Abstract
Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment, and traffic demand estimation. However, it is very time consuming and costly to obtain vehicle turning movement information manually. Previous efforts to simplify this process were focused on solving the problem using an O-D matrix, but this method proved to be inaccurate and unreliable with the existing data acquisition system. Another study involved the identification of vehicle turning movements from the detector information, but the presence of shared lanes led to uncertainties in vehicle matching, thus limiting application of the method only to intersections without shared lanes. In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry. The results from lab experiments as well as a field test show that the algorithm is very promising and may potentially be expanded for field applications.
Highlights
Turning Movement Information (TMI) relates to the number of vehicles completing left or right turns or through movements from each intersection approach
In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry
Chen et al [8] used a path flow estimator to derive TMI for an entire roadway network and Zhang et al [9] tested a nonlinear programming approach to calculate intersection O-D matrix. Those methods rely on accurate data from a large number of detectors to ensure feasible and stable solutions to the mathematical models; in addition, the TMI is not obtained in small time intervals in support of real time applications
Summary
Turning Movement Information (TMI) relates to the number of vehicles completing left or right turns or through movements from each intersection approach. Nihan and Davis [3] as well as Cremer and Keller [4] developed a set of dynamic O-D estimation models for intersections and small networks based on the prediction-error minimization method In their models, the volumes of vehicles flowing in and out of each intersection approach are used as input parameters. Chen et al [8] used a path flow estimator to derive TMI for an entire roadway network and Zhang et al [9] tested a nonlinear programming approach to calculate intersection O-D matrix Those methods rely on accurate data from a large number of detectors to ensure feasible and stable solutions to the mathematical models; in addition, the TMI is not obtained in small time intervals in support of real time applications. This system aims at improving data accuracy at intersections and the algorithm is designed to treat shared lanes in different lane configurations
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