Development and validation of energy demand uncertainty model for electric city buses

The prediction of electric city bus energy demand is crucial in order to estimate operating costs and to size components such as the battery and charging systems. Unfortunately, there are unpredictable dynamic factors that can cause variation in the energy demand, particularly concerning driver choices and traffic levels. The impact of these factors on energy demand has been difficult to study since fast computing sufficiently accurate dynamic simulation models have been missing, properly quantified in terms of relevant inputs which contribute to energy demand. The objective is to develop and validate a novel electric city bus model for computing the energy demand, to study the nature and impact of various input factors. The developed equation-based model predicted real-world electric city bus energy consumption within 0.1% error. The most crucial unmeasurable input factors were the driven bus route, the number of stops, the elevation profile, the traffic level and the driving style. This understanding can be used to specify routes and stops for a given electric bus battery capacity. Worst-case scenarios are also necessary for electric bus sizing analysis. The best- and worst-case levels of the crucial factors were identified and with them synthetic best- and worst-case speed profiles were generated to demonstrate their effect to the energy demand. While the measured nominal consumption was 0.70 kWh/km, the computed range of variation was between 0.19 kWh/km and 1.34 kWh/km. For design sizing purposes, an electric city bus can have a broad range of possible energy consumption rates due to mission condition variations.     

Measuring uncertainty in discrete choice travel demand forecasting models

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.     

Estimating the most likely space–time paths,dwell times and path uncertainties from vehicle trajectory data: A time geographic method

Global Positioning System and other location-based services record vehicles’ spatial locations at discrete time stamps. Considering these recorded locations in space with given specific time stamps, this paper proposes a novel time-dependent graph model to estimate their likely space–time paths and their uncertainties within a transportation network. The proposed model adopts theories in time geography and produces the feasible network–time paths, the expected link travel times and dwell times at possible intermediate stops. A dynamic programming algorithm implements the model for both offline and real-time applications. To estimate the uncertainty, this paper also develops a method based on the potential path area for all feasible network–time paths. This paper uses a set of real-world trajectory data to illustrate the proposed model, prove the accuracy of estimated results and demonstrate the computational efficiency of the estimation algorithm.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

Stochastic capacity expansion models for airport facilities

It is important and also challenging to plan airport facilities to meet future traffic needs in a rapidly changing environment, which is characterized by various uncertainties. One key issue in airport facility development is that facility performance functions (delay levels as functions of capacity utilization rates) are nonlinear, which complicates the solution method design. Potential demand fluctuations in a deregulated aviation market add another dimension to the decision making process. To solve this problem, a deterministic total cost minimization model is proposed and then extended into stochastic programs, by including uncertainties in traffic forecasts. After the exploration of properties of the delay cost function, an Outer-Approximation (OA) technique which is superior to the existing discrete approximation is designed. After model enhancements, an efficient solution framework based on the OA technique is used to solve the model to its global optimality by interactively generating upper and lower bounds to the objective. Computational tests demonstrate the validity of developed models and efficiency of proposed algorithms. The total cost is reduced by 18.8% with the stochastic program in the numerical example.     

船模水动力学试验中几何参数的不确定度分析研究

自从1992年ITTC-QS工作组致函ITTC技术委员会提请ITTC各成员单位依照ANSI/ASME PTC 19.1开展测量不确定度评定以来,不确定度分析一直是热门议题.AIAA于1999年发布了风洞试验不确定度分析的指南.ITTC从1999年起逐步发布了一系列关于船模试验不确定度分析的规程.在这些规程中,由模型加工误差引起的水动力系数不确定度分量一般是通过水动力系数的(无量纲)表达式进行分析计算的.这种分析,从数学上是合理的,但从物理上分析和依据工程判断,有些则是相当不合理的,甚至是分析中的一个误区.文章列举了若干实例对此进行了阐述,并提出:对于因水动力无量纲化而引入的几何参数,其不确定度影响分量不能简单地依据所采用的无量纲表达式进行分析评定,而是要基于水动力学的分析进行合理的评定.     

Uncertainty quantification in vehicle dynamics

ABSTRACT

This paper presents a review of various works that highlight the importance of introducing the variability of the road-track/vehicle system into dynamic simulations as soon as this latter is meant to be predictive. The first section of the paper presents the Uncertainty Quantification, Verification and Validation method (UQ-VV). This latter proposes tools to model uncertainties, to associate a confidence to the prediction of quantities of interest and to estimate the probability of occurrence of different scenarios. The method is illustrated by various examples mainly from the rail domain but also from the road sector. The second section summarises application examples of predictive modelling, robust optimisation and calibration.     

Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management

Aquatic biogeochemical models have been an indispensable tool for addressing pressing environmental issues, e.g., understanding oceanic response to climate change, elucidation of the interplay between plankton dynamics and atmospheric CO₂ levels, and examination of alternative management schemes for eutrophication control. Their ability to form the scientific basis for environmental management decisions can be undermined by the underlying structural and parametric uncertainty. In this study, we outline how we can attain realistic predictive links between management actions and ecosystem response through a probabilistic framework that accommodates rigorous uncertainty analysis of a variety of error sources, i.e., measurement error, parameter uncertainty, discrepancy between model and natural system. Because model uncertainty analysis essentially aims to quantify the joint probability distribution of model parameters and to make inference about this distribution, we believe that the iterative nature of Bayes' Theorem is a logical means to incorporate existing knowledge and update the joint distribution as new information becomes available. The statistical methodology begins with the characterization of parameter uncertainty in the form of probability distributions, then water quality data are used to update the distributions, and yield posterior parameter estimates along with predictive uncertainty bounds. Our illustration is based on a six state variable (nitrate, ammonium, dissolved organic nitrogen, phytoplankton, zooplankton, and bacteria) ecological model developed for gaining insight into the mechanisms that drive plankton dynamics in a coastal embayment; the Gulf of Gera, Island of Lesvos, Greece. The lack of analytical expressions for the posterior parameter distributions was overcome using Markov chain Monte Carlo simulations; a convenient way to obtain representative samples of parameter values. The Bayesian calibration resulted in realistic reproduction of the key temporal patterns of the system, offered insights into the degree of information the data contain about model inputs, and also allowed the quantification of the dependence structure among the parameter estimates. Finally, our study uses two synthetic datasets to examine the ability of the updated model to provide estimates of predictive uncertainty for water quality variables of environmental management interest.     

Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle

Uncertainty is inherent in major infrastructure projects, but public decision-making for such projects ignores it. We investigate the uncertainty about the future effects of tearing down the Alaskan Way Viaduct in downtown Seattle, using an integrated model of housing, jobs, land use and transportation, on outcomes including average commute times. Our methodology combines the urban simulation model UrbanSim with the regional transportation model. We assess uncertainty using Bayesian melding, yielding a full predictive distribution of average commute times on 22 different routes in 2020. Of these routes, 14 do not include the viaduct and eight do. For the 14 base routes that do not include the viaduct, the predictive distributions overlap substantially, and so there is no indication that removing the viaduct would increase commute times for these routes. For each of the eight routes that do include the viaduct, the 95% predictive interval for the difference in average travel times between the two scenarios includes zero, so there is not strong statistical support for the conclusion that removing the viaduct would lead to any increase in travel times. However, the median predicted increase is positive for each of these routes, with an average of 6 min, suggesting that there may be some measurable increase in travel time for drivers that use the viaduct as a core component of their commute.     

汽车碰撞事故再现估算速度的不确定度分析

不确定度分析是一种考察人试验测量输入值得到计算结果输出之间不确定度传播的数学方法。本文中将不确定度分析方法引入汽车碰撞事故再现的模型算法中，推算碰撞前速度的不确定度，给出了在汽车正面斜碰撞事故再现中的一个应用实例。通过对计算结果的分析，初步得到了不确定度的传播规律，并指出了相关问题和发展方向。