大直径钢管桩承载力恢复过程试验研究

针对目前预制桩承载力恢复特性研究与工程应用中的不足,依托西非某海工工程,提出高应变法。采用对同一钢管桩进行初打与不同休止时间复打相结合的试验方法,研究了大直径钢管桩沉桩后的承载力恢复过程。结合地质情况、沉桩与试验结果,得到了钢管桩承载力、侧阻力及端阻力随时间变化的一般规律及影响因素,并通过静载试验对研究结果进行了验证。该研究在提高项目施工效率、降低成本的同时,还得到了有意义的规律与结论,可为后续类似项目提供参考。     

Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

Mental models of driving and speed: biases,choices and reality

This paper provides a review of research performed by Svenson with colleagues and others work on mental models and their practical implications. Mental models describe how people perceive and think about the world including covariances and relationships between different variables, such as driving speed and time. Research on mental models has detected the time-saving bias [Svenson, O. (1970). A functional measurement approach to intuitive estimation as exemplified by estimated time savings. Journal of Experimental Psychology, 86, 204–210]. It means that drivers relatively overestimate the time that can be saved by increasing speed from an already high speed, for example, 90–130?km/h, and underestimate the time that can be saved by increasing speed from a low speed, for example, 30–45?km/h. In congruence with this finding, mean speed judgments and perceptions of mean speeds are also biased and higher speeds given too much weight and low speeds too little weight in comparison with objective reality. Replacing or adding a new speedometer in the car showing min per km eliminated or weakened the time-saving bias. Information about braking distances at different speeds did not improve overoptimistic judgments of braking capacity, but information about collision speed with an object suddenly appearing on the road did improve judgments of braking capacity. This is relevant to drivers, politicians and traffic regulators.     

A copula-based approach for estimating the travel time reliability of urban arterial

Estimating the travel time reliability (TTR) of urban arterial is critical for real-time and reliable route guidance and provides theoretical bases and technical support for sophisticated traffic management and control. The state-of-art procedures for arterial TTR estimation usually assume that path travel time follows a certain distribution, with less consideration about segment correlations. However, the conventional approach is usually unrealistic because an important feature of urban arterial is the dependent structure of travel times on continuous segments. In this study, a copula-based approach that incorporates the stochastic characteristics of segments travel time is proposed to model arterial travel time distribution (TTD), which serves as a basis for TTR quantification. First, segments correlation is empirically analyzed and different types of copula models are examined. Then, fitting marginal distributions for segment TTD is conducted by parametric and non-parametric regression analysis, respectively. Based on the estimated parameters of the models, the best-fitting copula is determined in terms of the goodness-of-fit tests. Last, the model is examined at two study sites with AVI data and NGSIM trajectory data, respectively. The results of path TTD estimation demonstrate the advantage of the proposed copula-based approach, compared with the convolution model without capturing segments correlation and the empirical distribution fitting methods. Furthermore, when considering the segments correlation effect, it was found that the estimated path TTR is more accurate than that by the convolution model.     

Field tests of a dynamic green driving strategy based on inter-vehicle communication

This paper presents the design and results for field tests regarding the environmental benefits in stop-and-go traffic of an algorithmic green driving strategy based on inter-vehicle communication (IVC), which was proposed in Yang and Jin (2014). The green driving strategy dynamically calculates advisory speed limits for vehicles equipped with IVC devices so as to smooth their speed profiles and reduce their emissions and fuel consumption. For the field tests, we develop a smartphone-based IVC system, in which vehicles’ speeds and locations are collected by GPS and accelerometer sensors embedded in smartphones, and communications among vehicles are enabled by specially designed smartphone applications, a central server, and 4G cellular networks. Six field tests are carried out on an uninterrupted ring road under slow or fast stop-and-go traffic conditions. We compare the performances of three alternatives: no green driving, heuristic green driving, and the IVC-based algorithmic green driving. Results show that heuristic green driving has better smoothing and environmental effects than no green driving, but the IVC-based algorithmic green driving outperforms both. In the future, we are interested in field tests under more realistic traffic conditions.     

Green walking networks for climate change adaptation

Climate change (CC) potentially affects people travel behaviour, due to extreme weather conditions. This is particularly true for pedestrians, that are more exposed to weather conditions. Introducing the effect of this change in transport modelling allows to analyse and plan walking networks taking into consideration the climatic variable. The aim of this work is to develop a tool that can support planning and design of walking networks, by assessing the effects of actions oriented to increase resilience with respect to extreme weather conditions (CC adaptation).An integrated approach is used, thus combining transport and land-use planning concepts with elements of outdoor thermal comfort and network accessibility. Walking networks are analysed through centrality indexes, including thermal comfort aspects into a general cost function of links and weighted nodes. The method has been applied to the walking network inside the Campus of the University of Catania (Italy), which includes different functions and where pedestrian paths are barely used by people. Results confirm that this tool is sensitive to the variables representing weather conditions and it can measure the influence of CC adaptation measures (e.g. vegetation) on walking attitude and on the performance of the walking network.     

基于客流需求的城市轨道交通动态时刻表优化模型

为使城市轨道交通列车运行时刻表更贴合客流需求,依据不断变化的客流需求确定每列车的发车时刻和停站时间,采用多目标优化方法构建以乘客出行时间费用和列车运行时间费用最小为目标、列车发车时刻和停站时间为决策变量的城市轨道交通动态时刻表优化模型,并采用粒子群算法求解。以广州地铁13号线为例进行验证,结果表明优化后的时刻表更满足客流需求,能有效地提高乘客出行效率,具有更好的动态适应性。     

基于生长曲线函数的货车运营环节碳达峰研究

货车是我国大气环境污染的重要来源之一，也是影响我国碳达峰总体目标实现的重要因 素。本文从货车运营环节入手，在运用生长曲线函数对货车保有量进行预测的基础上，对不同类 型货车的保有量和单车碳排放变化进行研究，并从货车节能技术发展、新能源货车推广和应用进 程两方面入手，分3种情景对货车运营环节中产生的碳排放总量趋势进行预判，推演货车运营环 节的碳达峰时间。研究结果表明，只有同时加快货车节能技术发展以及新能源货车推广和应用 进程，货车运营环节中产生的碳排放总量规模才能得到有效抑制并逐渐减少。若到 2030 年货 车单车燃料消耗水平较 2019 年降低 20%以上，新能源货车在货车整体保有量中的占比达到 20%，到 2060 年货车单车燃料消耗水平较 2019 年降低 50%，新能源货车占比达到 50%，则货车 运营环节碳达峰时间将缩短至2030年左右实现，2030年后货车运营环节产生的碳排放总量规 模将逐渐减少。