5,159 research outputs found

    A formulation of the relaxation phenomenon for lane changing dynamics in an arbitrary car following model

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    Lane changing dynamics are an important part of traffic microsimulation and are vital for modeling weaving sections and merge bottlenecks. However, there is often much more emphasis placed on car following and gap acceptance models, whereas lane changing dynamics such as tactical, cooperation, and relaxation models receive comparatively little attention. This paper develops a general relaxation model which can be applied to an arbitrary parametric or nonparametric microsimulation model. The relaxation model modifies car following dynamics after a lane change, when vehicles can be far from equilibrium. Relaxation prevents car following models from reacting too strongly to the changes in space headway caused by lane changing, leading to more accurate and realistic simulated trajectories. We also show that relaxation is necessary for correctly simulating traffic breakdown with realistic values of capacity drop

    Optimal Ventilation Control in Complex Urban Tunnels with Multi-Point Pollutant Discharge

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    We propose an optimal ventilation control model for complex urban vehicular tunnels with distributed pollutant discharge points. The control problem is formulated as a nonlinear integer program that aims to minimize ventilation energy cost while meeting multiple air quality control requirements inside the tunnel and at discharge points. Based on the steady-state solutions to tunnel aerodynamics equations, we propose a reduced form model for air velocities as explicit functions of ventilation decision variables and traffic density. A compact parameterization of this model helps to show that tunnel airflows can be estimated using standard linear regression techniques. The steady-state pollutant dispersion model is then incorporated for the derivation of optimal pollutant discharge control strategies. A case study of a new urban tunnel in Hangzhou, China demonstrates that the scheduling of fan operations based on the proposed optimization model can effectively achieve different air quality control objectives under varying traffic intensity.U.S. Department of Transportation 69A355174711

    Twenty-five years of random asset exchange modeling

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    The last twenty-five years have seen the development of a significant literature within the subfield of econophysics which attempts to model economic inequality as an emergent property of stochastic interactions among ensembles of agents. In this article, the literature surrounding this approach to the study of wealth and income distributions, henceforth the "random asset exchange" literature following the terminology of Sinha (2003), is thoroughly reviewed for the first time. The foundational papers of Dragulescu and Yakovenko (2000), Chakraborti and Chakrabarti (2000), and Bouchaud and Mezard (2000) are discussed in detail, and principal canonical models within the random asset exchange literature are established. The most common variations upon these canonical models are enumerated, and significant papers within each kind of modification are introduced. The successes of such models, as well as the limitations of their underlying assumptions, are discussed, and it is argued that the literature should move in the direction of more explicit representations of economic structure and processes to acquire greater explanatory power

    Variance Reduction for Score Functions Using Optimal Baselines

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    Many problems involve the use of models which learn probability distributions or incorporate randomness in some way. In such problems, because computing the true expected gradient may be intractable, a gradient estimator is used to update the model parameters. When the model parameters directly affect a probability distribution, the gradient estimator will involve score function terms. This paper studies baselines, a variance reduction technique for score functions. Motivated primarily by reinforcement learning, we derive for the first time an expression for the optimal state-dependent baseline, the baseline which results in a gradient estimator with minimum variance. Although we show that there exist examples where the optimal baseline may be arbitrarily better than a value function baseline, we find that the value function baseline usually performs similarly to an optimal baseline in terms of variance reduction. Moreover, the value function can also be used for bootstrapping estimators of the return, leading to additional variance reduction. Our results give new insight and justification for why value function baselines and the generalized advantage estimator (GAE) work well in practice

    Cost benefit analysis of various California renewable portfolio standard targets: is a 33% RPS optimal?

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    Renewable Portfolio Standards (RPSs') require that a certain fraction of the electricity generated for a given region be produced from renewable resources. California's RPS mandates that by 2020, 33% of the electricity sold in the state must be generated from renewables. Such mandates have important implications for the electricity sector as well as for the whole society. In this paper, we estimate the costs and benefits of varying 2020 California RPS targets on electricity prices, greenhouse gas (GHG) emissions, criteria pollutant emissions, the electricity generation mix, the labor market, renewable investment decisions, and social welfare. We have extended the RPS Calculator model, developed by Energy and Environmental Economics (E3) Inc., to account for distributions of fuel and generation costs, to incorporate demand functions, and to estimate the effects of RPS targets on GHG emissions, criteria pollutant emissions, and employment. The results of our modeling provide the following policy insights: (1) the average 2020 electricity price increases as the RPS target rises, with values ranging between 0.152and0.152 and 0.175/kWh (2008 dollars) for the 20% RPS to 50% RPS, respectively; (2) the 33% and 50% RPS targets decrease the GHG emissions by about 17.6 and 35.8 million metric tons of carbon dioxide equivalent (MMTCO2e) relative to the 20% RPS; (3) the GHG emission reduction costs of the RPS options are high (71to71 to 94 per ton) relative to results from policy options other than RPS or prices that are common in the carbon markets; and (4) a lower target (e.g., a 27% RPS) provides higher social welfare than the 33% RPS (mandate) under low and moderate CO2 social costs (lower than $35/ton); while a higher RPS target (e.g., 50%) is more beneficial when using high CO2 social costs or with rapid renewable technology diffusion. However, under all studied scenarios, the mandated 33% RPS for California would not provide the best cost/benefit values among the possible targets and would not maximize the net social benefit objective

    Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

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    The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.Peer ReviewedPostprint (author's final draft

    Structure and magnetic properties of sputtered thin films of Fe0.79Ge0.21

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    Films of Fe0.79Ge0.21 with thicknesses of 300 nm were synthesized by ion beam sputtering, and were annealed at temperatures from 200 to 550 °C. The materials were characterized by x-ray diffractometry, Mössbauer spectrometry, vibrating sample magnetometry, ferromagnetic resonance spectrometry, and electrical resistivity measurements. The as-prepared materials comprised chemically disordered bcc crystallites of sizes less than 20 nm, and were found to have a distribution of internal strains. Upon annealing at temperatures of 250 °C and below, there occurred strain relaxation, some evolution of short range chemical order, and an improvement in soft magnetic properties. The coercive field was a minimum for the sample annealed at 250 °C. Crystallite growth occurred at higher annealing temperatures, accompanied by a transition in several measured parameters from those of ultrafine grained materials to those typical of polycrystalline materials. This trend can be explained with the random anisotropy model. Mössbauer and magnetization measurements indicated that the Ge atoms behave as magnetic holes. The 57Fe hyperfine magnetic field distribution, and its change during chemical ordering, can be calculated approximately with a model of magnetic response. The large local isomer shifts at 57Fe atoms near Ge atoms suggest that a local depletion of 4s conduction electron density should be incorporated into the model
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