水力式升船机塔楼结构分析

塔楼结构是升船机的主体建筑物,针对景洪水电站水力式升船机塔楼的受力特点,采用数值计算方法及模型试验方法对水力式升船机塔楼结构进行深入研究。结果表明:数值计算结果和模型试验结果基本一致。通过施工完建后的相关监测结果对该设计进行论证,可为同类水力式升船机的塔楼设计提供借鉴。     

水力式升船机平衡重波动机理

水力式升船机是一种新型的通航建筑物,它完全利用水的浮力来驱动承船厢运行,与传统形式升船机相比具有十分明显的优越性。设计并建立了水力式升船机单竖井概化物理模型,初步探讨影响水力式升船机运行稳定性的主要因素,对比不同试验工况下竖井水力学特性及平衡重波动等特性。结果显示:竖井与平衡重间隙比为0.061时,在一定范围内增大流量对平衡重上升过程波动几乎没有影响,且平衡重上升过程相对稳定。     

水力式升船机竖井内水体的振动分析

地震荷载下升船机系统内水体与浮筒间的耦合作用是影响升船机安全运行的一个难题。通过流体动力学模拟(CFD)方法,讨论在不同频率的正弦激励下升船机竖井内水体的振荡。结果表明:竖井的水体存在一个自振频率,此频率比相同径度、相同水深的矩形池的值大约10%,自振周期约为0.42s;竖井水体的自振频率基本不随高度变化。     

The role of volume-delay functions in forecasting and evaluating congestion charging schemes: the Stockholm case

This paper uses observations from before and during the Stockkholm congestion charging trial in order to validate and improve a transportation model for Stockholm. The model overestimates the impact of the charges on traffic volumes while at the same time it substantially underestimates the impact on travel times. These forecast errors lead to considerable underestimation of economic benefits which are dominated by travel time savings. The source of error lies in the static assignment that is used in the model. Making the volume-delay functions (VDFs) steeper only marginally improves the quality of forecast but strongly impacts the result of benefit calculations. We therefore conclude that the dynamic assignment is crucial for an informed decision on introducing measures aimed at relieving congestion. However, in the absence of such a calibrated dynamic model for a city, we recommend that at least a sensitivity analysis with respect to the slope of VDFs is performed.     

An integrated framework for embedding large-scale dynamic traffic assignment models in advanced traveler information systems

In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort.     

A two-phase model for dry port location with an application to the port of Dalian in China

This paper provides guidance for an optimal and reasonable dry port layout for the port of Dalian in China. We present a two-phase framework on the location of dry ports, which solves the selection of candidate inland cities and optimal dry port location choice, respectively. Fuzzy C-Means Clustering is applied to select alternative cities in the vast hinterland of the seaport of Dalian, with a view to identify evaluation factors that affect the location selection decision. A cost-minimisation linear programming solution is proposed, with the aid of a genetic algorithm, to choose the optimal location as well as capacity level among the candidate inland cities.     

Instantaneous communication throughputs of vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) formed by connected vehicles in a traffic stream could be applied to improve safety, mobility, and environmental impacts of a transportation system. In this paper, we present analytical models for the instantaneous communication throughputs of VANETs to measure the efficiency of information propagation under various traffic conditions at a time instant. In particular, we define broadcast and unicast communication throughputs by the wireless channel bandwith multiplied by the average probabilities that one vehicle is a successful receiver and sender in a VAENT, respectively. With a protocol communication model, we derive formulas to determine the probabilities for an equipped vehicle to be a successful broadcast receiver and a successful unicast receiver/sender, and obtain broadcast and unicast throughputs along discrete and continuous traffic streams. We further examine the impacts on communication throughputs of the transmission range and the interference range of dedicated short range communication devices as well as the market penetration rate of equipped vehicles and the percentage of senders. Finally, we investigate the influence of shock waves on communication throughputs.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Understanding members' carsharing (activity) persistency by using econometric model

Carsharing is an innovative travel alternative that has recently experienced considerable growth and become part of sustainable transportation initiatives. Although carsharing is becoming increasingly a popular alternative transportation mode in North America, it is still an under‐researched area. Current research is aimed at better understanding of the behavior of carsharing users. For every member, a two‐stage approach microsimulates the probability of being active in any month using a binary probit model and given that a particular member is active during a month, the probability of that member using the service multiple times using a random utility‐based model. The model is estimated using empirical data from one of the largest carsharing companies in North America. The model estimates reveal that the activity persistency of members is positively linked to previous behaviors for up to 4 months, and that the influence of previous months weakens over time. It also shows that some attributes of the traveler (gender, age, and language spoken at home) impact his or her behaviors. Copyright © 2010 John Wiley & Sons, Ltd.     

A latent class accelerated hazard model of social activity duration

Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.