Multiple USV cooperative algorithm method for hunting intelligent escaped targets北大核心CSCD

[目的]针对无人艇协同围捕过程中逃跑目标具备智能性,现有无人艇策略难以围捕成功的问题,提出一种基于双层切换策略的多无人艇协同围捕算法。[方法]第1层围捕策略采用改进势点法,以无人艇与势点的总直线距离最小为优化目标,采用匈牙利算法为无人艇动态分配势点,并采用人工势场法实现无人艇的协同避碰;第2层围捕策略利用了阿波罗尼奥斯圆的性质,在两艘无人艇前往逃跑目标的目标点进行拦截,剩余无人艇运动方向保持与逃跑目标相同,以不断缩紧包围区域;为应对逃跑目标不同的逃跑方式,第1层围捕策略和第2层围捕策略可互相转化。[结果]仿真实验表明,该算法相较于顺序分配势点算法和极角分配势点算法,围捕时间更少或持平,证明了该算法的有效性和先进性。[结论]该多无人艇协同双层围捕算法,对具备典型智能性的逃跑目标具有围捕效果。     

北斗卫星与车载传感器融合的车辆定位

为了改善北斗卫星定位性能,基于卡尔曼滤波设计了北斗卫星与车载传感器的数据融合算法。算法包括车辆方向融合和车辆位置融合两部分,前者融合电子罗盘和陀螺测量得到的北向角及其变化率,估算北向角的真实值;后者首先同步车速矢量与北斗卫星测量得到的车辆位置矢量,再根据融合结果估算车辆定位信息。为了验证数据融合算法,设计了北斗卫星与车辆状态信息采集系统,利用本系统采集的实车数据进行的仿真计算表明,算法能够提高定位频率、减小定位误差,提高北斗卫星定位的性能。     

求解客户需求动态变化的车辆路径规划方法

对于集货过程中客户需求随时间变化的动态车辆路径规划问题,按时间段划分为一系列车辆已驶离中心车场的静态车辆路径问题,引入虚拟任务点与相关约束方法,将其进一步等价转化为普通的静态车辆路径问题,使用适用于静态问题的算法对其进行求解。应用此车辆路径规划方法,以改进的节约法为静态算法,对于客户数为20的动态路径规划问题进行求解,得到重新优化路径所用的时间为0.49s,说明这种规划方法可行。     

港口体系职能结构的定量分析方法和实证研究

以长株潭港口体系为例,运用区位商和分工系数指标对长株潭港口体系职能结构动态变化特征和港口专业化发展趋势进行了定量分析,可供长株潭经济一体化下的港口规划参考。该定量分析方法也可推广应用于其他港口体系。     

舰船航行功能体系易损性分析研究

针对舰船航行功能体系较为复杂的特点,引入故障树中概率重要度的概念对舰船航行功能体系进行功能易损性分析。建立舰船航行功能毁伤树模型,并对其进行定性和定量分析,将底事件的概率重要度作为底事件功能易损性的评价指标。分析结果表明该方法对舰船功能易损性分析具有一定的参考意义,是解决该问题的一种途径。     

An analysis of bicycle route choice preferences in Texas,US

In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.

A traffic analysis zone definition: a new methodology and algorithm

This paper develops a comprehensive approach to the definition of transportation analysis zones (TAZ), and therein, presents a new methodology and algorithm for the definition of TAZ embedded in geographic information systems software, improves the base algorithm with several local algorithms, and comprehensively analyses the obtained results. The results obtained are then compared to these presently used in the transportation analysis process of the Lisbon Metropolitan Area. The proposed algorithm presents a new methodology for TAZ design based on a smoothed density surface of geocoded travel demand data. The algorithm aims to minimise the loss of information when moving from a continuous representation of the origin and destination of each trip to their discrete representations through zones, and focuses on the trade-off between the statistical precision, geographical error, and the percentage of intra-zonal trips of the resulting OD matrix. The results for the Lisbon Metropolitan Area case study suggest a significant improvement in OD matrix estimates compared to current transportation analysis practises based on administrative units.

A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.

Traveled distance,stock and fuel efficiency of private vehicles in Canada: price elasticities and rebound effect

This paper presents estimates of the rebound effect and other elasticities for the Canadian light-duty vehicle fleet using panel data at the provincial level from 1990 to 2004. We estimate a simultaneous three-equation model of aggregate demand for vehicle kilometers traveled, vehicle stock and fuel efficiency. Price and income elasticities obtained are broadly consistent with those reported in the literature. Among other results, an increase in the fuel price of 10% would reduce driving by ~2% in the long term and by 1% the average fuel consumption rate. Estimates of the short- and long-term rebound effects are ~8 and 20%, respectively. We also find that an increase in the gross domestic product per capita of 10% would cause an increase in driving distance of 2–3% and an increase of up to 4% in vehicle stock per adult. In terms of policy implications, our results suggest that: (1) the effectiveness of new fuel efficiency standards will be somewhat mitigated by the rebound effect and (2) fuel price increases have limited impacts on gasoline demand.

<Emphasis Type="Italic">Not</Emphasis> driving alone? American commuting in the twenty-first century

This paper investigates recent commuting trends by American workers. Unlike most studies of commuting that rely on data from the American Community Survey this study utilizes the American Time Use Survey to detail the complex commuting patterns of modern-day workers. Changes in the price of gasoline in recent years suggest that the incidence of “driving alone” should be on the decline. Indeed, results show that the sensitivity of modal commuting with respect to changes in gasoline prices appears to be relatively large. We estimate the gasoline-price elasticity of driving alone to be 0.057 and the gasoline-price elasticity of carpooling to be 0.502. Additional factors also affect commuting, including socio-economic characteristics and social desires. However, it is changes in gasoline prices that appear to account for nearly all of the recent variation in the mode chosen for commuting.