Estimation of the effects of aircraft noise on residential satisfaction

This study assesses the effects of aircraft noise on residential satisfaction, an important indicator of subjective well-being. A structural equation model is specified that estimates the relationships between objective variables, noise annoyance variables and residential satisfaction. Secondary data-analysis is used to estimate the model. The survey was conducted in 1996/1997 among the population living within a 25-km radius of Amsterdam Schiphol, the largest airport in the Netherlands. The effect of aircraft noise annoyance is found to be relatively small. In addition, the objective level of aircraft noise exposure is found to be a better predictor of residential satisfaction than its subjective counterpart. The most important determinants of residential satisfaction are found to be road traffic noise annoyance, age and neighbor noise annoyance.     

Impact of phytoplankton community size on a linked global ocean optical and ecosystem model

We isolated the effect phytoplankton cell size has on varying remote sensing reflectance spectra (R_rs(λ)) in the presence of optically active constituents by using optical and radiative transfer models linked in an offline diagnostic calculation to a global biogeochemical/ecosystem/circulation model with explicit phytoplankton size classes. Two case studies were carried out, each with several scenarios to isolate the effects of chlorophyll concentration, phytoplankton cell size, and size-varying phytoplankton absorption on R_rs(λ). The goal of the study was to determine the relative contribution of phytoplankton cell size and chlorophyll to overall R_rs(λ) and to understand where a standard band ratio algorithm (OC4) may under/overestimate chlorophyll due to R_rs(λ) being significantly affected by phytoplankton size. Phytoplankton cell size was found to contribute secondarily to R_rs(λ) variability and to amplify or dampen the seasonal cycle in R_rs(λ), driven by chlorophyll. Size and chlorophyll were found to change in phase at low to mid-latitudes, but were anti-correlated or poorly correlated at high latitudes. Phytoplankton size effects increased model calculated R_rs(443) in the subtropical ocean during local spring through early fall months in both hemispheres and decreased R_rs(443) in the Northern Hemisphere high latitude regions during local summer to fall months. This study attempts to tease apart when/where variability about the OC4 relationship may be associated with cell size variability. The OC4 algorithm may underestimate [Chl] when the fraction of microplankton is elevated, which occurs in the model simulations during local spring/summer months at high latitudes in both hemispheres.     

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle’s mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.     

Comparing operator and users costs of light rail, heavy rail and bus rapid transit over a radial public transport network

A model to compare three alternative forms of public transport - light rail, heavy rail and bus rapid transit - is developed for an urban network with radial lines emanating from the borders to the city centre. The theoretical framework assumes an operation aimed at minimising the total cost associated with public transport service provision, which encompasses both operator and users costs. The decision variables are the number of lines (network density) and the frequency per period for each mode. This approach has no prejudices a priori in respect of whether a specified delivery scenario is aligned with existing modal reputation. Rather, we establish the conditions under which a specific transit mode should be preferred to another in terms of the operator (supply) and user (demand) side offerings. The model is applied using data from Australian cities, suggesting that in most of the scenarios analysed a high standard bus service is the most cost-effective mode, because it provides lower operator costs (infrastructure, rolling stock and operating cost), access time costs (due to a larger number of lines) and waiting time cost (due to larger frequencies of operation). A rail mode, such as light rail or heavy rail, may have a lower total cost only if it is able to run faster than bus rapid transit, and the difference in speed is enough to outweigh the bus advantage on operator cost and access and waiting times.     

Damage detection in offshore structures using neural networks

This paper discusses the damage detection in offshore jacket platforms subjected to random loads using a combined method of random decrement signature and neural networks. The random decrement technique is used to extract the free decay of the structure from its online response while the structure is in service. The free decay and its time derivative are used as input for a neural network. The output of the neural network is used as an index for damage detection. It has been shown that function N is effective in damage detection in the members of an offshore structure. Experimental studies conducted on a reduced model for a real jacket structure with geometrical scale of 1:30 are used. The applied loads were random loads. Two different load spectra were used: White noise, and Pierson-Moskowitz.     

USS Boone (FFG 28) Emergent Underwater Rudder Replacement

On November 30, 2006, USS Boone (FFG 28) lost all steering control in the Mediterranean Sea due to a lost rudder. Within hours of the casualty, numerous US Navy and contractor activities sprung into action to coordinate and complete an emergent voyage repair to return USS Boone to service to support the Global War on Terrorism. The Navy faced a large hurdle to identify and transport a replacement rudder to Rota, Spain, and prepare and carry out an extremely complex repair procedure. Pearl Harbor Naval Shipyard personnel removed the replacement rudder from USS Crommelin (FFG 37) in Pearl Harbor Naval Shipyard and transported the rudder to Rota, Spain. The Navy's Supervisor of Salvage and Diving developed the repair procedure and coordinated the repair operation, which involved over 30 US Navy and contractor divers. The repair team completed the rudder replacement on December 28, 2006 and returned USS Boone to underway operations.     

Interval estimation in the calibration of certain trip distribution models

This paper argues for interval, rather than point, estimation when calibrating some variants of the trip distribution “gravity” models. Analytic expressions are derived for the approximate asymptotic covariances of least squares and maximum likelihood estimates of the parameters in the impedance function under a variety of conditions. A comparative numerical example, and an application using migration flows, are also presented.     

The utilisation and user characteristics of Uber services in London

ABSTRACT

Ridesourcing services such as Uber are nowadays a common feature within available transport options of many cities around the world (E.g. London & San Francisco). There has been much publicity about the potential impacts of ridesourcing services and how (or if) they should be managed or regulated without an objective understanding of who uses these services and why, as well as its current and future implications for public transport (PT).

Ridesourcing is part of a broader tech-driven, mobile app-based sharing phenomenon – the ‘sharing economy’ – which has disrupted traditional market models and industries, for example, the transport industry, where new players such as Uber have emerged and have quickly become part of the urban transport landscape. Uber has been at the forefront in disrupting the transport sector since its first launch in 2010 (San Francisco, USA). Since its launch, Uber has generated extensive media coverage and debate among policymakers, transport planners and transport authorities on how these services are affecting traditional transport modes such as buses and taxis. However, without objective empirical data – in terms of impacts on trip making characteristics, PT ridership and congestion – policymakers and transport regulators are yet to fully understand the real impacts ridesourcing services are having on the transport network.

This paper is part of broader research that aims to provide insights and empirical-based evidence on how Uber services are used (UberX and Uberpool) in London. A comprehensive survey was undertaken using a detailed questionnaire, issued to UberX and Uberpool users in London to gather detailed data on who uses the Uber services, why they use it and what are the trip purposes, in order to understand Uber user demographics and what effects (if any) Uber services are having on PT usage and trip making characteristics in London. The final findings provide important insights on Uber user demographics, trip purposes, types of trips replaced, impact on car ownership and why travellers use Uber services.     

Accounting for travel time variability in the optimal pricing of cars and buses

A number of studies have shown that in addition to travel time and cost as the common influences on mode, route and departure time choices, travel time variability plays an increasingly important role, especially in the presence of traffic congestion on roads and crowding on public transport. The dominant focus of modelling and implementation of optimal pricing that incorporates trip time variability has been in the context of road pricing for cars. The main objective of this paper is to introduce a non-trivial extension to the existing literature on optimal pricing in a multimodal setting, building in the role of travel time variability as a source of disutility for car and bus users. We estimate the effect of variability in travel time and bus headway on optimal prices (i.e., tolls for cars and fares for buses) and optimal bus capacity (i.e., frequencies and size) accounting for crowding on buses, under a social welfare maximisation framework. Travel time variability is included by adopting the well-known mean–variance model, using an empirical relationship between the mean and standard deviation of travel times. We illustrate our model with an application to a highly congested corridor with cars, buses and walking as travel alternatives in Sydney, Australia. There are three main findings that have immediate policy implications: (i) including travel time variability results in higher optimal car tolls and substantial increases in toll revenue, while optimal bus fares remain almost unchanged; (ii) when bus headways are variable, the inclusion of travel time variability as a source of disutility for users yields higher optimal bus frequencies; and (iii) including both travel time variability and crowding discomfort leads to higher optimal bus sizes.