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).