Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.     

Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions

This research intends to explore external factors affecting driving safety and fuel consumption, and build a risk and fuel consumption prediction model for individual drivers based on natural driving data. Based on 120 taxi drivers’ natural driving data during 4 months, driving behavior data under various conditions of the roadway, traffic, weather, and time of day are extracted. The driver's fuel consumption is directly collected by the on-board diagnostics (OBD) unit, and safety index is calculated based on Data Threshold Violations (DTV) and Phase Plane Analysis with Limits (PPAL) considering speed, longitudinal and lateral acceleration. By using a linear mixed model explaining the fixed effect of the external conditions and the random effect of the driver, the influences of various external factors on fuel consumption and safety are analyzed and discussed. The prediction model lays a foundation for drivers' fuel consumption and risk prediction in different external conditions, which could help improve individual driving behavior for the benefit of both fuel consumption and safety.     

Container liner fleet deployment: A systematic overview

Container liner fleet deployment (CLFD) is the assignment of containerships to port rotations (ship routes) for efficient transport of containers. As liner shipping services have fixed schedules, the ship-related operating cost is determined at the CLFD stage. This paper provides a critical review of existing mathematical models developed for the CLFD problems. It first gives a systematic overview of the fundamental assumptions used by the existing CLFD models. The operating characteristics dealt with in existing studies are then examined, including container transshipment and routing, uncertain demand, empty container repositioning, ship sailing speed optimization and ship repositioning. Finally, this paper points out four important future research opportunities: fleet deployment considering ship surveys and inspections, service dependent demand, pollutant emissions, and CLFD for shipping alliances.     

Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.     

Assessing carbon emissions from road transport through traffic flow estimators

Carbon emissions from road transport are one of the main issues related to modern transport planning. To address them adequately, the acquisition of reliable data about traffic flow is an essential prerequisite. However, the large quantity and the heterogeneity of available information often cause problems; missing or incomplete data are one of the most critical aspects. This paper discusses how technology handles imperfect information in order to obtain more accurate quantification of CO₂ emissions. First, an analysis of single estimators and combination models is provided, highlighting their main characteristics. Then, the TANINO model (Tool for the Analysis of Non-conservative Carbon Emissions In TraNspOrt) is presented, jointly developed at the University of Seville and at the IUAV University of Venice. It consists of two different modules: the first is a combination model that optimizes the results of three traffic flow single estimators, while the second is a macro-model of carbon evaluation, which takes into account road infrastructure, vehicle type and traffic conditions. TANINO is then tested to calculate CO₂ emissions along the ring road of the Spanish city of Seville, showing its more efficient performance, compared to the single estimators normally adopted for such aims. Transport planning can benefit from the adequate knowledge of traffic flows and related CO₂ emissions, since it allows a more reliable monitoring of the progresses granted by specific carbon policies.     

柴-燃联合动力装置“船-机-桨”匹配特性

根据柴-燃联合动力装置各个部件之间的结构关系及系统热力循环方式,采用模块化建模思想,基于MATLAB/SIMULINK环境,建立了并车控制器、原动机、齿轮箱、离合器、轴系、螺旋桨等部件和系统仿真模型。以船速最优为原则将车钟手柄控制档位划分为十档,即将原动机输出功率由低到高划分为十档,在每个档位下利用系统稳态仿真,通过优化变距桨螺距比的方式,得出最高船速,并以此确定各个档位下的螺旋桨轴转速,获得了基于船-机-桨匹配的柴-燃联合动力装置稳态运行特性,为装置动态控制目标参数的确定提供了依据。     

基于蚁群算法的煤炭运输优化方法

蚁群算法是指通过人工模拟蚂蚁搜索食物的过程来求解运输优化问题的一种算法。给出蚁群算法模型及算法步骤。研究一种带容量限制和考虑损耗的煤炭运输数学模型的优化计算,并给出算法步骤。运用蚁群算法对某一钢铁企业煤炭运输问题进行优化计算,计算结果符合实际生产情况。     

燃气轮机性能的数学模型及其优化解法

本文建立了流量残量方程组形式的燃气轮机性能的新数学模型，并提出了用Newton-Raphson和DFP－BFS优化组合解法来求解该模型。计算实践表明，该法不仅稳定性高、收敛速度快，而且求解精度高，可在任何工况下都得到较为满意的结果，在燃气轮机设计中有一定的实用价值。     

船舶坐墩配墩优化

本文建立了船舶坐墩配墩优化设计的数学模型，采用分级优化方法和约束变尺度算法在微机上求解。讨论了墩木搁置基础、墩木最大容许承压面积及船体首、尾结构强弱对优化结果的影响。本文方法已在实际中应用，收到了良好的效果。     

Long term distribution of non-linear wave induced vertical bending moments

A method is proposed for the long-term formulation of wave induced vertical bending moments in ship structures. The non-linearity of the response is represented by an uncertain modelling factor that is calibrated by experimental values. Long term predictions are obtained for a tanker and a container ship hull showing that only in the latter case is the response clearly non-linear and reproduced in the long-term predictions.