波浪中过度加速度的非线性时域预报

国际海事组织(IMO)船舶设计建造分委会(SDC)第4次会议把过度加速度稳性直接评估的制定提上议程。然而,如何准确预报和评估波浪中的过度加速度依然是一个亟待解决的问题。文章针对此挑战,采用三维时域混合源法进行了波浪中过度加速度的直接预报和评估。首先,采用三维时域混合源法,建立了波浪中船舶大幅横摇运动和船体任意位置处横向加速度的非线性时域预报方法;其次,以4000TEU集装箱船为研究对象,开展规则波和不规则波中的大幅运动模型试验,对计算方法进行验证;然后,对比分析了3DOF(垂荡—纵摇—横摇)和4DOF(横荡—垂荡—纵摇—横摇)耦合数学模型的计算精度;最后,分析了船体横向加速度的影响因素。研究表明,考虑横荡影响的4DOF数学模型计算精度较高;数值计算结果和模型试验结果吻合良好,证明文中建立的非线性时域方法可有效预报波浪中的过度加速度,可用于IMO过度加速度衡准的制定,也可为船舶设计提供评估手段。此外,文中还研究了IMO薄弱性衡准草案中中国和德国联合提出的加速度简化计算方法的适用性,证明该简化计算方法具有一定的保守性,符合衡准的要求,可用于过度加速度薄弱性衡准计算。     

低长径比浮式圆柱涡激运动实验研究

为研究低长径比浮式圆柱涡激运动响应,对其进行了水槽模型实验研究。测试了三种长径比(L/D=2.5、2.0、1.5)圆柱在不同来流速度下的运动响应及顺流向、横流向动水压力,从响应幅值、涡泄频率、受力分析等多个角度出发,分析其涡激运动的关键特征。研究表明:涡激运动响应幅值随长径比的减小呈降低趋势,这种趋势在锁定阶段更加明显;涡激运动锁定区前后阶段,涡泄频率与固有频率比值随约化速度呈线性增加,锁定区前St≈0.18,锁定区后St数小于0.18,且随长径比的减小而减小。     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessment of spatial transferability of an activity-based model,TASHA

Spatial transferability has been recognized as a useful validation test for travel demand models. To date, however, transferability of activity-based models has not been frequently assessed. This paper assesses the spatial transferability of an activity-based model, TASHA (Travel Activity Scheduler for Household Agents), which has been developed for the Greater Toronto Area (GTA), Canada. TASHA has been transferred to the context of the Island of Montreal, Canada using the 2003 Origin–Destination (O–D) travel survey and the 2001 Canadian Census. It generates daily schedules of activities (individual and joint) for each individual in this region. The modelled activity attributes (frequency, start time, duration and distance) from TASHA and observed attributes from the 2003 O–D travel survey are compared for five different activities (i.e. work, school, shopping, other, and return to home). At the aggregate level, TASHA provides quite reasonable outcomes (in some cases – better results than for the Toronto Area) for all four attributes for work, school and return to home activities with few exceptions (for instance, school start time). The model outcomes are also promising for shopping frequency and start times; however, TASHA provides larger differences for average shopping durations and distances. Only the forecasts for all four attributes for the ‘other’ activity type differ greatly with the observed attributes for the Montreal Island. These large differences most likely indicate the differences in behaviour between the Montreal Island and the Toronto Area. In general, we conclude that re-estimation of model parameters and the use of local activity attribute distributions (frequency, start time and duration) is a desirable step in the transfer of the TASHA model from one context to another.     

Policy packages for modal shift and CO2 reduction in Lille,France

This paper proposes different policy scenarios to cut CO₂ emissions caused by the urban mobility of passengers. More precisely, we compare the effects of the ‘direct tool’ of carbon tax, to a combination of ‘indirect tools’ – not originally aimed at reducing CO₂ (i.e. congestion charging, parking charges and a reduction in public transport travel time) in terms of CO₂ impacts through a change in the modal split. In our model, modal choices depend on individual characteristics, trip features (including the effects of policy tools), and land use at origin and destination zones. Personal “CO₂ emissions budgets” resulting from the trips observed in the metropolitan area of Lille (France) in 2006 are calculated and compared to the situation related to the different policy scenarios. We find that an increase of 50% in parking charges combined with a cordon toll of €1.20 and a 10% travel time decrease in public transport services (made after recycling toll-revenues) is the winning scenario. The combined effects of all the policy scenarios are superior to their separate effects.     

Does crowding affect the path choice of metro passengers?

This paper investigates crowding effect on the path choice of metro passengers. We show people reroute not only to avoid the delay from crowding but also to evade crowding itself. More specifically, a logit model fits best when it uses the transit delay from crowding as well as the passenger load of a connection in addition to the conventional explanatory variables. Also, we demonstrate that crowding decreases the overall welfare of metro passengers. The model is tested on the real path choice data acquired by the recent algorithm by Hong et al. (2015) known to detect the real path choice from Smart Card data in more than 90% of the cases.     

Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

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.     

The relationship between land use and automobile travel utility: A Multiple Indicators Multiple Causes approach

The relationship between land use and the utility of automobile travel is examined by refining the utility concept, particularly by combining the microeconomic utility theory, which is concerned with the disutility of travel, and the perspective on the positive utility. A conceptual model is accordingly developed and then adjusted considering different purposes of travel. The purpose-specific models are tested through a Multiple Indicators Multiple Causes approach in Seoul, Korea, using datasets from a sample survey and geographic information systems. The major finding is that land use affects the utility mainly by changing synergy and affective utility rather than instrumental utility, which encompasses disutility variables. Among land use variables, the utility is found to be the most sensitive to the number of transit facilities for commuting and shopping travel and land use balance for leisure travel.     

Extending Time to Collision for probabilistic reasoning in general traffic scenarios

Vehicle-to-vehicle communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty and to loss of communication links. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method to calculate Time to Collision for unconstrained vehicle motion. This metric is gated using a novel technique based on relative vehicle motion that we call “looming”. Finally, these ideas are integrated into a probabilistic framework that accounts for uncertainty in vehicle state and loss of vehicle-to-vehicle communication. Together this work represents a new way of considering vehicle collision estimation. These algorithms are validated on data collected from real world vehicle trials.