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 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.     

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.     

Spatio-Temporal Population Density and Spatial Dynamic Spectrum Allocation

Introduction The scarcity of available radio spectrum limits theextension of the modern wireless communication. Oneapproach to alleviate this limitation is the dynamicspectrum allocation (DSA) at network level, which isable to adapt to the spatial and temporal variation intraffic load[1]. The concept of DSA was first presentedin the DARPA (Defense Advanced Research ProjectsAgency) XG Program[2], with the aim to increase thespectral efficiency by spectrum sharing among radios.Due to the…     

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.     

An approximate method for the surge response of the tension leg platform

The solution for the Duffing equation in anonlinear vibration problem is studied in this paper. Clearly,in the case of the perturb parameter being a larger value, thetraditional perturbation method is no longer valid but theHomotopy Perturbation Method （HPM） is applicable usually.HPM is used to solve the weak and strong nonlineardifferential equations for finding the perturbed frequency ofthe response. The obtained frequencies via HPM and theapproximate method have good accordance for weak andstrong nonlinear differential equations. Additionally, thecalculated responses by use of the approximate method arecompared with the responses obtained from the Numericalmethod in the time history of the response and phase plane.The results represent ~ood accordance between them.     

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...     

An appropriate unidirectional wave spectrum model for the Strait of Hormuz

The objective of this paper is to introduce an appropriate unidirectional wave spectrum model for the Strait of Hormuz. The research is focused on assessing performance of standard wave spectrum models in the region. By evaluating such models based on valuable measurement data recently published, the calibration procedure has been conducted on such standard models to reach a better concordance between a modified standard spectral model and observed field spectra. The calibration is performed initially with respect to four distinct directions related to four available measurement stations. So, it results in four sets of coefficients for a nominated model. Next, it is continued to reach just one model insensitive to directions. Results clearly showed that the International Towing Tank Conference (ITTC) model is more appropriate than Joint North Sea Wave Project (JONSWAP) and Pierson and Moskowitz (PM) models in this area, even without any calibration. However, modifications have been successful on improving the conformity of the model.     

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.