Coastal Tourist Interest in Value-Added,Aquaculture-Based,Culinary Tourism Opportunities

Despite increasing focus on sustainable aquaculture investment in the U.S. coastal zones, the aquaculture industry continues to face skepticism among consumers due to perceived negative environmental impacts, and health and food safety concerns. Partnering with culinary tourism and local food promotion efforts has potential for improving public outreach about the benefits of aquaculture, especially in coastal destinations where sustainable marine aquaculture and tourism are both occurring. Culinary tourism implies that local foods reflect the local livelihood and culture, and may be important to the tourist's experience. Aquaculture is becoming increasingly valuable to coastal destinations for assuring the steady availability of local seafood to meet demand and retaining commercial fisheries as a means to differentiate themselves to tourists. This study examined the role of subjective knowledge and attitude about aquaculture, and their effect on intention to participate in value-added culinary aquaculture tourism experiences. The study found that tourists' subjective knowledge and attitude about aquaculture influenced intentions to participate in culinary aquaculture tourism. In order for farmers or tour operators to be successful in diversifying with value-added culinary aquaculture, attention needs to be paid to educating consumers, which may be possible through strategic partnerships with tourism organizations, chefs, and restaurants.     

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

Traffic noise abatement: How different pavements, vehicle speeds and traffic densities affect annoyance levels

In this paper, annoyance ratings from traffic noise recorded on cobblestones, dense asphalt, and open asphalt rubber pavements are assessed with regard to car speeds and traffic densities. It was found that cobblestones pavements are the most annoying; also while open asphalt rubber pavement imposes less annoyance than dense asphalt it is not significantly different. Higher car speeds always lead to greater annoyance, as does higher traffic densities. LAeq and LAmax correlate well with annoyance, but loudness is the best predictor. Roughness and sharpness exhibit inconsistent interactions.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Collection, spillback, and dissipation in pedestrian evacuation: A network-based method

We present a method of predicting pedestrian route choice behavior and physical congestion during the evacuation of indoor areas with internal obstacles. Under the proposed method, a network is first constructed by discretizing the space into regular hexagonal cells and giving these cells potentials before a modified cell transmission model is employed to predict the evolution of pedestrian flow in the network over time and space. Several properties of this cell transmission model are explored. The method can be used to predict the evolution of pedestrian flow over time and space in indoor areas with internal obstacles and to investigate the collection, spillback, and dissipation behavior of pedestrians passing through a bottleneck. The cell transmission model is further extended to imitate the movements of multiple flows of pedestrians with different destinations. An algorithm based on generalized cell potential is also developed to assign the pedestrian flow.     

Rapid first-stage tests of on-ship infection

Protection against on-ship infectious disease—whether due to mishap or to harmful purpose—faces special situational problems. Sometimes, when infection levels on board have reached threshold levels, emergency actions are required. Often, the most thorough strategies for responding to threat are not feasible. A rapid first-stage test (RFT) is a fast, minimally invasive procedure used to rule out from possible infection a large percentage of an infection-threatened group. Prevention and control of on-ship infection need to combine various interconnected tactics. When timely criterion tests are not possible, the medical team must adopt fast alternative measures. The methods used to summarize protection against on-ship infectious agents included a scientific literature review and a web search. The fields of the review were maritime, health, and technology sources. Special attention was paid to material dealing with risks and threats of on-ship penetration by infectious agents, on-ship infection prevalence thresholds, and rapid diagnostic screens. The Bayes rule and the law of large numbers were applied to the analysis, for large on-ship populations, of RFT indications of crossing of an infection prevalence threshold. The increasing risk of serious on-ship infection—either accidental or purposeful—calls for a multi-layered protection approach. RFTs are a key part of the outer layer of such a defense. Well-designed and well-administered RFTs provide several advantages for defense against on-ship infection: low-cost, non-invasive, fast, and focuses on a drastically smaller number of infection possibilities.     

Black Boxes - Pathetic progress but some success now in sight

Little has been written about the ship demolition market, an essential element in the supply/demand balance for shipping. Either technical or economic obsolescence may be the cause for scrapping a ship, where the latter is strongly influenced not only by anticipated freight market levels, but the rate at which more efficient ships are being introduced. The scrap value of a ship is a function both of the realizable value of the materials within the ship and cost of demolition. Both are strongly influenced by the cost structures prevailing in the likely country of demolition. The paper explores not only the fundamentals of the ship demolition market, but the trends from the 1960s to the present. The shipbreaking market has moved in that time from being West European-centred to Asian-centred, concentrating for some years in Taiwan, but now shifting to the Indian subcontinent.     

Characteristics of premium transit services that affect mode choice

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.     

Examining the influence of firm performance on business risk-taking and the mediation effect of scale of operations in the container terminal industry

Container shipping and its related service sectors help accelerate globalization of the world economy. This industry has been experiencing rapid growth, prompting container terminal operators to increase their handling capacity in response. Providing container terminal services requires substantial capital investment in physical assets such as cargo handling facilities and information systems. On the other hand, operating container terminals is a long-term investment that typically spans several business cycles. Hence prudent asset management using appropriate tools is critical for container terminal operators to sustain their businesses. Generally, due to risk-adverseness, investors are unwilling to take more risk in their investment unless they can reap a higher return. Contrary to this argument, this study finds no direct influence of better firm performance as a proxy of higher return on business risk-taking by container terminal operators. Instead, scale of operations is positively associated with business risk-taking, suggesting that container terminal operators with a larger scale of operations are willing to take more business risk.