Energy and emissions impacts of a freeway-based dynamic eco-driving system

Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US CO₂ emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO₂ emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person’s driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle’s vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10–20% in fuel savings and lower CO₂ emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO₂ emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.     

交通拥堵与温室气体排放

缓解拥堵项目可减少温室气体排放,但基于单个变量(如仅基于出行距离)的CO2排放预测并不精确.为了准确预测缓堵项目减少的CO2排放量,利用车辆运行检测技术、车辆活动数据库及根据车辆类型设计的排放模型,通过分析速度与排放的关系绘制速度-排放曲线,并结合交通运行检测数据,评价缓堵策略、速度管理策略、交通平滑策略等交通运行管理...     

智能网联汽车协同生态驾驶策略综述

为了跟踪近年来智能网联汽车(CAV)协同生态驾驶策略的研究进展, 分析了车辆、驾驶行为、交通网络和社会这4类因素对CAV能耗的影响程度, 以车辆、基础设施和旅行者为对象对目前CAV生态研究进行分类, 重点分析了信号交叉口生态驶入与离开、生态协同自适应巡航控制、匝道合流区生态协同驾驶、生态协同换道轨迹规划和生态路由5种典型车辆协同生态驾驶应用场景的研究现状。分析结果表明: 相比人类驾驶方式, 在任何交通流量CAV 100%渗透率的条件下和低交通流量CAV部分渗透率的条件下, CAV油耗节省效果显著, 最高可达63%, 而具有部分智能化和网联化等级的CAV油耗可至少节省7%;现有研究较少考虑人机共驾情况下, 驾驶人反应延迟和自动控制器传输延迟导致的轨迹跟踪偏离; 现有研究将车车通信/车路通信假定为理想数据交互过程, 未考虑通信拓扑、传输时延、通信失效与基站切换等因素对CAV生态协同驾驶策略的影响; 现有研究较少探讨多车道、交叉口转向-直行共用车道和U型车道等交通场景, 以及不同智能网联等级CAV与人类驾驶汽车、行人、自行车等共存的混合交通条件下的生态驾驶策略; 受限于自动驾驶技术和基础设施尚未成熟和完善, 真实交通场景下的测试验证工作尚未开展; 车辆控制、车车通信、多车协同、混合交通流场景、半实物仿真测试和真实交通场景测试等方面将是CAV协同生态驾驶策略的进一步发展方向。      

Forecast verification of a 3D model of the Mediterranean Sea. The use of discrete wavelet transforms and EOFs in the skill assessment of spatial forecasts

The quality assessment of a nested model system of the Mediterranean Sea is realised. The model has two zooms in the Provençal Basin and in the Ligurian Sea, realised with a two-way nesting approach. The experiment lasts for nine weeks, and at each week sea surface temperature (SST) and sea level anomaly are assimilated. The quality assessment of the surface temperature is done in a spatio-temporal approach, to take into account the high complexity of the SST distribution. We focus on the multi-scale nature of oceanic processes using two powerful tools for spatio-temporal analysis, wavelets and Empirical Orthogonal Functions (EOFs). We apply two-dimensional wavelets to decompose the high-resolution model and observed SST into different spatial scales. The Ligurian Sea model results are compared to observations at each of those spatial scales, with special attention on how the assimilation affects the model behaviour. We also use EOFs to assess the similarities between the Mediterranean Sea model and the observed SST. The results show that the assimilation mainly affects the model large-scale features, whereas the small scales show little or no improvement and sometimes, even a decrease in their skill. The multiresolution analysis reveals the connection between large- and small-scale errors, and how the choice of the maximum correlation length of the assimilation scheme affects the distribution of the model error among the different spatial scales.     

Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin experiments and real in-situ data assimilation

The Singular Evolutive Extended Kalman (SEEK) filter has been implemented to assimilate in-situ data in a 1D coupled physical-ecosystem model of the Ligurian Sea. The biogeochemical model describes the partly decoupled nitrogen and carbon cycles of the pelagic food web. The GHER hydrodynamic model (1D version) is used to represent the physical forcings. The data assimilation scheme (SEEK filter) parameterizes the error statistics by means of a set of empirical orthogonal functions (EOFs). Twin experiments are first performed with the aim to choose the suitable experimental protocol (observation and estimation vectors, number of EOFs, frequency of the assimilation,…) and to assess the SEEK filter performances. This protocol is then applied to perform real data assimilation experiments using the DYFAMED data base. By assimilating phytoplankton observations, the method has allowed to improve not only the representation of the phytoplankton community, but also of other variables such as zooplankton and bacteria that evolve with model dynamics and that are not corrected by the data assimilation scheme. The validation of the assimilation method and the improvement of model results are studied by means of suitable error measurements.     

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.     

The Horsepower Reserve Formulation of Driveability for a Vehicle Fitted With a Continuously Variable Transmission

Summary A systematic methodology is developed for choosing the optimum ratio trajectory of a continuously variable transmission in a passenger vehicle. The optimum CVT ratio schedule is formulated as a constrained optimization problem with maximum fuel economy as the objective function and driveability concerns and physical limitations included as the constraints. The key notion to achieving good driveability is the introduction and definition of a horsepower reserve function that creates a consistent and desirable vehicle response under different driving conditions. Simulation results compare the optimized schedule's performance with several other possible ratio schedules, including the minimum brake specific fuel consumption map. Results from the optimized schedule indicate only a mild tradeoff between driveability and fuel economy relative to the other ratio schedules. The ratio optimization problem formulation and solution provide a novel and unique approach for systematically addressing driveability and fuel economy considerations associated with a continuously variable transmission.     

Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea

A system of two nested models composed by a coarse resolution model of the Mediterranean Sea, an intermediate resolution model of the Provençal Basin and a high resolution model of the Ligurian Sea is coupled with a Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three models. The forecast error is estimated by an ensemble run of 200 members by perturbing initial condition and atmospheric forcings. The 50 dominant empirical orthogonal functions (EOF) are taken as the error covariance of the model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system.