Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics from the data samples. In some situations, the inferences based on the data samples includes repeatedly determining traffic flow characteristics for road segments of interest during periods of time, such as to determine traffic volume and/or average occupancy of the road.

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics from the data samples. In some situations, the inferences based on the data samples includes repeatedly determining traffic flow characteristics for road segments of interest during periods of time, such as to determine traffic volume and/or average occupancy of the road.

Abstract: Techniques are described for generating predictions of future traffic conditions at multiple future times, such as by using probabilistic techniques to assess various input data while repeatedly producing future time series predictions for each of numerous road segments (e.g., in a real-time manner based on changing current conditions for a network of roads in a given geographic area). In some situations, one or more predictive Bayesian models and corresponding decision trees are automatically created for use in generating the future traffic condition predictions for each geographic area of interest, such as based on observed historical traffic conditions for those geographic areas. Predicted future traffic condition information may then be used in a variety of ways to assist in travel and for other purposes, such as to plan optimal routes through a network of roads based on predictions about traffic conditions for the roads at multiple future times.

Abstract: Techniques are described for displaying or otherwise providing information to users regarding various types of road traffic condition information in various ways. The information may be provided, for example, as part of a user interface (or “UI”), which may in some situations further include one or more types of user-selectable controls to allow a user to manipulate in various ways what road traffic condition information is displayed and/or how the information is displayed. A variety of types of road traffic condition information may be presented to users in various manners, including by presenting information on graphically displayed maps for geographic areas to indicate various information about road conditions in the geographic area. In addition, provided controls may allow users to select particular times, select particular routes, indicate to perform animation of various types of changing traffic conditions over a sequence of multiple successive times, etc.

Abstract: Techniques are described for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. Such automatically determined information may then be used to assist users in obtaining information from the domain that is of interest (e.g., documents with contents that are relevant to user-specified terms and/or to other terms that are determined to be sufficiently related to the user-specified terms). For example, recommendations may be automatically generated for a user by using information about specified preferences or other interests of the user with respect to one or more terms and identifying other particular terms that are sufficiently probable to be of interest to that user, such as based on a generated probabilistic representation of relationships between particular terms for the domain.

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from road traffic sensors (e.g., physical sensors that are near or embedded in the roads) and/or from vehicles and other mobile data sources traveling on the roads. The assessment of road traffic conditions based on obtained sensor data readings and/or other data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest. Assessing obtained data may further include determining traffic conditions (e.g., traffic flow and/or average traffic speed) for various portions of a road network in a particular geographic area, based at least in part on obtained data samples.

Abstract: Techniques are described for determining and using relevant information related to domains of interest. In at least some situations, the techniques include automatically analyzing documents, terms and other information related to a domain of interest in order to automatically determine information about relevant themes within the domain and/or about which documents have contents that are relevant to such themes. Such automatically determined information related to a domain may then be used in various ways, including to assist users in specifying themes of interest and/or in obtaining documents and/or document fragments with contents that are relevant to specified themes. In addition, information about how the automatically determined information is used by users may be tracked and used as feedback for learning improved determinations of relevant themes and relevant documents within the domain, such as by using automated machine learning techniques.

Abstract: Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

Abstract: Techniques are described for determining and providing location-based information, including based on decomposing a geographic area into numerous smaller areas and using one or more layers of polygons to represent various of the smaller areas. In some situations, location-based information for a geographic area is determined efficiently and accurately to reflect actual travel distances and/or times, such as for use in identifying one or more points of interest that are sufficiently proximate to an indicated location (e.g., a current location of a user), and/or to determine actual travel distances and/or times between two or more indicated locations of interest. The location-based information determination may in some situations be performed in a realtime or near-realtime manner after a request is received from a user, such as to interactively provide results to the user that include or are based at least in part on the determined location-based information.

Abstract: A model-based system and method for global optimization that utilizes quantum mechanics in order to approximate the global minimum of a given problem (e.g., mathematical function). A quantum mechanical particle with a sufficiently large mass has a ground state solution to the Schrödinger Equation which is localized to the global minimum of the energy field, or potential, it experiences. A given function is modeled as a potential, and a quantum mechanical particle with a sufficiently large mass is placed in the potential. The ground state of the particle is determined, and the probability density function of the ground state of the particle is calculated. The peak of the probability density function is localized to the global minimum of the potential.

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from road traffic sensors (e.g., physical sensors that are near or embedded in the roads) and/or from vehicles and other mobile data sources traveling on the roads. The assessment of road traffic conditions based on obtained sensor data readings and/or other data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest. Assessing obtained data may further include determining traffic conditions (e.g., traffic flow and/or average traffic speed) for various portions of a road network in a particular geographic area, based at least in part on obtained data samples.

Abstract: Techniques are described for automatically detecting anomalous road traffic conditions and for providing information about the detected anomalies, such as for use in facilitating travel on roads of interest. Anomalous road traffic conditions may be identified using target traffic conditions for a particular road segment at a particular selected time, such as target traffic conditions that reflect actual traffic conditions for a current or past selected time, and/or target traffic conditions that reflect predicted future traffic conditions for a future selected time. Target traffic conditions may be compared to distinct expected road traffic conditions for a road segment at a selected time, with the expected conditions reflecting road traffic conditions that are typical or normal for the road segment at the selected time. Anomalous conditions may be identified based on sufficiently large differences from the expected conditions, and information about the anomalous conditions may be provided in various ways.

Abstract: A model-based system and method for global optimization that utilizes quantum mechanics in order to approximate the global minimum of a given problem (e.g., mathematical function). A quantum mechanical particle with a sufficiently large mass has a ground state solution to the Schrödinger Equation which is localized to the global minimum of the energy field, or potential, it experiences. A given function is modeled as a potential, and a quantum mechanical particle with a sufficiently large mass is placed in the potential. The ground state of the particle is determined, and the probability density function of the ground state of the particle is calculated. The peak of the probability density function is localized to the global minimum of the potential.