Abstract: Implementations claimed and described herein provide systems and methods for map routing matching system, wherein the one or more machine-learning models scores link candidates of location data points to determine a most probable route. In one implementation, use a machine-learning model to calculate a probability score for a plurality of link candidates. The machine-learning model scores each link candidates based on weighting set by training data, wherein the weighting associated with at least one of travel time, match distance, heading difference, speed conformity, road curvature, road classification, or travel distance versus time delay. A plurality of total probability scores derived from one or more combinations of probability scores for respective link candidates is calculated, wherein one link candidate is selected for each location data point.

Abstract: Implementations include classifying vehicle trips as similar to previous trips or trips reversed based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips or trips reversed. In one instance, two trips that share a similar route but in opposite directions may be identified as a trip pair and stored in a trip database as the trip pair.

Abstract: Implementations claimed and described herein provide systems and methods for simulating traffic using synthetic data based on agent-based modeling that simulates real drivers in a particular geographic area and time frame. In one implementation, inputting, in a machine-learning model of a simulation system, real-world agent-based position data associated with a custom selection of a geographic area and a time frame. The machine-learning model of the simulation system outputs metrics associated with synthetic agent-based position data over time within a map for the simulated environment and the variation of the simulated environment, wherein the metrics represents synthetic movement behavior of agents associated with synthetic individuals based on real movement behavior associated with the geographic area and the time frame.

Abstract: Implementations claimed and described herein provide systems and methods for map routing matching system, wherein the one or more machine-learning models scores link candidates of location data points to determine a most probable route. In one implementation, use a machine-learning model to calculate a probability score for a plurality of link candidates. The machine-learning model scores each link candidates based on weighting set by training data, wherein the weighting associated with at least one of travel time, match distance, heading difference, speed conformity, road curvature, road classification, or travel distance versus time delay. A plurality of total probability scores derived from one or more combinations of probability scores for respective link candidates is calculated, wherein one link candidate is selected for each location data point.

Abstract: Implementations include classifying vehicle trips as similar to previous trips based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips. In one instance, information or data of a new trip may be adjusted based on previous trip data. For example, aspects of the new trip or the previous trip may be updated with information or data of a previous trip fi the new trip and the previous trip are similar.

Abstract: Implementations include classifying vehicle trips as similar to previous trips based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips. In one instance, information or data of a new trip may be adjusted based on previous trip data. For example, aspects of the new trip or the previous trip may be updated with information or data of a previous trip if the new trip and the previous trip are similar.

Abstract: Implementations include classifying vehicle trips as similar to previous trips or trips reversed based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips or trips reversed. In one instance, two trips that share a similar route but in opposite directions may be identified as a trip pair and stored in a trip database as the trip pair.

Abstract: Implementations include classifying vehicle trips as similar to previous trips based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips. In one instance, information or data of a new trip may be adjusted based on previous trip data. For example, aspects of the new trip or the previous trip may be updated with information or data of a previous trip if the new trip and the previous trip are similar.