Towards a seascape typology. I. Zipf versus Pareto laws

Two data analysis methods, referred to as the Zipf and Pareto methods, initially introduced in economics and linguistics two centuries ago and subsequently used in a wide range of fields (word frequency in languages and literature, human demographics, finance, city formation, genomics and physics), are described and proposed here as a potential tool to classify space–time patterns in marine ecology. The aim of this paper is, first, to present the theoretical bases of Zipf and Pareto laws, and to demonstrate that they are strictly equivalent. In that way, we provide a one-to-one correspondence between their characteristic exponents and argue that the choice of technique is a matter of convenience. Second, we argue that the appeal of this technique is that it is assumption-free for the distribution of the data and regularity of sampling interval, as well as being extremely easy to implement. Finally, in order to allow marine ecologists to identify and classify any structure in their data sets, we provide a step by step overview of the characteristic shapes expected for Zipf's law for the cases of randomness, power law behavior, power law behavior contaminated by internal and external noise, and competing power laws illustrated on the basis of typical ecological situations such as mixing processes involving non-interacting and interacting species, phytoplankton growth processes and differential grazing by zooplankton.     

Development of real-world driving cycle: Case study of Pune,India

The critical component of all emission models is a driving cycle representing the traffic behaviour. Although Indian driving cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban driving cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the driving cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the driving cycle is constructed considering five important parameters of the time–space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The driving cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing driving cycles.     

Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach.     

Residual circulation in the Ría de Muros (NW Spain): A 3D numerical model study

The residual circulation of the Ría de Muros, a large coastal embayment in NW Spain, are studied using a three-dimensional baroclinic finite-difference model. The driving forces considered by the model include the tide, winds, river inflows and density forcing at the open boundary. In situ data of current velocity and direction, water level, wind velocity and direction, river discharge, and temperature and salinity are used for model validation. Simulated and observed time series of water level and current velocity are in good agreement. Once validated, the model is applied to compute the residual circulation induced by the relevant agents of the ría hydrodynamics—the tide, an upwelling-favourable wind characteristic of spring and summer, a downwelling-favourable wind typical of winter, and freshwater inflows associated with high river runoff. The resulting residual circulation differ notably. The tide does not generate significant residual flows except in the inner ría. By contrast, winds and river discharges induce important residual flows throughout; in the middle and outer ría they generate a 3D residual circulation pattern which renders the conventional two-layer scheme of estuarine circulation too simplistic in this case. Thus, this first application of a 3D numerical model to the Ría de Muros sheds new light on its fundamental hydrodynamics.     

Development and evaluation of a knowledge-based system for traffic congestion management and control

This paper describes a real-time knowledge-based system (KBS) for decision support to Traffic Operation Center personnel in the selection of integrated traffic control plans after the occurrence of non-recurring congestion, on freeway and arterial networks. The uniqueness of the system, called TCM, lies in its ability to cooperate with the operator, by handling different sources of input data and inferred knowledge, and providing an explanation of its reasoning process. A data fusion algorithm for the analysis of congestion allows to represent and interpret different types of data, with various levels of reliability and uncertainty, to provide a clear assessment of traffic conditions. An efficient algorithm for the selection of control plans determines alternative traffic control responses. These are proposed to an operator, along with an explanation of the reasoning process that led to their development and an estimation of their expected effect on traffic. The validation of the system, which is one of only few examples of validation of a KBS in transportation, demonstrates the validity of the approach. The evaluation results, in a simulated environment demonstrate the ability of TCM to reduce congestion, through the formulation of traffic diversion and control schemes.     

A study of the Alboran sea mesoscale system by means of empirical orthogonal function decomposition of satellite data

This paper presents the results of a combined empirical orthogonal function (EOF) analysis of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data and sea-viewing wide field-of-view sensor (SeaWiFS) chlorophyll concentration data over the Alboran Sea (Western Mediterranean), covering a period of 1 year (November 1997–October 1998). The aim of this study is to go beyond the limited temporal extent of available in situ measurements by inferring the temporal and spatial variability of the Alboran Gyre system from long temporal series of satellite observations, in order to gain insight on the interactions between the circulation and the biological activity in the system. In this context, EOF decomposition permits concise and synoptic representation of the effects of physical and biological phenomena traced by SST and chlorophyll concentration. Thus, it is possible to focus the analysis on the most significant phenomena and to understand better the complex interactions between physics and biology at the mesoscale. The results of the EOF analysis of AVHRR-SST and SeaWiFS-chlorophyll concentration data are presented and discussed in detail. These improve and complement the knowledge acquired during the in situ observational campaigns of the MAST-III Observations and Modelling of Eddy scale Geostrophic and Ageostrophic motion (OMEGA) Project.     

A behavioural car-following model for computer simulation

The ability to predict the response of a vehicle in a stream of traffic to the behaviour of its predecessor is important in estimating what effect changes to the driving environment will have on traffic flow. Various proposed to explain this behaviour have different strengths and weaknesses. The paper constructs a new model for the response of the following vehicle based on the assumption that each driver sets limits to his desired braking and acceleration rates. The parameters in the model correspond directly to obvious characteristics of driver behaviour and the paper goes on to show that when realistic values are assigned to the parameters in a simulation, the model reproduces the characteristics of real traffic flow.     

A model of traffic dispersion from a congested road

The study of traffic flow dynamics is developed by defining and clarifying traffic divergence, continuity, congestion and dispersion. Velocity potential is introduced as a gravity function generated by the interaction of two or more motorists occupying neighbouring points in space and describes interference to continuous traffic flow. The relationship between the potential function and carrying capacity is developed and dispersion, when considered as a random walk, satisfies a diffusion equation. A model of traffic dispersion along a maximum congested road in space and time is presented as eigenfunctions of the velocity potential. This suggests that traffic can be dispersed by a series of quantum steps. A probability density function is introduced to define the probability of locating a motorist in a congestion zone.