Assessment of large transport infrastructure projects: The CBA-DK model

This paper presents a newly developed decision support model to assess transport infrastructure projects: CBA-DK. The model combines use of conventional cost–benefit analysis to produce aggregated single point estimates, with quantitative risk analysis using Monte Carlo simulation to produce interval results. The embedded uncertainties within traditional CBA such as ex-ante based investment costs and travel time savings are of particular concern. The paper investigates these two impacts in terms of the Optimism Bias principle which is used to take account of the underestimation of construction costs and the overestimation of travel time savings. The CBA-DK methodological approach has been used to apply suitable probability distribution functions on the uncertain parameters, thus resulting in feasibility risk assessment moving from point to interval results. The proposed assessment model makes use of both deterministic and stochastic based information. Decision support as illustrated in this paper aims to provide assistance in the development and ultimately the choice of action, while accounting for the uncertainties surrounding transport appraisal schemes. The modelling framework is illustrated by the use of a case study appraising airport and runway alternatives in the capital of Greenland – Nuuk. The case study has been conducted in cooperation with the Home Rule Authorities of Greenland.     

Voyage-based statistical fuel consumption models of ocean-going container ships in Korea

ABSTRACT

Accurately estimating fuel consumption of ships is crucial for shipping companies, port authorities, and environmental protection agencies. The bottom-up approach is becoming increasingly popular because it can estimate ship fuel consumption by accounting for ship activity conditions, such as changes in voyage speed, time, and distance; however, its use is still limited when estimating ship fuel consumption. Ship-specific information, such as the daily fuel consumption rate for main and auxiliary engines for every vessel, is expensive to gather, and generally not collected from private shipping companies. To address this research gap, we develop simplified and composite ship fuel consumption models for ocean-going container ships by size using a regression model. To estimate the fuel consumption models for container ships, we rely on ship activity data, including average speed and sailing time, distance, and actual fuel consumption for main and auxiliary engines. This information is obtained from a major container shipping company in Korea. We estimate and validate the parameters associated with fuel consumption for five different container ship sizes, all of which are smaller than the Post-Panamax container ship (15,000 TEU and above).     

Shape optimization of rubber isolators in automotive cooling modules for the maximization of vibration isolation and fatigue life

Rubber isolators are mounted between a cooling module and a carrier to isolate the car body from vibration due to the rotation of the cooling fan. The isolators should be durable against fatigue loads originating from fan rotation and road disturbance. Thus, the design of rubber isolators is required to maximize both vibration isolation and fatigue life. In this study, the shapes of the rubber isolators are optimally designed using a process integration and design optimization (PIDO) tool that integrates the various computer-aided engineering (CAE) tools necessary for vibration and fatigue analyses, automates the analysis procedure and optimizes the design solution. In this study, we use CAE models correlated to the experimental results. A regression-based sequential approximate optimizer incorporating Process Integration, Automation and Optimization (PIAnO), a commercial PIDO tool, is employed to handle numerically noisy responses with respect to the variation in design variables. Using the analysis and design procedure established in this study, we successfully obtained the optimal shapes of the rubber isolators in two different cooling modules; these shapes clearly have better vibration isolation capability and fatigue lives than those of the baseline designs used in industry.     

Smooth motion control of the adaptive cruise control system by a virtual lead vehicle

The adaptive cruise control system maintains the appropriate distance to the lead vehicle when the lead vehicle exists and maintains the desired speed when no lead vehicle is detected. A virtual lead vehicle scheme is introduced to make the switching between the speed control algorithm and the distance control algorithm unnecessary and simplify the structure of the control system. The speed and the position of the virtual vehicle can be decided by the control system according to the current situation. Smoother responses are achieved by the virtual lead vehicle scheme compared to the conventional mode switching scheme. This method is also shown to provide a good reaction for when a lead vehicle cuts in or out. A linear quadratic controller with variable weights is suggested to control the virtual lead vehicle. This scheme shows improved performance in terms of passenger comfort and fuel efficiency of the host vehicle.     

Expanding transmission capacity of can systems using dual communication channels with kalman prediction

The controller area network (CAN) protocol is widely used for in-vehicle network (IVN) systems, and many automotive companies also use the CAN in chassis network systems. However, the increasing number of electronic control units (ECUs) dictated by the need for more intelligent and fuel-efficient functions requires an IVN system with a greater transmission capacity and less network delay. Automotive companies have tried several approaches such as segmenting CAN systems and developing time-triggered protocols. This paper presents a practical method for increasing the transmission capacity and reducing the network delay in CAN systems using dual communication channels with a traffic-balancing algorithm based on Kalman prediction to forecast the traffic on each channel and allocate frames to the one that is most appropriate. An experimental testbed using commercial microcontrollers with two or more CAN protocol controllers was used to demonstrate the feasibility of the Kalman traffic-balancing algorithm. Experimental results show that the traffic-balancing CAN system with Kalman prediction reduced the transmission delay of all priority messages compared to that of a simple method, such as a channel-switching CAN, without sacrificing the performance for high-priority messages.     

Robust detection of preceding vehicles in crowded traffic conditions

This paper introduces an adaptive scheme for robustly detecting multiple preceding vehicles in crowded traffic conditions. The scheme focuses on issues frequently observed in the interpretation of traffic scenes recorded by cameras installed in vehicles: stable extraction of features and accurate classification in spite of the vehicle??s constant vibrations and dynamic changes in the distance between vehicles. To address these issues, we introduce the concept of integral features and a method of utilizing the scene geometry information. Each of the simple attributes, such as edges, shadows, and symmetry, is compiled in the window confined by the scene geometry to improve the expressiveness and robustness of the extracted features. The scene geometry information that is then estimated from the perspective view is extensively utilized in constituent system components, including not only feature extraction/integration but also neural network-based classification and distance-adaptive clustering. In addition, employing the Kalman filter along with a confidence measure makes the detection and tracking of potential vehicles robust. Experimental results prove that the system employing the proposed scheme detects and tracks multiple vehicles more effectively, even in crowded traffic conditions, with a lower rate of false positives.     

Cooling performance evaluation of height-downsized front end module for pedestrian protection

The front end module (FEM) needs enough space from hood to absorb the energy from any pedestrian collision. The FEM with downsized cooling module for pedestrian protection is important to reduce the severity of pedestrian injury. When a vehicle collision happens, the FEM with downsized cooling module is required to reduce the risk of injury to the upper legs of adults and the heads of children. In this study, the performance of cooling module to cool the engine was investigated under 25% height reduction. The heat dissipation and pressure drop characteristics have been experimentally studied with the variation of coolant flow rate, air inlet velocity and A/C operation (on/off) for the downsized cooling module. The results indicated that the cooling performance was about 94% level compared to that of the conventional cooling module. Therefore, we concluded that the cooling module had a good performance, and expected that the cooling module could meet the same cooling performance as conventional cooling module through optimization of components efficiency. This paper also deals with the development of FEM with downsized cooling module for the cooling performance level in vehicles. In the test of front end module??s heat dissipation performance, the prototype presented about 15% decrease under the conditions of all the vehicle speeds than that of conventional one.     

Drowsy behavior detection based on driving information

Drowsy behavior is more likely to occur in sleep-deprived drivers. Individuals’ drowsy behavior detection technology should be developed to prevent drowsiness related crashes. Driving information such as acceleration, steering angle and velocity, and physiological signals of drivers such as electroencephalogram (EEG), and eye tracking are adopted in present drowsy behavior detection technologies. However, it is difficult to measure physiological signal, and eye tracking requires complex experiment equipment. As a result, driving information is adopted for drowsy driving detection. In order to achieve this purpose, driving experiment is performed for obtaining driving information through driving simulator. Moreover, this paper investigates effects of using different input parameter combinations, which is consisted of lateral acceleration, longitudinal acceleration, and steering angles with different time window sizes (i.e. 4 s, 10 s, 20 s, 30 s, 60 s), on drowsy driving detection using random forest algorithm. 20 s-size datasets using parameter combination of accelerations in lateral and longitudinal directions, compared to the other combination cases of driving information such as steering angles combined with lateral and longitudinal acceleration, steering angles only, longitudinal acceleration only, and lateral acceleration only, is considered the most effective information for drivers’ drowsy behavior detection. Moreover, comparing to ANN algorithm, RF algorithm performs better on processing complex input data for drowsy behavior detection. The results, which reveal high accuracy 84.8 % on drowsy driving behavior detection, can be applied on condition of operating real vehicles.     

Using a k-means clustering algorithm to examine patterns of pedestrian involved crashes in Honolulu,Hawaii

The purpose of this paper is twofold: 1) to describe a statistical technique known as K-means clustering in term of its advantages and disadvantages in safety research; and, 2) to use this method to analyze spatial patterns of pedestrian-involved crashes in Honolulu. K-means, a partitioning clustering technique, provides a powerful tool for analyzing and visualizing spatial patterns. While there are other techniques, one of the advantages of the K-means approach is that it is a well established technique that has been used for many different applications other than traffic safety. In this paper, we compare it to hierarchical clustering techniques and suggest that both are useful in the arsenal of spatial analytic tools for safety research.     

Fatigue and buckling analysis of automotive components considering forming and welding effects

Some vehicle components are developed by setting target weights to the gram level at their design stages to accomplish a lightweight design. Recently, there have been many studies that have focused on lightweight design through the use of ultra-high-strength steels. However, a lightweight design can face many challenges if the reliability of the analysis is not also secured at the design stage. Such challenges include difficulties in coupled analyses when the file formats are different among PAM-STAMP, ABAQUS, and NASTRAN. In this study, we developed a mapping interface that enables mapping between the file formats of various software programs. Buckling analysis was coupled to the forming analysis, in which pre-strain test data were applied in considering the material’s strain hardening, to evaluate the rigidity of the front lower control arm that controls the wheels and transfers loads. The influence of forming effects on endurance was evaluated, and residual stresses around the weld zone were calculated. A comparison of experimental and analytical results indicated that the proposed analysis was highly reliable.