Dealing with traffic risk in Latin American toll roads

Análisis de los sistemas de mitigación del riesgo de tráfico en autopistas de peaje en diferentes países de Latinoamérica. This paper presents a cross-country analysis of traffic risk allocation in road concessions of Latin America. It shows that some countries such as Chile, Colombia, and Peru have been greatly concerned with mitigating traffic risk, either by putting into practice public guarantees, implementing flexible term concessions, or through availability payment concessions; whereas other countries such as Mexico and Brazil have assigned traffic risk to the private concessionaire by using fixed-term concession contracts without any traffic guarantees. Based on an analysis of data from 1990 to 2010, the paper finds that shifting traffic risk from the concessionaire to the government or users was not confined to the riskiest projects, as one might expect. The analysis also suggests that the implementation of traffic risk mitigation mechanisms in Latin American toll roads has not been very successful in reducing renegotiation rates or in increasing the number of bidders in the tenders

Sight distance studies on roads: influence of digital elevation models and roadside elements

Sight distance plays an important role in road traffic safety. Two types of Digital Elevation Models (DEMs) are utilized for the estimation of available sight distance in roads: Digital Terrain Models (DTMs) and Digital Surface Models (DSMs). DTMs, which represent the bare ground surface, are commonly used to determine available sight distance at the design stage. Additionally, the use of DSMs provides further information about elements by the roadsides such as trees, buildings, walls or even traffic signals which may reduce available sight distance. This document analyses the influence of three classes of DEMs in available sight distance estimation. For this purpose, diverse roads within the Region of Madrid (Spain) have been studied using software based on geographic information systems. The study evidences the influence of using each DEM in the outcome as well as the pros and cons of using each model.

Dimitris Pikionis: Roads of the Times

Dimitris Pikionis: Roads of the Times

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patternsof this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.

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.

On roads not taken in the evolution of protein catalysts: Antibody steroid isomerases that use an enamine mechanism

Reactive immunization has emerged as a new tool for the study of biological catalysis. A powerful application resulted in catalytic antibodies that use an enamine mechanism akin to that used by the class I aldolases. With regard to the evolution of enzyme mechanisms, we investigated the utility of an enamine pathway for the allylic rearrangement exemplified by Δ5-3-ketosteroid isomerase (KSI; EC 5.3.3.1). Our aldolase antibodies were found to catalyze the isomerization of both steroid model compounds and steroids. The kinetic and chemical studies showed that the antibodies afforded rate accelerations up to a factor of 104 by means of an enamine mechanism in which imine formation was the rate-determining step. In light of our observations and the enzyme studies by other workers, we suggest that an enamine pathway could have been an early, viable KSI mechanism. Although this pathway is amenable to optimization for increased catalytic power, it appears that certain factors precluded its evolution in known KSI enzymes.

Computer determination of peptide conformations in water: different roads to structure.

Fragments of proteins (short peptides) that "fold" suggest a mechanism of how complete conformational search in protein folding is avoided. We used a computational method to determine structures of two foldable peptides in explicit water: RVEW and CSVTC. The optimization starts from random structures and no experimental constraints are used. In agreement with NMR data, the simulations find a hydrophobic pair (Val/Trp) in REVW. The structure of CSVTC is induced by a surface water that bridges two amide hydrogens, a drive to structure hypothesized by Ben-Naim [Ben-Naim, A. (1990) J. Chem. Phys. 93, 8196-8210] that is largely ignored in studies of folding. Tendency to structure in short peptide chains suggests a mechanism for the formation of short-range nucleation sites in protein folding.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.