遗传算法在公路工程工期成本优化问题中的应用

利用遗传算法对公路工程工期成本优化问题进行探讨,给出利用遗传算法对整个问题的求解设计思路,在公路工程工期成本优化中利用遗传算法优化时染色体设计、优秀个体的充分利用以及遗传操作后不合理结果的处理提出解决方法,为今后遗传算法在公路工程进度优化中的应用提供有益的思路。最后设计求解流程图。     

遗传算法在区段站到发线的应用研究

为解决车站到发线的合理运用问题，采用遗传算建立了到发线运用的染色体结构和适应度函数，对这种复杂的非线性组合优化问题进行了仿真。     

城市公交线网优化的线性模型

为了对现有公交线网进行优化,有效利用现有交通资源,解决城市交通问题,运用系统科学的思想,通过对城市公交线网优化的主要内容、优化原则、优化目标以及约束条件的分析,兼顾考虑乘客出行时间、公交线网密度和公交企业的利益以及公交线网的布局对整个城市的交通系统的影响,提出了优化目标的函数表达式及相应约束条件的数学表达式,建立了公交线网优化的线性模型,并给出了运用逐步筛选法对所提出的数学模型进行求解的方法。     

Optimization of terminal airspace operation with environmental considerations

The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.     

Methods to reduce dimensionality and identify candidate solutions in multi-objective signal timing problems

Adjusting traffic signal timings is a practical way for agencies to manage urban traffic without the need for significant infrastructure investments. Signal timings are generally selected to minimize the total control delay vehicles experience at an intersection, particularly when the intersection is isolated or undersaturated. However, in practice, there are many other potential objectives that might be considered in signal timing design, including: total passenger delay, pedestrian delays, delay inequity among competing movements, total number of stopping maneuvers, among others. These objectives do not tend to share the same relationships with signal timing plans and some of these objectives may be in direct conflict. The research proposes the use of a new multi-objective optimization (MOO) visualization technique—the mosaic plot—to easily quantify and identify significant tradeoffs between competing objectives using the set of Pareto optimal solutions that are normally provided by MOO algorithms. Using this tool, methods are also proposed to identify and remove potentially redundant or unnecessary objectives that do not have any significant tradeoffs with others in an effort to reduce problem dimensionality. Since MOO procedures will still be needed if more than one objective remains and MOO algorithms generally provide a set of candidate solutions instead of a single final solution, two methods are proposed to rank the set of Pareto optimal solutions based on how well they balance between the competing objectives to provide a final recommendation. These methods rely on converting the objectives to dimensionless values based on the optimal value for each specific objectives, which allows for direct comparison between and weighting of each. The proposed methods are demonstrated using a simple numerical example of an undersaturated intersection where all objectives can be analytically obtained. However, they can be readily applied to other signal timing problems where objectives can be obtained using simulation outputs to help identify the signal timing plan that provides the most reasonable tradeoff between competing objectives.     

基于神经网络的小时间粒度交通流预测模型

为解决传统车队离散模型基于概率分布假设和现有交通流预测时间粒度过大不能应用于自适应信号配时优化等问题.在车队离散模型的建模思路上,先分析了下游交叉口车辆到达与上游交叉口车辆离去之间的关系,基于此构建了基于神经网络的小时间粒度交通流预测模型.该模型以上游交叉口离去流量分布为输入,下游交叉口到达流量分布为输出,时间粒度为5 s.最后,通过实际调查数据标定模型参数并应用模型预测下游交叉口到达流量.结果表明,与Robertson模型相比,本文模型预测结果能够更好地反映交通流的变化特征,平均预测误差减少了8.3%.成果可用于信号配时优化.     

The symmetric intersection design and traffic control optimization

It is well recognized that the left-turning movement reduces the intersection capacity significantly, because exclusive left turn phases are needed to discharge left turn vehicles only. This paper proposes the concept of Left-Hand Traffic (LHT) arterial, on where vehicles follow left-hand traffic rules as in England and India. The unconventional intersection where a LHT arterial intersects with a Right-Hand Traffic (RHT) arterial is named as symmetric intersection. It is only need three basic signal phases to separate all conflicts at symmetric intersection, while it at least need four signal phases at a conventional intersection. So, compared with the conventional intersection, the symmetric intersection can provide longer green time for the left-turning and the through movement, which can increase the capacity significantly. Through-movement waiting areas (TWAs) can be set at the symmetric intersection effectively, which can increase the capacity and short the cycle length furthermore. And the symmetric intersection is Channelized to improve the safety of TWAs. The Binary-Mixed-Integer-Linear-Programming (BMILP) model is employed to formulate the capacity maximization problem and signal cycle length minimization problem of the symmetric intersection. The BMILP model can be solved by standard branch-and-bound algorithms efficiently and outputs the lane allocation, signal timing decisions, and other decisions. Experiments analysis shows that the symmetric intersection with TWAs can increase the capacity and short the signal cycle length.     

城市轨道交通槽形梁设计参数优化研究

以跨径为30 m的城市轨道交通槽形梁为研究对象,采用精细化有限元方法研究槽形梁设计几何参数梁高、道床板厚度及角隅斜率对其力学性能的影响。分析结果表明:槽形梁主梁截面刚度随梁高的增加而增加,在给定跨径30 m的情况下存在最佳梁高1.8 m;道床板厚度与横向跨度有关,横向跨度为4 m时,适宜的道床板厚度是0.26 m;角隅斜率对槽形梁的影响主要表现在结合处的力学性能,推荐使用1:(2.5~3.0)。