关于Cn^4和Cn^5（n≡0（mod5））的邻强边色数和全色数

得到了Cn^4和Cn^5（n≡0（mod5））的邻强边色数和全色数．     

大型图的割集算法研究

割集的概念与性质在网络问题的研究中受到广泛重视,但目前的割集搜索算法可操作性较差.提出了一种适合大型图的割集搜索算法CSA-CJ,利用二进制数分割无向图的顶点集,通过对子图各顶点的关联集的运算产生相应的割集.该算法简单适用,易于用计算机实现,尤其适合于大型图的割集搜索.     

船舶操纵仿真中图谱数值化方法的研究

在船舶操纵仿真中涉及大量的图谱转换工作,传统的图谱处理方法在精度和工作量上存在着一对矛盾。本文试图通过应用正弦小波拟合方法来替代传统折线方法,并在某些图谱上得到验证,可望在类似图谱数值化上得到推广和应用,以提高计算精度和减少计算工作量。     

基于有向图卷积神经网络的交通预测与拥堵管控

为解决城市快速路正面临的日益严重的交通拥堵问题,提出了一种针对城市快速路的基于有向图卷积神经网络的交通预测与拥堵管控方法,该方法能够有效利用海量交通数据进行交通预测,实现拥堵的主动管控。首先,基于交通路网的空间有向性和交通流的时空特性,定义了有向的距离影响矩阵、修正欧式距离矩阵和自由流可达矩阵,构建出有向的图卷积算子,并将其应用于长短时记忆神经网络模型中,提出了能学习交通路网时空双重特性的有向图卷积-长短时记忆神经网络（Directed Graph Convolution-LSTM,DGC-LSTM）模型;其次,基于DGC-LSTM的交通预测结果识别出拥堵产生点并将其作为拥堵管控的对象;再次,采用控制进口匝道车辆输入快速路主线的手段,针对管控对象的时空特征,设计了全圈层分时段阶梯式拥堵管控策略;最后,基于上海市快速路网上布设的2 712个检测器在122个工作日每间隔5 min记录的速度、流量和占有率信息,开展实例分析,测试了DGC-LSTM模型的预测精度以及全圈层分时段阶梯式拥堵管控策略的有效性。结果表明：与传统的循环神经网络、长短时记忆神经网络相比,DGC-LSTM模型具有更高的预测精度,能将速度预测的平均绝对误差和误差标准差分别降低38%和20%以上;基于预测结果采用的全圈层分时段阶梯式拥堵管控策略能令拥堵产生点的速度提升14 km·h^-1以上,并能使拥堵的持续时长缩短40%,可阻止拥堵从产生点开始发生大范围的蔓延,降低整个路网的拥塞程度。     

深圳地铁线网客流分析及与运行图匹配措施研究

深圳地铁二期工程于2011年6月28日投入试运营,形成178 km线网,客运量激增,并具明显特点。通过分析线网客流,尤其是3号线客流特点,制定了工作日早晚高峰及休息日全天的调图措施,以满足服务并买现运能匹配和成本控制。     

城市轨道交通列车自动监控仿真系统站场图自动生成

通过对上海轨道交通3号线、8号线仿真系统站场图的研究,提出了一种基于统一站场数据格式下不同制式站场图的自动生成方法。该方法基于站场图结构与组件化程序设计,运用规范格式的站场数据自动生成站场图,简化了仿真系统主视图的开发过程。     

基于键合图的车用蓄电池建模与仿真

按照键合图的规则,分别建立了蓄电池等效电路的键合图模型和冷却系统的键合图模型,然后将电系统和热系统耦合在一起,实现了蓄电池系统的键合图模型,最后建立了蓄电池的数学模型和仿真模型,并实现了动态仿真。     

HEV驱动系统的键合图建模和仿真计算

在深入研究某种混合动力汽车(HEV)驱动系统及其各单元动态特性的相互关系的基础上，运用键合图原理对该系统进行数学建模，并应用Matlab／Simulink进行仿真计算和研究。研究表明，所建模型较准确地反映HEV的动态特性，并发现由于多动力耦合中弹性与柔性耦合影响导致的载荷与运动波动，并证明调整阻尼可有效减小波动。     

Life cycle emissions and cost model for urban light duty vehicles

The growth of vehicle sales and use internationally requires the consumption of significant quantities of energy and materials, and contributes to the deterioration of air-quality and climate conditions. Advanced propulsion systems and electric drive vehicles have substantially different characteristics and impacts. They require life cycle assessments and detailed comparisons with gasoline powered vehicles which, in turn, should lead to critical updates of traditional models and assumptions. For a comprehensive comparison of advanced and traditional light duty vehicles, a model is developed that integrates external costs, including emissions and time losses, with societal and consumer life cycle costs. Life cycle emissions and time losses are converted into costs for seven urban light duty vehicles. The results, which are based on vehicle technology characteristics and transportation impacts on environment, facilitate vehicle comparisons and support policy making in transportation. Substantially, more sustainable urban transportation can be achieved in the short-term by promoting policies that increase vehicle occupancy; in the intermediate-term by increasing the share of hybrid vehicles in the car market and in the long-term by the widespread use of electric vehicles. A sensitivity-analysis of life cost results revealed that vehicle costs change significantly for different geographical areas depending on vehicle taxation, pricing of gasoline, electric power and pollution. Current practices in carbon and air quality pricing favor oil and coal based technologies. However, increasing the cost of electricity from coal and other fossil fuels would increase the variable cost for electric vehicles, and tend to favor the variable cost of hybrid vehicles.     

Adoption of alternative fuel vehicles: Influence from neighbors,family and coworkers

During the last years, many governments have set targets for increasing the share of biofuels in the transportation sector. Understanding consumer behavior is essential in designing policies that efficiently increase the uptake of cleaner technologies. In this paper we analyze adopters and non-adopters of alternative fuel vehicles (AFVs). We use diffusion of innovation theory and the established notion that the social system and interpersonal influence play important roles in adoption. Based on a nationwide database of car owners we analyze interpersonal influence on adoption from three social domains: neighbors, family and coworkers. The results point primarily at a neighbor effect in that AFV adoption is more likely if neighbors also have adopted. The results also point at significant effects of interpersonal influence from coworkers and family members but these effects weaken or disappear when income, education level, marriage, age, gender and green party votes are controlled for. The results extend the diffusion of innovation and AFV literature with empirical support for interpersonal influence based on objective data where response bias is not a factor. Implications for further research, environmental and transport policy, and practitioners are discussed.