9 research outputs found

    Modeling Long Term Impacts of Freeway Traffic Incidents on Travel Time Reliability

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    The objective of this study is to calibrate models of relationships between Travel Time Reliability measures and incident and traffic characteristics for a given highway segment

    The Impacts of Emergency Vehicle Signal Preemption on Urban Traffic Speed

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    We used GPS data from paratransit vehicles to evaluate the impact of emergency vehicles on urban traffic speeds. The results indicate that speed variance is significantly higher during emergency preemption and the mean speeds of traffic flowing in the same direction as the emergency vehicle and on crossing streets are lower during preemption than during normal conditions. Regression results indicate that traffic on major arterials and traffic in the opposite direction of the emergency vehicle tend to have higher speed during signal preemption. Signal preemption during peak periods and duration of preemption had a significant negative impact on traffic speeds. Also, the transition time has a negative impact on traffic speeds. The authors recommend further research on how to optimize (minimize) the preemption duration as well as transition time. Also, the impact of median type and number of lanes should be evaluated

    Nonlinear Acceleration and Deceleration Response Behavior in Stimulus-Response Car-Following Models

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    This study developed a nonlinear family of car-following models that emulate driving behavior in congested freeway traffic conditions. The study developed separate sub-models for acceleration and deceleration responses. The study calibrated these models using individual vehicle trajectory data for automobile following automobile collected on a segment of Interstate 101 in Los Angeles, California. The study used nonlinear regression with robust standard errors to estimate the model parameters and to obtain their distributions across drivers. The stimulus response thresholds that delimited the acceleration and deceleration responses were determined based on Signal Detection Theory. The results indicated that the average driver\u27s response time lag was lower for the deceleration response than for the acceleration response. This result was expected, since deceleration response is related to safety, therefore, drivers tend to respond faster than for acceleration response. The acceleration response is related to drivers\u27 desire to attain maximum speed, which is a less critical need than deceleration response. Due to similar reasons, the results also showed that the average stimulus response threshold was lower for deceleration response than acceleration response. Furthermore, the deceleration response had higher magnitude of parameters than the acceleration response, which further indicated that, on the average, drivers were more aggressive when required to decelerate than when they wanted to accelerate. Additionally, drivers\u27 response to negative stimuli is sometimes further aided by the activation of brake lights for a leading vehicle that is braking

    A Two-Stage Fuzzy Logic Controller for Traffic Signals

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    This paper presents the design and evaluation of a fuzzy logic traffic signal controller for an isolated intersection. The controller is designed to be responsive to real-time traffic demands. The fuzzy controller uses vehicle loop detectors, placed upstream of the intersection on each approach, to measure approach flows and estimate queues. These data are used to decide, at regular time intervals, whether to extend or terminate the current signal phase. These decisions are made using a two-stage fuzzy logic procedure. In the first stage, observed approach traffic flows are used to estimate relative traffic intensities in the competing approaches. These traffic intensities are then used in the second stage to determine whether the current signal phase should be extended or terminated. The performance of this controller is compared to that of a traffic-actuated controller for different traffic conditions on a simulated four-approach intersection

    System selection, benefits, and financial feasibility of implementing an advanced public transportation system

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    A procedure for selecting a suitable backbone radio communications system necessary for the implementation of advanced public transportation system technologies is presented. This procedure was used by the Regional Transportation Commission of Clark County, Nevada, in preparation of a radio communications master plan for the Las Vegas Citizens Area Transit system. The procedure recognizes the importance of taking into account not only the relative costs of the alternative candidate systems, but also other important factors such as the degree of system control by the transit agency, confidence in system development and technical practicality, potential for future expansion, and technical simplicity. Several alternative technologies for radio communications are reviewed and evaluated. Selection of the recommended system is based on the procedure that assigns relative weights to the important factors and gives each candidate system a score for each factor. The system with the highest total score is recommended for implementation. Potential system benefits are discussed and financial feasibility of the recommended system is evaluated by computing the annual rate of return on invested capital. Analysis shows that the system can pay for itself and also produce significant net savings in operation and capital costs. Although the analysis considered only potential savings in fleet size, an annual rate of return of over 21% on the invested capital has been shown to be achievable. Such savings can enable transit agencies to provide the same level of service at significantly reduced cost or expand the service without increasing operating costs

    Comparison of Traditional and Neural Classifiers for Pavement-Crack Detection

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    This paper presents a comparative evaluation of traditional and neural‐network classifiers to detect cracks in video images of asphalt‐concrete pavement surfaces. The traditional classifiers used are the Bayes classifier and the k‐nearest neighbor (k‐NN) decision rule. The neural classifiers are the multilayer feed‐forward (MLF) neural‐network classifier and a two‐stage piecewise linear neural‐network classifier. Included in the paper is a theoretical background of the classifiers, their implementation procedures, and a case study to evaluate their performance in detection and classification of crack segements in pavement images. The results are presented and compared, and the relative merits of these techniques are discussed. The research reported in this paper is part of an ongoing research project, the objective of which is to develop a neural‐network‐based methodology for the processing of video images for automated detection, classification, and quantification of cracking on pavement surfaces

    Simulating and Analyzing Incidents Using CORSIM and VISSIM Traffic Simulation Software

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    A key component in measuring the performance of a transportation network using simulation software involves simulating incidents and studying their impacts on system performance. Incidents on a freeway influence traffic behavior on not only the lane on which it occurred but also show considerable effect on the traffic of the adjacent lanes and the secondary road network. The capabilities afforded by various traffic simulation software to create and analyze incident vary significantly. The objective of this paper is to explore the capabilities of the CORSIM and VISSIM traffic simulation software to simulate and analyze incidents on a basic freeway segment. The study showed that incidents could be simulated more effectively using CORSIM when compared to VISSIM. The advantage in CORSIM lies with the comprehensive freeway incident simulation procedure, which is available as a part of the FRESIM module. The user can control the incident by specifying various factors such as the longitudinal position of a freeway link at which the incident has occurred, distance over which the effects last and the duration of the incident that influence the incident. The percentage of the traffic affected by the incident on the lane adjacent to the incident lane can be pre-established. This is used to reproduce real time situations where the traffic is affected by the adjacent lane traffic behavior. VISSIM has no special provision to simulate freeway incidents and/or work-zones that close a lane. However, incidents can be created on a freeway lane and its effects studied by exploring the capabilities of VISSIM by simulating a bus to stop for a stipulated amount of time. But, VISSIM does not have the ability to specify blockages or rubber necking on a lane specific basis or to simulate short-term and long-term interruptions to traffic (known as events) as can be done using CORSIM software. Data from the Las Vegas metropolitan area are used to compare the capabilities and performance of CORSIM and VISSIM simulation software

    Estimating Highway Capacity Considering Two-Regime Models

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    Capacity can be derived by fitting a relationship between the three fundamental traffic variables: flow, speed, and density. The study uses a dual-regime model with the consideration of all three variables. Different forms of functions were specified for congested and uncongested conditions. An optimization based estimation procedure was developed to minimize the distance between the theoretical functions and the traffic stream variable measurements. The optimal procedure was coded into a computer program and applied to three data sets representing different roadway classifications. The results indicate that the proposed method can produce good estimates of the key traffic stream parameters including the roadway capacity
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