Effects of longer heavy vehicles on traffic congestion

Two-lane, "microscopic" (vehicle-by-vehicle) simulations of motorway traffic are developed using existing models and validated using measured data from the M25 motorway. An energy consumption model is also built in, which takes the logged trajectories of simulated vehicles as drive-cycles. The simulations are used to investigate the effects on motorway congestion and fuel consumption if "longer and/or heavier vehicles" (LHVs) were to be permitted in the UK. Baseline scenarios are simulated with traffic composed of cars, light goods vehicles and standard heavy goods vehicles (HGVs). A proportion of conventional articulated HGVs is then replaced by a smaller number of LHVs carrying the same total payload mass and volume. Four LHV configurations are investigated: an 18.75 m, 46 t longer semi-trailer (LST); 25.25 m, 50 t and 60 t B-doubles and a 34 m, 82 t A-double. Metrics for congestion, freight fleet energy consumption and car energy consumption are defined for comparing the scenarios. Finally, variation of take-up level and LHV engine power for the LST and A-double are investigated. It is concluded that: (a) LHVs should reduce congestion particularly in dense traffic, however, a low mean proportion of freight traffic on UK roads and low take-up levels will limit this effect to be almost negligible; (b) LHVs can significantly improve the energy efficiency of freight fleets, giving up to a 23% reduction in fleet energy consumption at high take-up levels; (c) the small reduction in congestion caused by LHVs could improve the fuel consumption of other road users by up to 3% in dense traffic, however in free-flowing traffic an opposite effect occurs due to higher vehicle speeds and aerodynamic losses; and (d) underpowered LHVs have potential to generate severe congestion, however current manufacturers' recommendations appear suitable. © 2013 IMechE.

Diagnosing Network-Wide Traffic Anomalies

Anomalies are unusual and significant changes in a network's traffic levels, which can often involve multiple links. Diagnosing anomalies is critical for both network operators and end users. It is a difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data. In this paper we propose a general method to diagnose anomalies. This method is based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions. We show that this separation can be performed effectively using Principal Component Analysis. Using only simple traffic measurements from links, we study volume anomalies and show that the method can: (1) accurately detect when a volume anomaly is occurring; (2) correctly identify the underlying origin-destination (OD) flow which is the source of the anomaly; and (3) accurately estimate the amount of traffic involved in the anomalous OD flow. We evaluate the method's ability to diagnose (i.e., detect, identify, and quantify) both existing and synthetically injected volume anomalies in real traffic from two backbone networks. Our method consistently diagnoses the largest volume anomalies, and does so with a very low false alarm rate.

Mining Anomalies Using Traffic Feature Distributions

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

Desenvolvimento de um perfilômetro laser para determinação da área e volume de contato entre o pneu e o solo: Cristiano Alberto de Paula. -

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparação entre um modelo teórico e o real, no investimento em construção de estradas de uso florestal, relacionado com o volume de madeira transportado

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effects of traffic control on the soil physical quality and the cultivation of sugarcane

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo sobre o potencial de alguns latossolos da região do Vale do Paraíba, estado de São Paulo, para uso em camadas estruturais de pavimentos de baixo volume de tráfego

The main purpose of this research is to raise, through lab rehearsals, the geometric properties of some oxisols from the region of Vale do Paraíba as well as comparing them to demands prescribed by Technical Specifications ESP08/05 of the Road Department of the State of Paraná, for sub-base or fine grained lateritic soil base (SAFL) and in the Technical Specification ET-DEP00/006 of the Road Department of the State of São Paulo (DER/SP) for subbase or crushed stone base. According to the results that were obtained it will be feasible to appraise the usage of these sorts of soils in structure layers of low-weight traffic pavements

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.

Osmotically Induced Cell Volume Changes Alter Anterograde and Retrograde Transport, Golgi Structure, and COPI Dissociation

Physiological conditions that impinge on constitutive traffic and affect organelle structure are not known. We report that osmotically induced cell volume changes, which are known to occur under a variety of conditions, rapidly inhibited endoplasmic reticulum (ER)-to-Golgi transport in mammalian cells. Both ER export and ER Golgi intermediate compartment (ERGIC)-to-Golgi trafficking steps were blocked, but retrograde transport was active, and it mediated ERGIC and Golgi collapse into the ER. Extensive tubulation and relatively rapid Golgi resident redistribution were observed under hypo-osmotic conditions, whereas a slower redistribution of the same markers, without apparent tubulation, was observed under hyperosmotic conditions. The osmotic stress response correlated with the perturbation of COPI function, because both hypo- and hyperosmotic conditions slowed brefeldin A-induced dissociation of βCOP from Golgi membranes. Remarkably, Golgi residents reemerged after several hours of sustained incubation in hypotonic or hypertonic medium. Reemergence was independent of new protein synthesis but required PKC, an activity known to mediate cell volume recovery. Taken together these results indicate the existence of a coupling between cell volume and constitutive traffic that impacts organelle structure through independent effects on anterograde and retrograde flow and that involves, in part, modulation of COPI function.

National survey of speeding and other unsafe driving actions. Volume I: methodology.

National Highway Traffic Safety Administration, Office of Research and Traffic Records, Washington, D.C.

National survey of speeding and other unsafe driving actions. Volume II: driver attitudes and behavior.

National Highway Traffic Safety Administration, Office of Research and Traffic Records, Washington, D.C.

Evaluation of the Intelligent Cruise Control System. Volume I: study results.

National Highway Traffic Safety Administration, Washington, D.C.

Evaluation of New York State's Mandatory Occupant Restraint Law. Volume II - attitudinal surveys of licensed drivers in New York state. Final report.

National Highway Traffic Safety Administration, Washington, D.C.