Length-based vehicle classification data are important inputs for traffic operation, pavement design, and transportation planning. However, such data are not directly measurable by singleloop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. In this study a Video-based Vehicle Detection and Classification (VVDC) system was developed for truck data collection using wide-ranging available surveillance cameras. Several computer-vision based algorithms were developed or applied to extract background image from a video sequence, detect presence of vehicles, identify and remove shadows, and calculate pixel-based vehicle lengths for classification. Care was taken to robustly handle negative impacts resulting from vehicle occlusions in the horizontal direction and slight camera vibrations. The pixel-represented lengths were exploited to relatively distinguish long vehicles from short vehicles, and hence the need for complicated camera calibration can be eliminated. These algorithms were implemented in the prototype VVDC system using Microsoft Visual C#. As a plug & play system, the VVDC system is capable of processing both digitized image streams and live video signals in real time. The system was tested at three test locations under different traffic and environmental conditions. The accuracy for vehicle detection was above 97 percent and the total truck count error was lower than 9 percent for all three tests. This indicates that the video image processing method developed for vehicle detection and classification in this study is indeed a viable alternative for truck data collection.

Vehicle headway distribution is fundamental for several important traffic research and simulation issues. Many headway models have been developed over the past decades. Each has its own strength and weakness. Selection of the most suitable model for a certain traffic condition remains an open issue. A comprehensive study of the performance of typical headway distribution models on urban freeways is presented. With the advanced loop event data analyzer system, many accurate headway observations were obtained from I-5 in the area of Seattle, Washington. These headway data were used to calibrate and examine the performance of various headway models. The goodness of fit for several most commonly used headway distribution models was investigated by using headways observed on regular lanes and high-occupancy-vehicle (HOV) lanes from different time periods of day. To evaluate the performance of these headway models, the analytical Kolmogorov-Smirnov test statistic and visualized comparison curves were used to measure and reflect their overall goodness of fit to the collected headway data. Although each model has its own practicability to a certain extent, the test results showed that the double-displaced negative exponential distribution model provided the best fit to these urban freeway headway data, especially for HOV lanes at wideranging flow levels. The shifted lognormal distribution also fits the general purpose lane headways very well. As a byproduct, a new standard parameter estimation method was developed for calibrating complex multiparameter headway models.

A multi-agent reinforcement learning for adaptive traffic signal control optimization.• Consider control unit at intersection as agent that can communicate with others through knowledge-sharing protocol.• Proposed algorithm achieves consistent improvements over baselines on both simulated and real-world data.

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Dramatically increasing travel demands and insufficient traffic facilities have induced severe traffic congestion. High-occupancy toll (HOT) lane operation has been proposed as one of the most applicable and acceptable countermeasures against freeway congestion. With balanced pricing and vehicle occupancy constraints, HOT lane operations can realize the optimal traffic allocation and enhance overall infrastructure efficiency. However, few previous studies have concentrated on optimal tolling strategies. Two major problems with inferior tolling strategies degrade HOT lane system performance. First, an undersensitive tolling algorithm is incapable of handling the hysteretic properties of traffic systems and may cause severe response delays. Second, oversensitive characteristics of imperfect tolling strategies may cause unfavorable flow fluctuations in HOT and general-purpose lanes that disrupt traffic operations. A new feedback-based tolling algorithm to optimize HOT lane operations addresses these problems. To decompose the calculation complexity, a second-order control scheme is used in this algorithm. On the basis of traffic speed conditions and toll changing patterns, the optimal flow ratio for HOT lane use is calculated by using feedback control theory. The appropriate toll rate is then estimated backward by using the discrete route choice model. This algorithm is simple, effective, and easy to implement. VISSIM-based simulation tests were conducted to examine its practicality and effectiveness. Test results show that the proposed tolling algorithm performed reasonably well in optimizing overall traffic operations of the HOT lane system under various traffic conditions.

This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three‐priority‐layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on‐ramp storage capacities, and preventing on‐ramp vehicles from overflowing to local streets, balancing on‐ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real‐time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM‐based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real‐world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system‐wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm.

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