Real-time estimation of lane-to-lane turning flows at isolated signalized junctions

In this paper, we develop rule- and model-based approaches for the real-time estimation of lane-to-lane turning flows. Our aim is to determine the turning proportions of vehicles based on detector information at isolated signalized junctions and thereby establish effective control strategies for adaptive traffic control systems. The key concept involves identifying the entrance lane of a vehicle detected in an exit lane at the signalized junction. Lane-to-lane turning flows are estimated by tracing the corresponding entrance lanes of the vehicle based on the detector and signal information from the set of potential entrance lanes at the junction. In the rule-based approach, the entrance lane of a vehicle detected in an exit lane is identified according to a set of specified rules. The model-based approach, which is based on utility maximization, is used to identify the most probable turns in a set of potential upstream entrance lanes. Both computer simulations and real-world traffic data show that the model-based approach outperforms the rule-based approach, particularly when turning on red is allowed, and is capable of accurate estimation under a wide range of traffic conditions in real time. However, the rule-based approach is simpler and does not require calibration, which are positive assets when no prior data are available for calibration.