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Advances in machine vision techniques have led to algorithms and integrated systems that can be applied in transportation engineering to improve surveillance and control. Despite these advances, certain problems in the effective integration of machine-vision based systems at complex intersections and complex freeway sections still remain. These are related to increasing system performance in the identification, analysis, and detection of the traffic state in real time. This work examines the feasibility of providing transformed visual input to existing machine-vision based systems, in order to gain increased efficiency and cost effectiveness of integrated transportation systems. Two transformations are developed, homography-based transformation and panoramic image reprojection. Homography-based transformation operates on video of the road scene, provided by classical cameras, and seeks to transform any view to a top-down view. This transforms the three-dimensional problem of image analysis for, e.g., road event detection to a two-dimensional one. Panoramic image reprojection employs panoramic cameras to reduce required hardware, and the complexity and cost incurred in obtaining the desired road view. The image reprojection technique allows the reconstruction of undistorted, perspectively correct views from panoramic images in real time. Tests at sites in Spain, the United Kingdom, and Greece are performed on-line and off-line in combination with operating machine-vision based incident detection systems. Test results indicate that the two methods simplify the input provided to machine vision, and reduce the workload and amount of hardware in implementing complex machine-vision based systems for incident detection. Both modules can be integrated into incident detection systems to improve their overall efficiency and ease of application. DOI: 10.1061/(ASCE)0733-947X(2006) (837) CE Database subject headings: Imaging techniques; Transportation systems; Traffic accidents; Algorithms.

Hierarchical traffic control that adapts to exogenous changes, optimizes system performance, and reduces local disturbances is one of the most effective advanced traffic management system methods for relieving congestion in freeway corridors. Optimal ramp-metering control lies at the center of such a hierarchical control system. However, current ramp-metering control strategies are not truly dynamic, are not optimal, or consider only parts of the freeway corridor. This paper presents an optimal ramp-metering control method to alleviate congestion in the interacting corridor components, including freeway, parallel arterials, and connecting streets. The control formulation employs a continuum traffic-flow model of the corridor dynamics, and the conjugate gradient method to search for optimal ramp-metering rates according to the total travel time in the corridor. A test of the optimal metering for a simple one-ramp segment of a freeway corridor is presented.

Traffic data, essential for the detection of freeway incidents, are often corrupted by impulsive noise and short-term traffic inhomogeneities that may impair detection performance. Rigorous data filtering can reduce the undesirable noise and enhance the incident signal. A new incident detection algorithm is developed that employs short-term time averaging (low-pass filter) to reduce the adverse effects of short-term traffic fluctuations and impulsive noise in the detection process. The detection algorithm traces the filtered spatial occupancy difference between adjacent detector stations through time, and detects an incident when this difference changes significantly in a short time period. The new algorithm was evaluated with data from a congested freeway in the Minneapolis-St. Paul metropolitan area and compared against major existing algorithms. Test results indicate the ability of the Minnesota algorithm to significantly alleviate the false-alarm problem, while preserving high detection performance, as compared to existing algorithms.