Abstract: A user interface facilitates assessing security risks for linear corridors. The interface includes a navigation window presenting geospatial data for an area associated with a corridor. The interface also includes a navigation tool having a corridor indicator that provides a diagrammatic linear representation of the linear corridor, indicia along the corridor indicator that represent critical assets, and a corridor window superimposed on the corridor indicator that slides linearly along the corridor indicator. The portion of the corridor displayed in the interface corresponds to a location of the corridor window on the corridor indicator. The interface also includes an attributes area presenting information associated with the area presented in the navigation window. At least one of the navigation window and the attributes area is configured to present risk assessment information associated with the linear corridor.

Abstract: Travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of anticipated traffic routes, demographic data and/or other types of socioeconomic data. This data can source a prediction algorithm or be used to forecast traffic patterns. Methods and systems are described for, among other things, optimizing traffic predictions, forecasting traffic patterns using user-assigned trip patterns, associating rich attribute information to navigation routes, exposing personal-logistic information to a group, communicating traffic-situation-generated alerts based on user information, optimizing a presentation of user-defined traffic routes, and presenting location indications based on proximity (temporal or geographical).

Abstract: Travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of anticipated traffic routes, demographic data and/or other types of socioeconomic data. This data can source a prediction algorithm or be used to forecast traffic patterns. Methods and systems are described for, among other things, optimizing traffic predictions, forecasting traffic patterns using user-assigned trip patterns, associating rich attribute information to navigation routes, exposing personal-logistic information to a group, communicating traffic-situation-generated alerts based on user information, optimizing a presentation of user-defined traffic routes, and presenting location indications based on proximity (temporal or geographical).

Abstract: A user interface facilitates assessing security risks for linear corridors. The interface includes a navigation window presenting geospatial data for an area associated with a corridor. The interface also includes a navigation tool having a corridor indicator that provides a diagrammatic linear representation of the linear corridor, indicia along the corridor indicator that represent critical assets, and a corridor window superimposed on the corridor indicator that slides linearly along the corridor indicator. The portion of the corridor displayed in the interface corresponds to a location of the corridor window on the corridor indicator. The interface also includes an attributes area presenting information associated with the area presented in the navigation window. At least one of the navigation window and the attributes area is configured to present risk assessment information associated with the linear corridor.

Abstract: The assessment of security risks associated with linear corridors, such as transportation corridors and utilities corridors, is provided. Initially, a site is selected for evaluation. Survey data and context sensitive information, such as demographic information, environmental information, corridor asset information, and the like, is collected for the site. Linear corridors are identified within the data, and critical assets within each linear corridor are also identified. Security risks associated with linear corridors may then be assessed using the survey data and context sensitive information. In addition, mitigation strategies, response strategies, and recovery strategies may be developed for the security risks assessed for the linear corridors.

Abstract: Travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of anticipated traffic routes, demographic data and/or other types of socioeconomic data. This data can source a prediction algorithm or be used to forecast traffic patterns. Methods and systems are described for, among other things, optimizing traffic predictions, forecasting traffic patterns using user-assigned trip patterns, associating rich attribute information to navigation routes, exposing personal-logistic information to a group, communicating traffic-situation-generated alerts based on user information, optimizing a presentation of user-defined traffic routes, and presenting location indications based on proximity (temporal or geographical).

Abstract: Travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of origin/destination data pairs, user preferences, demographic data and other types of socioeconomic data. This data can source a prediction algorithm or be used to calibrate or more make accurate a current algorithm. Methods and systems are described for, among other things, optimizing traffic predictions, forecasting traffic patterns using user-assigned trip patterns, associating rich attribute information to navigation routes, exposing personal-logistic information to a group, communicating traffic-situation-generated alerts based on user information, optimizing a presentation of user-defined traffic routes, and presenting location indications based on proximity (temporal or geographical).

Abstract: Travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of origin/destination data pairs, user preferences, demographic data and other types of socioeconomic data. This data can source a prediction algorithm or be used to calibrate or more make accurate a current algorithm. Methods and systems are described for, among other things, optimizing traffic predictions, forecasting traffic patterns using user-assigned trip patterns, associating rich attribute information to navigation routes, exposing personal-logistic information to a group, communicating traffic-situation-generated alerts based on user information, optimizing a presentation of user-defined traffic routes, and presenting location indications based on proximity (temporal or geographical).

Abstract: Travel-demand forecasting methods are described for predicting traffic volume based, at least in part, on user-entered data in the form of origin/destination data pairs, user preferences, demographic data and other types of socioeconomic data. This data can source a prediction algorithm or be used to calibrate or more make accurate a current algorithm. Methods and systems are described for, among other things, optimizing traffic predictions, forecasting traffic patterns using user-assigned trip patterns, associating rich attribute information to navigation routes, exposing personal-logistic information to a group, communicating traffic-situation-generated alerts based on user information, optimizing a presentation of user-defined traffic routes, and presenting location indications based on proximity (temporal or geographical).