A study on time constraints and task deviations at sea leading to accidents – a cultural-historical perspective

The high frequency of maritime accidents and incidents occurring at sea has been a major challenge for the maritime industry in the last decades. The majority of these accidents are attributed to seafarers’ poor performance. This, despite the fact that the international maritime domain continues to adopt and update conventions regulating maritime safety to mitigate these accidents from occurring. In this paper, utilising a qualitative research approach, we show through a socio-cultural contextual perspective that time constraints as a major influencing factor in causing task deviations at sea which leads to dangerous situations. We analyse how some of the present barriers in place to prevent accidents at sea are in effect prompt seafarers who are working under time pressure to deviate from their task. Moreover, the paper discusses the social constituents such as job insecurity and the seafarers’ viewpoint towards the ship operators’ commitment to safe ship operations are crucial in motivating seafarers’ deviating from the task at hand when faced with time pressure.     

Discrete mode-choice analysis of urban travel demand by the Analytic Hierarchy Process

This paper develops a new procedure for the problem of multimodal urban corridor travel demand estimation by using the Analytic Hierarchy Process (AHP). Certain conceptual and operational features of the AHP are common to the discrete choice theory-based modeling approach. Whereas the computational and data requirements of standard discrete choice models are immense, the proposed AHP approach deals efficiently with multidimensionality, nested demand structure and discrete travel decision making behavior. The paper concludes by summarizing the AHP-aided, step-by-step procedure for metropolitan travel demand (modal split) estimation.     

Real-time estimation of the road bank and grade angles with unknown input observers

This paper proposes an approach for the estimation of the road angles independent from the road friction conditions. The method employs unknown input observers on the roll and pitch dynamics of the vehicle. The correlation between the road angle rates and the pitch/roll rates of the vehicle is also investigated to increase the accuracy. Dynamic fault thresholds are implemented in the algorithm to ensure reliable estimation of the vehicle body and road angles. Performance of the proposed approach in reliable estimation of the road angles is experimentally demonstrated through vehicle road tests. Road test experiments include various driving scenarios on different road conditions to thoroughly validate the proposed approach.     

A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors

Drivers’ behavior evaluation is one of the most important problems in intelligent transportation systems and driver assistant systems. It has a great influence on driving safety and fuel consumption. One of the challenges in this regard is the modeling perspective to treat with uncertainty in judgments about driving behaviors. Really, assessing a single maneuver with a rigid threshold leads to a weak judgment for driving evaluation. To fill this gap, a novel neuro-fuzzy system is proposed to classify the driving behaviors based on their similarities to fuzzy patterns when all of the various maneuvers are stated with some fuzzy numbers. These patterns are also fuzzy numbers and they are extracted from statistical analysis on the smartphone sensors data. Our driving evaluation system consists of three processes. Firstly, it detects the type of all of the maneuvers through the driving period, by using a multi-layer perceptron neural network. Secondly, it extracts a new feature based on the acceleration and assigns three fuzzy numbers to driver’s lane change, turn and U-turn maneuvers. Thirdly, it determines the similarity between these three fuzzy numbers and the fuzzy patterns to evaluate the safe and the aggressive driving scores. To validate this model, Driver’s Angry Score (DAS) questionnaires are used. Results show that the fusion of Inertial Measurement Unit (IMU) sensors of smartphones is enough for the proposed driving evaluation system. Accuracy of this system is 87% without using GPS and GIS data and this system is independent of smartphones and vehicles types.     

A numerical investigation into strength and deformation characteristics of preloaded tubular members under lateral impact loads

This paper presents the results of a numerical investigation into the structural behaviour of preloaded tubular members under lateral impact loads by means of finite element method. The lateral load represents a statically modelled impact from collision between tubular member and a solid rectangular indenter. Three different kinds of end conditions have been applied to the model and the effects of boundary conditions are investigated. Also, the effect of preloading on the buckling strength as well as the ultimate strength for laterally impacted tubes is assessed and it will be shown that preloading and position of applying force directly affect these strengths. In other words, by increasing in the amount of preloading, ultimate strength reduces and member tends to collapse under lower amounts of loads. The influence of the position of applying lateral load has also been addressed and relevant results will be discussed. In order to verify the performance of numerical model, the results have been examined against an available experimental test.     

A Fuzzy Logic Direct Yaw-Moment Control System for All-Wheel-Drive Electric Vehicles

Summary In-wheel-motors are revolutionary new electric drive systems that can be housed in vehicle wheel assemblies. Such E-wheels permit packaging flexibility by eliminating the central drive motor and the associated transmission and driveline components, including the transmission, the differential, the universal joints and the drive shaft. Apart from many advantages of such a system, unequalled independent wheel control allows vehicle dynamic improvement to assist the driver in enhancing cornering and straight-line stability on slippery roads and in adverse ground conditions. In this paper a Fuzzy logic driver-assist stability system for all-wheel-drive electric vehicles based on a yaw reference DYC is introduced. The system assists the driver with path correction, thus enhancing cornering and straight-line stability and providing enhanced safety. A feed-forward neural network is employed to generate the required yaw rate reference. The neural net maps the vehicle speed and the steering angle to give the yaw rate reference. The vehicle true speed is estimated using a multi-sensor data fusion method. Data from wheel sensors and an embedded accelerometer are fed into an estimator, where a Fuzzy logic system decides which input is more reliable. The efficiency of the proposed system is approved by conducting a computer simulation. The proposed control system is an effective and easy to implement method to enhance the stability of all-wheel-drive electric vehicles.     

Modeling to Predict Rollover Threat of Tractor-Semitrailers

Summary A predictive model to determine a rollover threat index associated with tractor-semitrailers is proposed. The purpose of this model is to predict the rollover threat sufficiently in advance of the actual event so as to enable the driver to react accordingly. The predictive model is established using simple roll-plane models of the vehicle sprung and unsprung masses in conjunction with online vehicle parameter identification. Using this predictive model, the predicted Load Transfer Ratio (LTR) for the trailer axle can be determined as the rollover threat index. The proposed predictive model and the associated parameter-identification algorithm are verified using a 12-degree-of-freedom vehicle model. It is shown that the identified parameter values are close to the actual ones used in detailed simulation study. Similarly it is shown that the predicted values of the LTR are close to the simulated ones, and hence the proposed approach is potentially suitable as the basis for application in rollover threat assessment.     

A computational investigation of the effects of both-sides general corrosion on the buckling/plastic collapse behaviour and strength of stiffened plates

This paper presents the results of an investigation into the post-buckling behaviour and ultimate strength of imperfect corroded stiffened steel plates used in ships and other marine-related structures. A series of elastic–plastic large deflection finite element analyses is performed on stiffened steel plates suffering general corrosion wastage with random distribution. General corrosion is introduced into the finite element models using a random thickness surface model. The effects of corroded stiffened plate parameters on the post-buckling and ultimate strengths are evaluated in detail. The stiffeners of different symmetrical or unsymmetrical cross-sections are introduced into the models for analysis. Some distinctions are explored and highlighted between the behaviours of steel plates suffering general corrosion in unstiffened and stiffened cases. Finally, a proposal is given in order to simulate the average stress–average strain relationship of stiffened steel plates having both-surface general corrosion wastage.     

Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals

This paper focuses on developing mathematical optimization models for the train timetabling problem with respect to dynamic travel demand and capacity constraints. The train scheduling models presented in this paper aim to minimize passenger waiting times at public transit terminals. Linear and non-linear formulations of the problem are presented. The non-linear formulation is then improved through introducing service frequency variables. Heuristic rules are suggested and embedded in the improved non-linear formulation to reduce the computational time effort needed to find the upper bound. The effectiveness of the proposed train timetabling models is illustrated through the application to an underground urban rail line in the city of Tehran. The results demonstrate the effectiveness of the proposed demand-oriented train timetabling models, in terms of decreasing passenger waiting times. Compared to the baseline and regular timetables, total waiting time is reduced by 6.36% and 10.55% respectively, through the proposed mathematical optimization models.