flow characteristics of the particular path. Using the traffic flow data, parameters for a traffic incident symptom propagation model are generated, and a location of a traffic incident along the segment of the path is determined. Using time-to-detection limits and the incident model, upstream and downstream distances are determined, and the locations of two sensors are identified based on the distances.

Abstract: A prediction modeling system, method and computer program product for implementing forecasting models that involve numerous measurement locations, e.g., urban occupancy traffic data. The method invokes a data volatility reduction technique based on computing a congestion threshold for each prediction location, and using that threshold in a filtering scheme. Through the use of calibration, and by obtaining an extremal or other specified solution (e.g., maximization) of empirical volume-occupancy curves as a function of the occupancy level, significant accuracy gains are achieved and at virtually no loss of important information to the end user. The calibration use quantile regression to deal with the asymmetry and scatter of the empirical data. The argmax of each empirical function is used in a unidimensional projection to essentially filter all fully congested occupancy level and treat them as a single state.

Abstract: A computerized method for adjusting control parameters of a traffic management system in a presence of one or more incidents on a network, said method includes representing, using a tree format, a prioritization across network junctions, as executed by a processor on a computer. Associating weights with each junction as a function of its height in the tree; and solving a real-time optimization of control parameters for the network, using the weights on the junctions, and upon an occurrence of an incident in the network and depending upon a severity level of the incident, an incident-affected junction is selectively elevated higher in the tree, and the reallocated junction weights resulting from the incident are used for solving the optimization of network control parameters.

Abstract: A location for a traffic incident can be determined by a computer system, using data from a first and second sensor along a travel path. A receiving and sending symptom of the traffic incident are detected from a first and second sensor, using traffic flow data from the sensors. The locations of the first and second sensors are determined. The location and traffic flow data from each sensor are used to create a sending and receiving profiles. From the profiles, a convergence formula is build. Using the convergence formula and by determining a convergence point for the sending and receiving symptoms, a time and location of the traffic incident is identified.

Abstract: Methods and systems may provide for joining an overlay network of a plurality of peer devices and identifying a local preference for an area service available to the plurality of peer devices. Additionally, the local preference may be used to negotiate a common preference for the area service with the plurality of peer devices. In one example, the common preference is a best fit value for the plurality of peer devices on the overlay network.

Abstract: Methods and systems may provide for joining an overlay network of a plurality of peer devices and identifying a local preference for an area service available to the plurality of peer devices. Additionally, the local preference may be used to negotiate a common preference for the area service with the plurality of peer devices. In one example, the common preference is a best fit value for the plurality of peer devices on the overlay network.

Abstract: Systems and methods for incident detection are provided. A system for incident detection includes a network including at least one detector for detecting events in the network, a detection module capable of processing data from the at least one detector, and a calibration module capable of calibrating a plurality of bands for the incident detection based on a plurality of decision variables, wherein the plurality of bands define thresholds that are time-varying for all measurement locations in the network, and the thresholds are estimated using nonparametric quantile regression.

Abstract: Systems and methods for incident detection are provided. A system for incident detection includes a network including at least one detector for detecting events in the network, a detection module capable of processing data from the at least one detector, and a calibration module capable of calibrating a plurality of bands for the incident detection based on a plurality of decision variables, wherein the plurality of bands define thresholds that are time-varying for all measurement locations in the network, and the thresholds are estimated using nonparametric quantile regression.

Abstract: A system, method and computer program product for estimating a vehicle arrival time. The system receives information representing prior travel times of vehicles between pre-determined vehicle stops along a vehicle route. The system receives real-time data representing a current journey. The current journey refers to a movement of a vehicle currently traveling along the route. The system calculates a regular trend representing the current journey based on the received prior travel times information and the received real-time data. The system computes a deviation from the regular trend in the current journey. The system determines a future traffic status in subsequent vehicle stops in the current journey. The system estimates, for the vehicle, each arrival time of each subsequent vehicle stop based on the calculated regular trend, the computed deviation and the determined future traffic status.

Abstract: A method, an apparatus and an article of manufacture for incident duration prediction. The method includes obtaining incident data for at least one traffic-related incident in a selected geographic area, obtaining traffic data for the selected geographic area, spatially and temporally associating the at least one traffic-related incident with the traffic data to generate incident duration data for the at least one traffic-related incident, and predicting incident duration of at least one additional traffic-related incident based on the incident duration data for the at least one traffic-related incident.

Abstract: A method (and structure) for predicting an impact of an incident on a system. Incident properties and traffic conditions of at least one historical incident are received, to calibrate one or more parameters of a traffic model, as executed by a processor on a computer. Current traffic conditions, a prediction of recurrent traffic conditions, and an indication of a current incident on the system are received. A duration of the current incident and traffic conditions at a location at which the current incident occurs are predicted. Predicted traffic conditions in the system are calculated, based on the calibrated model parameters, the current traffic conditions, the prediction of recurrent traffic conditions, and the predicted duration of the current incident and traffic conditions at the current incident location.

Abstract: The present disclosure relates generally to real-time traffic prediction and/or estimation using GPS data with low sampling rates. In various examples, real-time traffic prediction and/or estimation using GPS data with low sampling rates may be implemented in the form of systems, methods and/or algorithms.

Abstract: A method (and structure) for adjusting control parameters of a traffic management system in a presence of one or more incidents on a network, including representing, using a tree format, a prioritization across network junctions prior to an incident. An incident-affected junction is elevated higher in priority on the tree, depending upon a severity level of the incident. Weights are associated with each junction as a function of its height in the tree. A real-time optimization of the control parameters for the network is solved, using the weights on the junctions.