Bayesian kriging regression for the accuracy improvement of beam modeled T-junctions of buses and coaches structures with a methodology based on FEM behavioral analysis

This paper has been developed in the framework of the alternative beam T-junction solution previously propoused by the authors (Alcalá et al., 2013), with the scope of optimizing the behavior of buses and coaches upper structures modeled with beam type elements. The alternative beam T-junction model proposed by the authors, had a total of six elastic elements at the junction level allowing to modify the localized rigidity of any modeled T-junctions, therefore improving their behavior and avoiding the well known rigidity issue these elements have. A fundamental aspect behind the use of these alternative beam models is related to the necessity of correctly estimating the rigidity values of the elastic elements for each modeled T-junction. In this context, we propose applying a surrogate model for the reference calculations which is based on statistical Bayesian kriging predictors. Statistical predictions have the added value, with respect to deterministic solutions, of providing a quantification of uncertainty. The development of the kriging predictors has required the application of a statistical methodology including computer experiment design, computation of moments of inertia, graphical and ANOVA type sensitivity analysis and Monte Carlo computation of Bayesian inference. The results of the application have been a very satisfactory trade-off between accuracy of approximation (prediction) and computational cost. The surrogate kriging models also provide an useful tool for a better understanding of the input-output relationships involved in the reference computations.     

Evaluation of safety benefits of automatic crash information notification systems on freeways

The automatic crash information notification system (ACINS) is an effective technology to enhance the potential for saving crash victims by reducing the crash response time (CRT) of emergency medical services. Shorter CRT results in a greater potential to save the lives and to alleviate the severity of injuries for crash victims. To fully operate the ACINS, reliable assessments of the safety benefits would be needed for justifying public investment. This study proposed a methodology for quantifying the effectiveness of the ACINS and applied the methodology to the Korean freeway system. The proposed methodology consists of three steps. The first step is to develop a statistical model for predicting injury severity of crash victims using ordered logistic regression. The second step is to estimate the amount of reduced CRT by applying ACINS. The effectiveness of the ACINS, which are defined as the number of reduced fatalities and severe injuries, were evaluated with the consideration of the market penetration rate (MPR) in third step. It has been found that approximately 9.4–15.4% of fatalities can be reduced with 100% MPR when the proposed methodology is applied to 2011 freeway crash data. The outcomes of this study support decision making for public investments and for establishing relevant traffic safety policies.     

Nonlinear sliding mode observer for exhaust manifold pressure estimation in a light-duty diesel engine

In this paper, a sliding mode observer is proposed to estimate exhaust pressure for a diesel engine equipped with variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems. Since the exhaust pressure directly affects generation of the VGT power and the EGR rate in the cylinder, the exhaust pressure information is important for precise control of the VGT and EGR systems. In order to estimate the exhaust pressure accurately, a dynamic model of intake and exhaust pressure was derived. Furthermore, the mass flow rate and temperature of the air system in the diesel engines were modeled by consideration of physical phenomena and the thermodynamic law. Based on the developed models, a nonlinear sliding mode observer was designed to estimate the exhaust pressure. Convergence of the proposed observer was verified by the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated with the engine experiments. The experimental results show that the observer estimates the exhaust pressure accurately in both steady and transient engine operating conditions. Moreover, as a case study, the estimation results of the proposed observer could be applied for detecting a fault of the EGR system. The fault of the EGR system was detected precisely using the estimation result and the limited sensor information in mass-produced engines.     

Accounting for travel time variability in the optimal pricing of cars and buses

A number of studies have shown that in addition to travel time and cost as the common influences on mode, route and departure time choices, travel time variability plays an increasingly important role, especially in the presence of traffic congestion on roads and crowding on public transport. The dominant focus of modelling and implementation of optimal pricing that incorporates trip time variability has been in the context of road pricing for cars. The main objective of this paper is to introduce a non-trivial extension to the existing literature on optimal pricing in a multimodal setting, building in the role of travel time variability as a source of disutility for car and bus users. We estimate the effect of variability in travel time and bus headway on optimal prices (i.e., tolls for cars and fares for buses) and optimal bus capacity (i.e., frequencies and size) accounting for crowding on buses, under a social welfare maximisation framework. Travel time variability is included by adopting the well-known mean–variance model, using an empirical relationship between the mean and standard deviation of travel times. We illustrate our model with an application to a highly congested corridor with cars, buses and walking as travel alternatives in Sydney, Australia. There are three main findings that have immediate policy implications: (i) including travel time variability results in higher optimal car tolls and substantial increases in toll revenue, while optimal bus fares remain almost unchanged; (ii) when bus headways are variable, the inclusion of travel time variability as a source of disutility for users yields higher optimal bus frequencies; and (iii) including both travel time variability and crowding discomfort leads to higher optimal bus sizes.     

A meta-model of vehicle ownership choice parameters

This paper builds a meta-model of vehicle ownership choice parameters to predict how their values might vary across extended periods as a function of macroeconomic variables. Multinomial logit models of vehicle ownership are estimated from repeated cross-sectional data between 1971 and 1996 for large urban centers in Ontario. Three specifications are tested: a varying constants (VC) model where the alternative specific constants are allowed to vary each year; a varying scales (VS) model where the scale parameter varies instead; and a varying scales and constants model. The estimated parameters are then regressed on macroeconomic variables (e.g., employment rate, gas prices, etc.). The regressions yield good fit and statistically significant results, suggesting that changes in the macroeconomic environment influence household decision making over time, and that macroeconomic information could potentially help predict how model parameters evolve. This implies that the common assumption of holding parameters constant across forecast horizons could potentially be relaxed. Furthermore, using a separate validation dataset, the predictive power of the VC and VS models outperform conventional approaches providing further evidence that pooling data from multiple periods could also produce more robust models.     

Heterogeneity assumptions in the specification of bargaining models: a study of household level trade-offs between commuting time and salary

With many real world decisions being made in conjunction with other decision makers, or single agent decisions having an influence on other members of the decision maker’s immediate entourage, there is strong interest in studying the relative weight assigned to different agents in such contexts. In the present paper, we focus on the case of one member of a two person household being asked to make choices affecting the travel time and salary of both members. We highlight the presence of significant heterogeneity across individuals not just in their underlying sensitivities, but also in the relative weight they assign to their partner, and show how this weight varies across attributes. This is in contrast to existing work which uses weights assigned to individual agents at the level of the overall utility rather than for individual attributes. We also show clear evidence of a risk of confounding between heterogeneity in marginal sensitivities and heterogeneity in the weights assigned to each member. We show how this can lead to misleading model results, and argue that this may also explain past results showing bargaining or weight parameters outside the usual [0,1] range in more traditional joint decision making contexts. In terms of substantive results, we find that male respondents place more weight on their partner’s travel time, while female respondents place more weight on their partner’s salary.     

Comparative performance analysis of European airports by means of extended data envelopment analysis

Data envelopment analysis (DEA) has become an established approach for analyzing and comparing efficiency results of corporate organizations or economic agents. It has also found wide application in comparative studies on airport efficiency. The standard DEA approach to comparative airport efficiency analysis has two feeble elements, viz. a methodological weakness and a substantive weakness. The methodological weakness originates from the choice of uniform efficiency improvement assessment, whereas the substantive weakness in airport efficiency analysis concerns the insufficient attention for short‐term and long‐term adjustment possibilities in the production inputs determining airport efficiency. The present paper aims to address both flaws by doing the following: (i) designing a data‐instigated distance friction minimization (DFM) model as a generalization of the standard Banker–Charnes–Cooper model with a view to the development of a more appropriate efficiency improvement projection model in the Banker–Charnes–Cooper version of DEA and (ii) including as factor inputs also lumpy or rigid factors that are characterized by short‐term indivisibility or inertia (and hence not suitable for short‐run flexible adjustment in new efficiency stages), as is the case for runways of airports. This so‐called fixed factor case will be included in the DFM submodel of the DEA. This extended DEA—with a DFM and a fixed factor component—will be applied to a comparative performance analysis of several major airports in Europe. Finally, our comparative study on airport efficiency analysis will be extended by incorporating also the added value of the presence of shopping facilities at airports for their relative economic performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Fatigue life estimation of injection mold core using simulation-based approach

The purpose of this study is to present a simulation-based approach capable of predicting the fatigue life of a mold core with the help of CAE technology based on injection molding simulation, core stress analysis and fatigue life prediction. Injection molding simulation is performed in order to obtain the non-uniform pressure acting on a mold core due to the polymer melt flow during injection molding process. Subsequently, the melt pressure profile is used as loading condition for core stress analysis via one-way coupling technique. The lifetime of a mold core is then predicted by fatigue analysis with stress-life approach. For verification of simulation-based approach to the fatigue life prediction, tests were also performed.     

Wall-wetting model based method for air-fuel ratio transient control in gasoline engines with dual injection system

Modern gasoline engines with dual injection system improve combustion efficiency by using the two injection commands simultaneously. Due to the different dynamics in the two injection paths, the transient air-fuel ratio (A/F) performance when the injection authority is changed remarkably affects the emission performance of the engine system. For this kind of engines, this paper proposes a model-based design approach that gives a dynamic feedforward compensation for achieving good transient A/F performance. Actually, this work centers on discussing the mean-value model parameters for characterizing the wall-wetting phenomenon when the port injection authority is changed under different engine operation conditions. Moreover, from the view of practice, taking into account the event-based physics of the engine system, significant calibration for the model parameters that are employed for achieving the feedforward compensation is investigated experimentally. Finally, experimental validation results demonstrate the improvement of the transient A/F performance by using the control scheme.     

Development of estimation algorithms for vehicle’s mass and road grade

In controlling the longitudinal motion of electrified vehicles such as hybrid vehicles and PHEV (Plug-in Hybrid Electric Vehicles), the variation of the driving resistance loads (or driving loads) such as road grade and actual vehicle mass, is the most influential factor which limits the control performance. Measuring the driving load is not impossible, but it is costly since additional sensors have to be mounted on the vehicle. In this study, methods for estimating vehicle mass and road grade are designed to compensate for the driving loads. The proposed methods are verified using simulation tools and then evaluated experimentally.