671 research outputs found

    Doctor of Philosophy

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    dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations

    A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

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    Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

    Speed estimation using single loop detector outputs

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    Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology—Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way

    New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

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    As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective
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