Development of an on-line model to predict the in-cylinder residual gas fraction by using the measured intake/exhaust and cylinder pressures

The in-cylinder RGF (residual gas fraction) of internal combustion engines for new combustion concepts, such as CAI (controlled auto ignition) or HCCI (homogenous charged compression ignition), is a major parameter that affects the combustion characteristics. Thus, measurement or prediction of the cycle-by-cycle RGF and investigation into the relation between the RGF and the combustion phenomena are critical issues. However, on-line prediction of the cycle-by-cycle RGF during engine testing is not always practical due to the requirement of expensive, fast response exhaust-gas analyzers and/or theoretical models that are just too slow for application. In this study, an on-line model that can predict the RGF of each engine cycle and cylinder during the experiment in the test cell has been developed. This enhanced model can predict the in-cylinder charge conditions of each engine cycle during the test in three seconds by using the measured dynamic pressures of the intake, exhaust, and cylinder as the boundary conditions. A Fortran77 code was generated to solve the 1-D MOC (method of characteristics). This code was linked to Labview DAQ as a form of DLL (dynamic link library) to obtain three boundary pressures for each cycle. The model was verified at various speeds and valve timings under the CAI mode by comparing the results with those of the commercial code, GT-Power.     

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

This research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. The comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO₂, NO_x, VOC, PM₁₀) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.     

Practical Frequency and Time Optimal Design of Passive Linear Vibration Isolation Mounts

Summary In this paper we examine a linear one-degree of freedom vibration isolator mount. The linearity of the system allows us to analyze its frequency and time response characteristics analytically. Optimal damping and stiffness values for the isolator are obtained by minimizing certain cost functions, which are the Root Mean Square (RMS) of the absolute acceleration and the relative displacement. These RMS cost functions are used to create a design chart for the isolator parameters. This is very useful particularly in the presence of physical constraints such as a limit in relative displacement. The time response of the system for a unit step input is also considered to gain an insight into the transient characteristics of the system. We obtain an optimal value for the damping ratio of the system in order to minimize the transmitted acceleration. Combining the frequency and time response analyses leads to an optimal value for the mount natural frequency and damping ratio satisfying both time and frequency domains. The results are verified numerically using measured acceleration as input.     

Bayesian imputation of non‐chosen attribute values in revealed preference surveys

Obtaining attribute values of non‐chosen alternatives in a revealed preference context is challenging because non‐chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non‐chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non‐chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non‐chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non‐chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones. Copyright © 2012 John Wiley & Sons, Ltd.     

The importance of new‐generation freight terminals for intermodal transport

Manufacturers have been promoting new terminal concepts for several years. They claim more efficient operations, shorter handling times and lower costs compared to conventional operations. However, so far no new‐generation terminals have been implemented, nor is there any intention yet to implement them. This is regrettable, because if new‐generation terminal concepts can achieve what their designers claim, these terminals could significantly improve the competitiveness of intermodal transport. It is expected that new‐generation terminals will perform particularly well in complex bundling networks such as hub and spoke, collection‐distribution and line networks. A static‐process analysis is used to evaluate the claimed terminal performances. The method incorporates the terminal function and the type of bundling network. Specific network situations have been defined for each type of bundling network. This study shows that the new concepts perform better than the reference terminal and shunting in complex bundling operations.     

Transport policy prioritisation for Dublin

Internalisation of the external costs of transport is currently the subject of much debate. Estimation of costs such as those of pollution and congestion is a primary element in any strategy involving policies for use in the internalisation of these costs. The objective of the TRENEN II STRAN project, funded by the EU, was to develop a methodology for estimation of the marginal external costs of transport. The model developed during the project was used in a series of case studies. One of the case studies, that conducted for Dublin, is reported in this paper. A brief summary of the TRENEN approach is presented followed by the results for Dublin produced from policies such as Do Nothing, Uniform pricing (internalising external costs by means of fuel taxation), Congestion Pricing (cordon pricing) and a first-best policy, the Full Optimum where one assumes that the policy maker has perfect pricing instruments available. As one would expect, the model shows that the greatest reduction in traffic level and external costs would occur if it were possible to introduce a highly differentiated and sophisticated pricing system. Increased taxation on fuel is not an efficient policy as it does not address the marginal external costs of congestion in a way that time-differentiated road-use pricing would. The results from testing of the different measures are interesting particularly those relating to parking and the way in which residents within the CBD and commuters to the CBD are dealt with. This revised version was published online in June 2006 with corrections to the Cover Date.     

Shifting urban freight deliveries to the off-peak hours: a review of theory and practice

This paper presents a comprehensive review of the literature on off-peak hour deliveries (OPHD). The review identifies different approaches and policy levers used in the past, such as the laissez-faire approach, a road pricing approach, an incentives approach, and a regulatory approach. The paper also identifies different delivery reception schemes discussed in the literature. The authors complement the theory with a synthesis of pilot tests and the analysis of a set of interviews with practitioners (from the public sector and other organisations) in charge of OPHD programmes. The results from this review show the potential benefits that these programmes could bring about, the challenges faced in the early stages – along with potential solutions – and the significant progress that has been made in this domain in the last decade. According to the review, the results from the pilot tests tend to be positive, suggesting the importance of these programmes to reach more efficient and sustainable transportation systems.     

An exploratory analysis of observed and latent variables affecting intercity train service quality in developing countries

Transportation - Service quality (SQ) and customer satisfaction as perceived by 1037 passengers on intercity train services in Bangladesh were examined using structural equation modeling to explain...     

Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach

Transportation - Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict...     

Stated preference modelling of intra-household decisions: Can you more easily approximate the preference space?

While the study of choices focuses primarily on the individual decision maker, there is growing interest in the examination of the choices made by groups. Much of the research into the choices of multiple decision makers has revealed that they differ significantly to those of individuals. In this study of household vehicle choice we similarly compare individual choices to group choices and support this finding. Consequently any research into choices that involve groups should acquire data from those groups. In this paper we show how this may be done via an interactive agency choice experiment which makes the individual preferences endogenous to the choice of the group. This method and many like it, however, involve significant time, incentive and administrative costs that often make such studies prohibitive. In this paper we also compare another class of model, minimum information group inference, which is designed to provide an overview of the likely group choice and while not having the specificity of detail as other methods, has the advantage of being much easier and cheaper to implement and is perhaps the only methodology that can be employed when it is not feasible for respondents to interact. We find that this method is a good approximation of group choice, mapping the preference space over which group choice is likely to be located.