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: 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: A system, a method and computer-readable media for presenting dynamic traffic information. A user profile is stored, and advisory data based on the user profile is gathered. The advisory data may include traffic information associated with road segments identified by the user profile. Upon receiving a user input, the advisory data is presented to a user.

Abstract: A system, a method and computer-readable media for presenting dynamic traffic information. A user profile is stored, and advisory data based on the user profile is gathered. The advisory data may include traffic information associated with road segments identified by the user profile. Upon receiving a user input, the advisory data is presented to a user.

Abstract: A system, a method and computer-readable media for presenting dynamic traffic information. A user profile is stored, and advisory data based on the user profile is gathered. The advisory data may include traffic information associated with road segments identified by the user profile. Upon receiving a user input, the advisory data is presented to a user.