Dynamic pricing and matching in ride-hailing platforms

Ride-hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride-hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride-hailing platforms. We provide a review of matching and DP techniques in ride-hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool-matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.     

无人飞行器异源图像匹配辅助惯性导航定位技术综述

无全球定位系统下高精度定位与导航是飞行器实现自主侦查、巡航与打击的关键。视觉导航具有被动、低成本、能避免累积误差等优点。视觉导航与惯性导航融合更能够发挥出各自的优势,达到高精度定位的目的。总结了异源图像匹配辅助惯性导航的飞行器定位技术的发展历程; 从相机-惯导标定技术、异源图像匹配、姿态解算、数据融合和后端优化五个方面详细阐述了异源图像匹配辅助惯性导航的飞行器定位的关键技术; 指出了基于深度学习的异源图像匹配与惯性导航两种无源定位组合导航系统融合技术等四个未来可能的发展方向,可为实现异源图像匹配辅助惯性导航飞行器定位技术提供参考。     

基于压缩传感的PSO-OMP重构算法的研究

由于粒子群算法具有解决寻优问题的能力,将其应用于信号处理领域,提出了一种新的基于PSO-OMP的信号重构算法。为了降低计算复杂度,把粒子群算法运用在正交匹配追踪算法的匹配过程,以此来确定最优原子。实验结果表明,所提出的新的基于PSO-OMP的信号重构算法具有计算复杂度低和重构成功概率高等特点。     

Noncognitive skills and job match: evidence from military applicants

The study examines the effect of noncognitive skills on early career choices among young job seekers. Specifically, we analyze the influence of personality traits on the decision by military applicants either to choose the military or a civilian career option. We use a unique micro-level data-set of applicants to the US Navy and exploit the fact that many individuals who initially apply for military jobs eventually choose civilian careers instead. In this institutional setting, job candidates use new information to update their beliefs about the military job match. Personality traits are viewed as productive abilities that influence applicants’ expectations about the economic return to the job and occupational training offered by the Navy. The study finds that many of the 15 lower order personality facets associated with the Big Five traits are predictive of applicants’ job choices and provides suggestive evidence of a link between personality traits, job match expectations, and career choice.     

基于递推最小二乘的单站无源定位仿真分析

为达到无线射频注入的定位精度和实时性要求,提出采用递推最小二乘算法(RLS)进行定位.首先讨论了无线射频注入的定位精度需求,建立了机载无源单站定位模型,得出了最小二乘(LS)解.然后依据无线射频注入的实时性要求将其转化为递推形式.仿真结果表明,RLS算法与LS算法的定位精度和收敛速度接近,但前者实时性好,运算复杂度低,所需存储空间小,能够满足无线射频注入的定位需求.最后依据仿真结果给出了提高定位精度和收敛速度的措施.     

2D组网雷达目标真实高度的最小二乘估计

针对目标高度估计问题,给出了基于三部以上雷达距离测量的2D组网雷达目标真实高度的最小二乘估计,同时也给出了利用三部雷达计算目标真实高度的非最小二乘算法,并通过大量仿真分析了两种算法的性能。该算法能够在2D组网雷达内实现对目标真实高度的有效估计。     

装备维修器材保障最小费用模型研究

考虑部队装备维修器材需求量的随机变化对保障费用产生的影响,通过实际问题分析给出了在保障单位零星供应条件下,如何确定保障单位最佳的器材存储量并建立器材保障最小费用模型的程序和方法,最后结合实例,验证了模型的适用性.     

弹道导弹关机点状态估计的指数加权递归最小二乘方法

在双星预警条件下,将指数加权递归最小二乘算法应用于目标关机点状态估计问题中。通过引入加权因子对目标助推段运动的局部拟线性特性进行描述,从而在一定程度上克服了一般的线性多项式模型难以准确刻画整个助推段运动的难题。通过对助推段目标动力学特性的分析,考察了目标在垂直射面方向上的运动特性。在此基础上,提出了一种更为准确的助推段运动模型。仿真算例表明,所提出的关机点状态估计方法相对于传统的方法具有一定的优越性。     

多运动站测角频差无源定位方法研究

针对测角频差无源定位问题,提出了一种快速有效的解析定位方法,该方法通过引入中间量,将辐射源位置非线性估计问题转化成两个加权线性最小二乘估计问题,简化了定位解算.该方法无需进行参量搜索,比搜索法运算量小;无需初始值引导,比高斯-牛顿迭代法使用方便.在结构上,建立了观测模型,然后给出了辐射源定位误差对应的克拉美罗限,阐述了...     

基于空间子区一致性的异源图像匹配方法

提出一种基于空间子区一致性的异源图像匹配方法.该方法中,将图像划分为许多大小相同的子区;分别计算邻近子区间的灰度直方图距离,得到一种图像结构特征描述--似邻矩阵;利用似邻矩阵计算图像间的子区一致性;采用改进的搜索策略,快速获得匹配结果.实验结果表明,该方法在正确率和计算时间方面均优于传统方法.