Development of simplified analytical methods for predicting the response of offshore structures to blast and fire loading

This paper discusses structural response to blast loading and temperature changes in fires. Both extreme events can be modelled using a range of analytical techniques from simple methodologies to the very complicated finite element analysis. The relatively simple techniques are appropriate for this type of problem and degree of accuracy required given the level of uncertainty in the loading. Given specific boundary conditions and physical properties, the methodologies proposed in the paper provide as good estimates of the dynamic response to blast loading and temperature increases in fires as the more sophisticated finite element method. Experimental data compares well with the analytical results.     

Study of algorithms of new slender ship theory of wave resistance

In this paper, Noblesse‘s New Slender-Ship Wave-Making Theory was investigated numerically. Detailed expressions of zeroth and 1st order wave resistance have been derived and calculation programs have also been compiled. In the single and double integral terms of Green function, the kernel function of wave resistance expression, special function expansion method and Chebyshev polynomials approach have been adopted respectively, which greatly simplify the calculation and increase the convergence speed.     

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990–2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.     

Impact of family in-home quality time on person travel demand

This paper introduces the concept of Primary Family Priority Time (PFPT), which represents a high priority household decision to spend time together for in-home activities. PFPT is incorporated into a fully specified and operational activity based discrete choice model system for Copenhagen, called COMPAS, using the DaySim software platform. Structural tests and estimation results identify two important findings. First, PFPT has a place high in the model hierarchy, and second, strong interactions exist between PFPT and the other day level activity components of the model system. Forecasts are generated for a road pricing and congestion scenario by COMPAS and a comparison version of the model system that excludes PFPT. COMPAS with PFPT exhibits less mode changing and time-of-day shifting in response to pricing and congestion than the comparison version.     

Precise estimation of connections of metro passengers from Smart Card data

The aim of this study is to estimate both the physical and schedule-based connections of metro passengers from their entry and exit times at the gates and the stations, a data set available from Smart Card transactions in a majority of train networks. By examining the Smart Card data, we will observe a set of transit behaviors of metro passengers, which is manifested by the time intervals that identifies the boarding, transferring, or alighting train at a station. The authenticity of the time intervals is ensured by separating a set of passengers whose trip has a unique connection that is predominantly better by all respects than any alternative connection. Since the connections of such passengers, known as reference passengers, can be readily determined and hence their gate times and stations can be used to derive reliable time intervals. To detect an unknown path of a passenger, the proposed method checks, for each alternative connection, if it admits a sequence of boarding, middle train(s), and alighting trains, whose time intervals are all consistent with the gate times and stations of the passenger, a necessary condition of a true connection. Tested on weekly 32 million trips, the proposed method detected unique connections satisfying the necessary condition, which are, therefore, most likely true physical and schedule-based connections in 92.6 and 83.4 %, respectively, of the cases.     

Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility

Over the past decades research on travel mode choice has evolved from work that is informed by utility theory, examining the effects of objective determinants, to studies incorporating more subjective variables such as habits and attitudes. Recently, the way people perceive their travel has been analyzed with transportation-oriented scales of subjective well-being, and particularly the satisfaction with travel scale. However, studies analyzing the link between travel mode choice (i.e., decision utility) and travel satisfaction (i.e., experienced utility) are limited. In this paper we will focus on the relation between mode choice and travel satisfaction for leisure trips (with travel-related attitudes and the built environment as explanatory variables) of study participants in urban and suburban neighborhoods in the city of Ghent, Belgium. It is shown that the built environment and travel-related attitudes—both important explanatory variables of travel mode choice—and mode choice itself affect travel satisfaction. Public transit users perceive their travel most negatively, while active travel results in the highest levels of travel satisfaction. Surprisingly, suburban dwellers perceive their travel more positively than urban dwellers, for all travel modes.     

Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?

Current signal systems for managing road traffic in many urban areas around the world lack a coordinated approach to detecting the spatial and temporal evolution of congestion across control regions within city networks. This severely inhibits these systems’ ability to detect reliably, on a strategic level, the onset of congestion and implement effective preventative action. As traffic is a time-dependent and non-linear system, Chaos Theory is a prime candidate for application to Urban Traffic Control (UTC) to improve congestion and pollution management. Previous applications have been restricted to relatively uncomplicated motorway and inter-urban networks, arguably where the associated problems of congestion and vehicle emissions are less severe, due to a general unavailability of high-resolution temporal and spatial data that preserve the variability in short-term traffic patterns required for Chaos Theory to work to its full potential. This paper argues that this restriction can now be overcome due to the emergence of new sources of high-resolution data and large data storage capabilities. Consequently, this opens up the real possibility for a new generation of UTC systems that are better able to detect the dynamic states of traffic and therefore more effectively prevent the onset of traffic congestion in urban areas worldwide.     

Serum levels of RBP4 might not be determined by diabetes mellitus but by kidney function and renal replacement therapy

It has been reported that retinol-binding protein 4(RBP4) is associated to adiposity,insulin resistance,and type 2 diabetes.Meanwhile,circulating RBP4 levels are also affected by renal function.The aim of the present study is to investigate whether serum levels of RBP4 are primarily associated with different stages of chronic kidney disease(CKD) or type 2 diabetes,if there is more potential relevance between RBP4 and renal replacement therapy.The serum levels of RBP4 were assessed by commercial competitive enzyme-linked immunosorbent assay(ELISA)kit in 212 patients with the CKD stages 1—5 and in 24 healthy controls,while its correlation with clinical and metabolic parameters was analyzed.The serum level of RBP4 had a strong correlation with estimated glomerular filtration rate(e GFR)(P 0.001).Stratified by e GFR and treatment,no more differences in RBP4 serum concentration were detected between type 2 diabetic and non-diabetic subjects [CKD stages 1—5,non-dialysis(ND),hemodialysis(HD) and peritoneal dialysis(PD);P 0.05 for all].The elevation of RBP4 become higher in HD than in PD and ND in CKD5 patients(P = 0.008 and P = 0.04,respectively),while there was no significant difference between PD and ND groups.Multivariate linear regression analysis demonstrated three independent predictors of e GFR(β =-0.676,P 0.001),C-reactive protein(CRP)(β =-0.573,P 0.001) and creatine(β = 0.509,P = 0.024) in the study population.The study results demonstrated that the serum level of RBP4 was negatively related to the e GFR,whether diabetes mellitus(DM) affected the blood concentration of RBP4 or not.And the serum level of RBP4 exhibited significant difference in different renal replacement therapies.