Uncertainty in predictions of oil spill trajectories in a coastal zone

A method is introduced to determine the uncertainties in the predictions of oil spill trajectories using a classic oil spill model. The method considers the output of the oil spill model as a function of random variables, which are the input parameters, and calculates the standard deviation of the output results which provides a measure of the uncertainty of the model as a result of the uncertainties of the input parameters.In addition to a single trajectory that is calculated by the oil spill model using the mean values of the parameters, a band of trajectories can be defined when various simulations are done taking into account the uncertainties of the input parameters. This band of trajectories defines envelopes of the trajectories that are likely to be followed by the spill given the uncertainties of the input.The method was applied to an oil spill that occurred in 1989 near Sines in the southwestern coast of Portugal. This model represented well the distinction between a wind driven part that remained offshore, and a tide driven part that went ashore. For both parts, the method defined two trajectory envelopes, one calculated exclusively with the wind fields, and the other using wind and tidal currents. In both cases reasonable approximation to the observed results was obtained.The envelope of likely trajectories that is obtained with the uncertainty modelling proved to give a better interpretation of the trajectories that were simulated by the oil spill model.     

城市中心区拥挤收费系统评价方法

本文介绍了拥挤收费系统的概念,提出了拥挤收费系统的综合评价指标及评价方法.最后设计了中心区拥挤收费方案,对中心区拥挤收费方案进行了评价.     

基于OILMAP的中国舟山港溢油管理系统

介绍了基于美国ASA（Applied Science Associates,Inc.）公司的用于预测海水或淡水中溢油的轨迹和归宿的软件OILMAP建立的“中国舟山港溢油模拟信息系统”的组成、功能和特点，并用实例说明了其使用方法。     

交通拥挤收费原理与对策研究

交通拥挤收费（Oongestion Pricing）作为现代化城市交通需求管理（TDM）的有效措施之一，虽然在实践中颇多微词，但是其在减少交通吸引、控制交通流量、提高车速与路段通行能力、提高能源利用效率及提高路网的运行效率等方面效果显著，并且在缓解交通拥堵压力的同时带来了财政副收入。从经济学原理出发，利用边际成本定价理论、收入再分配理论以及公共产品和产权经济学理论分析了交通拥挤收费的本质、理论依据，最后提出交通拥挤收费的相关建议和措施。     

A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks

The cooperative vehicle-infrastructure technologies have enabled vehicles to collect and exchange traffic information in real time. Therefore, it is possible to use Vehicular Ad-hoc NETworks (VANETs) for detecting traffic congestion on urban expressways. However, because of the special topology of urban expressways (consisting of both major and auxiliary roadways), the existing traffic congestion detection methods using VANETs do not work very well. In addition, the existing dissemination methods of congestion information lack the necessary control mechanism, so the information may be disseminated to irrelevant geographical areas. This paper proposes a congestion detection and notification scheme using VANETs for urban expressways. The scheme adopts a simplified Doppler frequency shift method to estimate and differentiate traffic conditions for major and auxiliary roadways. Vehicular cooperation and human cognition are introduced to improve the estimation accuracy and to describe the overall traffic conditions. Additionally, the scheme develops a spatial–temporal effectiveness model based on the potential energy theory to control the dissemination area and survival time of the congestion information. Meanwhile, the proposed scheme uses several broadcast control mechanisms to alleviate vehicular network congestion. Simulations through TransModeler indicate that our scheme ensures the accuracy of the estimation of congestion degree. Consequently, the scheme can provide effective references for driving decision-making and path-planning.     

Influence of the heavy fuel spill from the Prestige tanker wreckage in the overlying seawater column levels of copper, nickel and vanadium (NE Atlantic ocean)

The water column above the Prestige wreckage was sampled during two consecutive campaigns: Prestinaut (December 2002) two weeks after the tanker sunk and HidroPrestige0303 (March 2003) one month after the sealing of the main fuel leaks. Samples of the original cargo fuel and the emulsified fuel in the surface of the ocean were also collected. Analysis of the fuel indicated the release of 135 kg of Cu, 1700 kg of Ni and 5300 kg of V from the original fuel to the water column, remaining 35 kg of Cu, 3100 kg of Ni and 13,800 kg of V in the emulsified fuel. The metal partitioning between the water column and the emulsioned floating fuel, Cu > Ni ~ V, are in accordance with the stability index for the metal–nitrogen bond in metalloporphyrins. This release had an impact on dissolved trace metal concentrations in the water column. An increase on dissolved copper (2.8–4.7 nM) and nickel (2.2–8.0 nM) with respect to natural values (1–3 nM for Cu and 1.6–5 nM for Ni) was observed. Values for vanadium (28–35 nM) were in the range of pristine North Atlantic waters (30–36 nM). This contamination was especially observed in the upper water column (0–50 m), associated with the mixing of seawater with the fuel moving upwards, and in deep waters, where the residence time of fuel is higher. Future research in this field should focus on the environmental variables and the processes that control the release of contaminants from fuels for a better assessment of the contamination in oil-spill events.     

Leveraging social networks for efficient hurricane evacuation

One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.     

Preparing a nation for autonomous vehicles: opportunities,barriers and policy recommendations

Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.     

Urban delivery industry response to cordon pricing, time-distance pricing, and carrier-receiver policies in competitive markets

The paper develops a set of analytical formulations to study the behavior of the urban delivery industry in response to cordon time-of-day pricing, time-distance pricing, and comprehensive financial policies targeting carriers and receivers. This is accomplished by modeling the behavior of receivers in response to financial incentives, and the ensuing behavior of the carrier in response to both pricing and the receivers’ decisions concerning off-hour deliveries. The analytical formulations consider both the base case condition, and a mixed operation with both regular hour and off-hour deliveries; two pricing schemes: cordon time of day, and time-distance pricing; two types of operations: single-tour, and multi-tour carriers; and three different scenarios in terms of profitability of the carrier operation, which include an approximation to the best case, the expected value, and the worst case. The analyses, both theoretical and numerical, highlight the limitations of pricing-only approaches. In the case of cordon time of day pricing, the chief conclusion is that it is of limited use as a freight demand management tool because: (1) in a competitive market the cordon toll cannot be transferred to the receivers as it is a fixed cost and (2) the structure of the cost function, that only provides an incentive to the carrier to switch to the off-hours when all the receivers in the tour switch to the off-hours. The analyses of time-distance pricing clearly indicate that, though its tolls could be transferred to the receivers and provide an incentive for behavior change, the magnitude of the expected toll transfers under real life conditions are too small to have any meaningful impact on receivers choice of delivery times. In essence, the key policy implication is that in order to change the joint behavior of carrier and receivers, financial incentives—or programs that foster unassisted off-hour deliveries—should be made available to receivers in exchange for their commitment to do off-hour deliveries. As the paper proves, if a meaningful number of receivers switch to the off-hours, the carriers are likely to follow suit.     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.