Lane-Level Traffic Prediction Using Machine Learning

The patent introduces lane-level traffic data as an intermediary between GPS location data and lane identification. By collecting traffic data (speed, volume, patterns) specific to each lane and comparing it with observed vehicle characteristics, the system can infer which lane a vehicle is in without requiring direct lane-detecting hardware. This mediator approach resolves the contradiction by achieving lane-level precision through data processing rather than direct measurement.

The patent replaces mechanical/physical lane detection methods (such as lane detection cameras, RFID tags, or specialized sensors) with a data-processing-based system. Instead of using physical means to directly detect lane position, the system uses machine learning models that analyze traffic patterns, speed distributions, and vehicle characteristics to probabilistically determine lane membership, thereby reducing device complexity while maintaining precision.

The patent segments traffic data collection and processing by lane. Instead of aggregating all traffic data at the road segment level, the system separately collects, processes, and analyzes traffic data for each individual lane. This segmentation allows the machine learning model to learn lane-specific traffic patterns, speed distributions, and congestion characteristics, thereby improving prediction precision while the modular architecture maintains processing efficiency through parallel data handling.

The patent applies local quality by treating each lane as a distinct entity with its own traffic characteristics rather than assuming uniform conditions across the entire road segment. The system collects and processes traffic data (speed, volume, flow patterns) specific to each lane, allowing the machine learning model to capture localized traffic behaviors. This lane-specific approach improves prediction accuracy for each lane while the overall system structure maintains efficiency through standardized processing pipelines.

The patent changes the parameters used for model training from basic traffic data to lane-specific parameters including speed distributions, traffic volume patterns, and temporal characteristics for each lane. By transforming the input data into these more granular parameters, the machine learning model can capture the distinct traffic patterns of split lanes (e.g., one lane congested while another is free-flowing). The increased prediction precision is achieved through these parameter changes while the model architecture remains relatively standard, managing training complexity.

The patent introduces dynamic elements to the training data by incorporating temporal variations in lane-specific traffic patterns. The system collects traffic data over time and uses these dynamic patterns (how traffic conditions evolve, peak hours, recurring congestion patterns) to train the model. This dynamic approach allows the model to predict split lane patterns more accurately by understanding the temporal behavior of each lane, while the incremental training approach manages complexity by building models progressively.