Fine sediment deposits in shelf seas

From field observations it appears that the top layer of a shelf bottom in general exhibits an intricate geographical pattern of sediment formations. Sediments of different composition are confined in distinct regions. This contradicts the idea that current and wave forces stir up bottom sediment and disperse it in a random way over the shelf; the dispersal process is counteracted by sorting mechanisms. In this paper the bottom patterns of fine cohesive sediments are considered. A specific sorting mechanism is studied which may explain the patchy structure of fine sediment deposits. It is shown that fine sediments can be trapped in bottom deposits which contain a fine sediment fraction high enough to prevent pore water motion in the shelf bed. This mechanism opposes sediment dispersal away from existing deposits. It may also explain the formation or the preservation of mud patches, even in regions where the bottom shear stress is relatively high.     

模拟流体-弹性结构相互作用分析的ISPH-TLSPH耦合方法研究（英文）

A fully Lagrangian algorithm for numerical simulation of fluid-elastic structure interaction(FSI) problems is developed based on the Smoothed Particle Hydrodynamics(SPH) method. The developed method corresponds to incompressible fluid flows and elastic structures. Divergence-free(projection based) incompressible SPH(ISPH) is used for the fluid phase, while the equations of motion for structural dynamics are solved using Total Lagrangian SPH(TLSPH) method.The temporal pressure noise can occur at ...     

A velocity and payload dependent emission model for heavy-duty road freight transportation

The paper develops a forecasting model of emissions from traffic flows embracing the dynamics of driving behavior due to variations in payload. To measure of emissions at the level of individual vehicles under varying payloads a portable emission measurement system is used. This paper reports on a model based on data at the level of individual vehicles for a representative road trajectory. The model aggregates the data to the level of a homogeneous flow dependent of velocity and specific power, which is dependent on payload weight. We find a lean specification for the model that provides emission factors for CO₂, NO_x, HC, CO, and NO₂. The results indicate that, in comparison with earlier models, NO_x emissions in particular tend to be underestimated.     

Expert versus lay perception of the risks of motor vehicle-generated air pollution

We explore whether experts’ perceptions of risk differ systematically from those of the public. To do so, we examine whether experts and non-experts make different location decisions in response to ground-level ozone pollution, one of the byproducts of motorized transportation. Physicians are experts in the field of health, and thus may differ from their lay neighbors in their knowledge of and attitude toward pollution and its health risks. If so, it is possible that they value locations with cleaner air differently than their neighbors do. Here we use hedonic price models based on willingness to bear housing and commute burdens to examine the differential valuation of clean air by doctors and laypeople in the Los Angeles region between 1980 and 2000. We find no evidence that doctors are more or less more willing than comparable lay residents to trade off time or money to live in cleaner-air neighborhoods.     

Investigating the link between cyclist volumes and air pollution along bicycle facilities in a dense urban core

In this paper we explore the air pollution levels along types of bicycle facilities using a NO₂ land use model previously developed for Montreal. We explore potential associations between bicycle volumes through signalized intersections and pollution levels at those intersections. We further investigate this relationship through the comparison of over thirty cycling corridors as well as an evaluation of the potential exposure of cyclists to air pollution along five routes. We observe NO₂ concentrations to be positively correlated with bicycle flows at the intersection level. We also observe that corridors with either a bicycle path or cycle track generally rank higher in terms of bicycle volume and also have higher NO₂ concentrations than corridors without bicycle facilities. This indicates that intersections and bicycle facilities with a large number of cyclists are also those characterized with the highest air pollution levels.     

Adaptive data dissemination for time-constrained messages in dynamic vehicular networks

With the advent of emerging wireless communication technologies, tremendous efforts have been put on promoting the safety and efficiency of transportation services by developing innovative applications. In particular, there has been significant interest in accessing information stored at RSUs (Roadside Units). The unique characteristics in vehicular networks, such as dynamic traffic factors including vehicle arrival rate, dwell time and data access patterns, bring us new challenges on data dissemination. This work dedicates to the investigation of timely and adaptive data dissemination in the dynamically changing traffic environment. Firstly, we derive an analytical model to explore and examine the effects of the dynamic traffic factors. In light of the theoretical results, an on-line scheduling algorithm is proposed for adaptive data dissemination. Finally, we evaluate performance of the new algorithm in a variety of circumstances. The simulation results demonstrate satisfactory performance of the proposed algorithm.     

Reliable route guidance: A case study from Chicago

Reliable route guidance can be obtained by solving the reliable a priori shortest path problem, which finds paths that maximize the probability of arriving on time. The goal of this paper is to demonstrate the benefits and applicability of such route guidance using a case study. An adaptive discretization scheme is first proposed to improve the efficiency in computing convolution, a time-consuming step used in the reliable routing algorithm to obtain path travel time distributions. Methods to construct link travel time distributions from real data in the case study are then discussed. Particularly, the travel time distributions on arterial streets are estimated from linear regression models calibrated from expressway data. Numerical experiments demonstrate that optimal paths are substantially affected by the reliability requirement in rush hours, and that reliable route guidance could generate up to 5-15% of travel time savings. The study also verifies that existing algorithms can solve large-scale problems within a reasonable amount of time.     

Optimal distribution of financial incentives to foster off-hour deliveries in urban areas

The main objective of this paper is to develop mathematical formulations to gain insight into the best way to distribute financial incentives to receivers of urban deliveries to maximize participation in off-hour deliveries. The paper considers two different types of incentive budgets: exogenous, and endogenous. The exogenous case represents the condition in which an external decision maker determines the incentive budget that is to be distributed among potential participants in off-hour deliveries. In the case of an endogenous incentive budget, the entity distributing the incentives must raise the necessary funds using revenue generation mechanisms such as tolls and fines. The optimal incentives are obtained from the Karush–Kuhn–Tucker conditions of a mathematical program that maximizes the number of truck trips shifted to the off-hours as a function of the incentives. The mathematical models developed in this paper provide guidelines about how to optimally distribute financial incentives to foster off-hour deliveries.     

A method to assess demand growth vulnerability of travel times on road network links

Many national governments around the world have turned their recent focus to monitoring the actual reliability of their road networks. In parallel there have been major research efforts aimed at developing modelling approaches for predicting the potential vulnerability of such networks, and in forecasting the future impact of any mitigating actions. In practice—whether monitoring the past or planning for the future—a confounding factor may arise, namely the potential for systematic growth in demand over a period of years. As this growth occurs the networks will operate in a regime closer to capacity, in which they are more sensitive to any variation in flow or capacity. Such growth will be partially an explanation for trends observed in historic data, and it will have an impact in forecasting too, where we can interpret this as implying that the networks are vulnerable to demand growth. This fact is not reflected in current vulnerability methods which focus almost exclusively on vulnerability to loss in capacity. In the paper, a simple, moment-based method is developed to separate out this effect of demand growth on the distribution of travel times on a network link, the aim being to develop a simple, tractable, analytic method for medium-term planning applications. Thus the impact of demand growth on the mean, variance and skewness in travel times may be isolated. For given critical changes in these summary measures, we are thus able to identify what (location-specific) level of demand growth would cause these critical values to be exceeded, and this level is referred to as Demand Growth Reliability Vulnerability (DGRV). Computing the DGRV index for each link of a network also allows the planner to identify the most vulnerable locations, in terms of their ability to accommodate growth in demand. Numerical examples are used to illustrate the principles and computation of the DGRV measure.