The influence of personality on acceptability of sustainable transport policies

Previous research has shown that fairness, infringement on freedom, and perceived effectiveness are determinants of transport pricing acceptability. In the present study we investigate determinants of acceptability of environmental (carbon) taxation for which trust in government and environmental concern are additional determinants. Carbon taxation is an extension of fuel taxes and may thus be viewed as transport pricing. Our main focus is on the role played by personality traits. Structural equation modeling reveals that acceptability is related to the personality traits extraversion, agreeableness, and conscientiousness. Extraverted individuals have higher levels of trust in government which leads to higher acceptability. Also correlations between agreeableness and conscientiousness as well as environmental problem awareness and personal norm are observed. We discuss strategies for effective marketing of transportation policies considering how acceptability is related to personality traits.     

An experimental study of the effect of low-noise wheels in reducing noise and vibration

Wheel/rail interaction is a major source of railway noise. A low-noise wheel structure is developed and its effect on noise reduction is investigated. This low-noise wheel employs a rubber material inserted into the steel rim or mounted on the wheel surface. The low-noise wheel has low stiffness and a high-damping ratio compared to a solid wheel. Measurement shows that it reduces rolling and squealing. It turns out that a subway line with the proposed wheel could reduce its interior noise level by 4–5 dB(A) and vehicle vibration level by 7–8 dB. While the proposed structure seems promising in noise reduction for railway vehicles, the endurance and cost effectiveness of the low-noise wheel are yet to be verified.     

Analysis of a regenerative braking system for Hybrid Electric Vehicles using an Electro-Mechanical Brake

The regenerative braking system of the Hybrid Electric Vehicle (HEV) is a key technology that can improve fuel efficiency by 20∼50%, depending on motor size. In the regenerative braking system, the electronically controlled brake subsystem that directs the braking forces into four wheels independently is indispensable. This technology is currently found in the Electronic Stability Program (ESP) and in Vehicle Dynamic Control (VDC). As braking technologies progress toward brake-by-wire systems, the development of Electro-Mechanical Brake (EMB) systems will be very important in the improvement of both fuel consumption and vehicle safety. This paper investigates the modeling and simulation of EMB systems for HEVs. The HEV powertrain was modeled to include the internal combustion engine, electric motor, battery and transmission. The performance simulation for the regenerative braking system of the HEV was performed using MATLAB/Simulink. The control performance of the EMB system was evaluated via the simulation of the regenerative braking of the HEV during various driving conditions.     

Belt routing and safety assessment through computer simulation

Most of the research on safety belt systems has involved crash simulation that only considered a dynamic human model. However, belt routing analysis, usually known as comfort level estimation, is also an important factor in safety belt design, considering that serious injuries of the abdominal region result from the infiltration of a belt into the neck or the chest. Thus, safety belt evaluations using kinematic human models are also needed. In this paper, a belt fit simulation method is suggested. Using the proposed process, both comfort and safety analyses can be performed under the same conditions continuously, and thus the safety belt design parameters, such as the location of anchor points, dummy posture and etc., can be evaluated. In conclusion, this computer process enables a belt system design to reduce injuries.     

Implementation of discharging/charging current sensorless state-of-charge estimator reflecting cell-to-cell variations in lithium-ion series battery packs

This paper offers novel insights to the design and implementation of an innovative state-of-charge (SOC) estimator for the lithium-ion (Li-Ion) series battery pack. The most interesting feature of this approach is that it can utilize information from each filtered terminal voltage of the Li-Ion cells connected in series for SOC estimation of the battery pack. Without actual sensing each discharging/charging current (DCC) applied to the Li-Ion cells, it is possible to extract each DCC estimation from the corresponding filtered terminal voltages with an equivalent electrical circuit model (EECM) identification of all Li-Ion cells in the battery pack. There are two advantages to SOC estimation of the battery pack with this approach. First, the proposal can be implemented simply and effectively, reducing the computational steps required for SOC estimation. By reducing computational steps, the proposal is expected to be more cost-effective. Second, the approach guarantees an improved SOC performance, even if the battery pack results in inevitable cell-to-cell variation among Li-Ion cells. Accordingly, there are fewer differences to previously estimated DCCs among Li-Ion cells. Specifically, all values from the estimated DCCs are properly compensated for by simultaneous parameter modification according to each cell’s electrochemical characteristics. Experimental results clearly demonstrate that our DCC sensorless SOC estimator provides robust SOC performance for the battery pack. This approach considered an experimental battery pack (12S1P) connected in series using 2.6 Ah LiCoO2 cells produced by Samsung SDI.     

Development of algorithms for commercial vehicle mass and road grade estimation

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.     

Application of a real-coded genetic algorithm for the fitting of a ship hull surface through a single non-uniform B-spline surface

In digital ship-design processes, surface modeling needs to be as accurate as possible for effectiveness in ship production as well as numerical analysis of the performance. Traditionally, the form of a ship hull is constructed from a set of cross-sectional data. This approach entails difficulties in the cross-sectional spacing and accuracy of the characteristic curves, such as the stern and bow profiles, deck side line, bottom tangential line, and unconnected curves. Genetic algorithms (GAs) have attracted increasing attention as a multimodal optimization solution for surface reconstruction that enable construction of a single non-uniform B-spline (NUB) surface at the initial stage of ship design with constraints such as knuckles, discontinuity conditions, and bulbous bows with high curvatures, . The first, simultaneous multi-fitting GA determines the boundary curves, such as the stem and stern profiles, and finds the common knot values for both curves. Similarly, the same GA technique is applied for other boundary curves at the bottom and the deck. The second GA is employed to fit the interior data points after the boundary curves are fitted. The encoded design variables for surface construction are the locations of the vertices and the knot values. Those variables are modified for improving the surface quality until a predefined degree of precision is attained. In four instances of application, the GA technique developed in this research has been shown to provide good, single, NUB surfaces with high efficiency. In the early design stage, a single NUB surface is more convenient for performance visualization and finite-element methods. It can be readily translated into many CAD/CAM packages, which facilitate the smooth transition of data across the different design stages.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.