A mathematical model for acceleration phase of aerodynamically alleviated catamarans and minimizing the time needed to reach final speed

Racing catamarans use aerodynamic alleviation concept which in existing extreme ground effect significantly enhances the performance. Beside design measures, controlling strategies may be employed as convenient solutions to improve the performance and address concerns regarding poor stability in these crafts. Being of substantial importance for a racing catamaran to reach the final speed as soon as possible, this study attempts to find the optimal form of changing the drive angle (as control variable) to minimize its acceleration time. In this regard, a mathematical model is developed for forward acceleration phase of these catamarans based on empirical and theoretical methods. Then the formulation and solution algorithm for the time-optimal problem are described according to an indirect method. Results for a representative racing craft have been presented in uncontrolled and controlled conditions. Problem in controlled condition has been solved without and with a predefined constraint regarding stability margin. Optimal controlling of the drive angle without stability constraint during the acceleration results in 40 % reduction in time required to reach the speed of 110 kn and 14 % reduction in resistance at this speed in comparison to the uncontrolled case. Addition of the stability constraint changes optimal solution for drive angle and causes craft trim angle follow a decreasing trend at higher speeds.     

Utilization patterns of park and ride facilities among Kuala Lumpur commuters

A parking utilization survey was undertaken with the main objective of analyzing and comparing the daily workday utilization patterns of two main park and ride stations within the Kuala Lumpur conurbation. This study also aimed to gauge the level of usage of the park and ride facilities. The findings showed that the overall utilization pattern of the facilities was very high with a utilization rate of between 80 and 95%. The stations, however, recorded a contrasting accumulation pattern. The study further revealed that most of the rail-based suburban park and ride users were long term parkers. The results of this study are comparable to results of similar studies in Seoul, Calgary, Tyne and Wear and others. Since parking availability is an important factor that has influence on the behavior of a park and ride user, more accurate information relating to the supply and demand of the park and ride facility will assist in planning new transport infrastructure.

Modelling commuting mode choice with explicit consideration of carpool in the choice set formation

This article investigates the carpool mode choice option in the context of overall commuting mode choice preferences. The article uses a hybrid discrete choice modelling technique to jointly model the consideration of carpooling in the choice set formation as well as commuting mode choice together with the response bias corrections through the accommodation of measurement equations. A cross-nested error structure for the econometric formulation is used to capture correlations among various commuting modes and carpool consideration in the choice set. Empirical models are estimated using a data set collected through a week-long commuter survey in Edmonton, Alberta. The empirical model reveals many behavioural details of commuting mode choice and carpooling. Interestingly, it reveals that interactions between various Travel Demand Management (TDM) tools with the carpooling option can be different at different level of decision making (choice set formation level and final choice making level).     

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.     

Numerical calculations of propeller shaft loads on azimuth propulsors in oblique inflow

This paper evaluates various computational methods used to compute propeller performance, hydrodynamic side force and bending moment applied to an azimuth propulsor propeller shaft in oblique inflow. The two non-viscous models used are the BEM method and the blade element momentum theory (BEMT). RANS calculations are used to compute the loads on the propeller and the nominal wake velocity from the thruster body to be used in the BEMT model. The effect of the ship hull is also considered in the calculation by implementing the measured nominal wake of a ship hull at different propeller azimuthal positions. All the models are compared and validated against the experimental results, and the discussions are presented.     

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.     

Effects of incorporating latent and attitudinal information in mode choice models

Abstract

Hybrid choice modelling approaches allow latent variables in mode choice utility functions to be addressed. However, defining attitude and behavior as latent variables is influenced by the researcher's assumptions. Therefore, it is better to capture the effects of latent behavioral and attitudinal factors as latent variables than defining behaviors and attitudes per se. This article uses a hybrid choice model for capturing such latent effects, which will herein be referred to as modal captivity effects in commuting mode choice. Latent modal captivity refers to the unobserved and apparently unexplained attraction towards a specific mode of transportation that is resulting from latent attitude and behavior of passengers in addition to the urban transportation system. In empirical models, the latent modal captivity variables are explained as functions of different observed variables. Empirical models show significant improvement in fitting observed data as well as improved understanding of travel behavior.     

Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data

Arrival processes are important inputs to many transportation system functions, such as vehicle prepositioning, taxi dispatch, bus holding strategies, and dynamic pricing. We conduct a comprehensive survey of the literature which shows that many transport systems employ basic homogeneous arrival process models or static nonhomogeneous processes. We conduct an empirical experiment to compare five state of the art arrival process short term prediction models using a common transportation system data set: New York taxi passenger pickups in 2013. Pickup data is split between 672 observations for model estimation and 96 observations for validation. From our experiment, we obtain evidence to support a recent model called FM‐IntGARCH, which is able to combine the benefits of both time series models and discrete count processes. Using a set of seven performance metrics from the literature, FM‐IntGARCH is shown to outperform the offline models—seasonal factor method, piecewise linear model—as well as the online models—ARIMA, Gaussian Cox process. Implications for operating data‐driven “smart” transit systems and urban informatics are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Hydrodynamic Performance Prediction of Stepped Planing Craft Using CFD and ANNs

In the present paper, the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics (CFD) analysis. For this purpose, th...