Dynamic pricing for managed lanes with multiple entrances and exits

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPLs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0–27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5–50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the “jam-and-harvest” behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.     

Flight and passenger delay assignment optimization strategies

This paper compares different optimization strategies for the minimization of flight and passenger delays at two levels: pre-tactical, with on-ground delay at origin, and tactical, with airborne delay close to the destination airport. The optimization model is based on the ground holding problem and uses various cost functions. The scenario considered takes place in a busy European airport and includes realistic values of traffic. A passenger assignment with connections at the hub is modeled. Statistical models are used for passenger and connecting passenger allocation, minimum time required for turnaround and tactical noise; whereas uncertainty is also introduced in the model for tactical noise. Performance of the various optimization processes is presented and compared to ration by schedule results.     

Dynamic pricing and reservation for intelligent urban parking management

Despite rapid advances of information technologies for intelligent parking systems, it remains a challenge to optimally manage limited parking resources in busy urban neighborhoods. In this paper, we use dynamic location-dependent parking pricing and reservation to improve system-wide performance of an intelligent parking system. With this system, the parking agency is able to decide the spatial and temporal distribution of parking prices to achieve a variety of objectives, while drivers with different origins and destinations compete for limited parking spaces via online reservation. We develop a multi-period non-cooperative bi-level model to capture the complex interactions among the parking agency and multiple drivers, as well as a non-myopic approximate dynamic programming (ADP) approach to solve the model. It is shown with numerical examples that the ADP-based pricing policy consistently outperforms alternative policies in achieving greater performance of the parking system, and shows reliability in handling the spatial and temporal variations in parking demand.     

机器学习——动力学耦合车辆跟驰模型

目前,跟驰模型的建立主要基于动力学方法和机器学习算法,将两者耦合起来建立跟驰模型的研究还没有.以线性组合预测为基础,对最优加权法中的目标函数进行改进,将经典的Gipps模型和基于BP神经网络的跟驰模型(BP Car-following Model,BP)耦合起来,建立线性组合车辆跟驰模型(Linear Combination Car-following Model,LC-CF).结果表明:BP模型的预测结果更加贴近真实值,Gipps模型的预测结果更加贴近安全值;LC-CF模型可以通过调整参数,来控制BP模型和Gipps模型在LC-CF模型中的权重,进而达到控制预测速度的真实性和安全性的目的.     

求解线性方程组的一种新算法：LMSS迭代法

本文对求解线性方程组AX=B(A∈R~(n×m),B∈R~n),给出了一种新的迭代算法——LMSS算法,并证明了该算法的收敛性.同时,对其解的收敛极限进行了讨论;并通过实例验证了该算法的有效性。     

求解交通配流问题的一种新算法

路径选择是交通配流的主要问题,本文采用遗传算法设计了一个新模型以求解交通配流问题。实例计算表明,这一算法能迅速求出问题的全局最优解。     

求解线性方程组的递推算法

针对求解线性方程组AX=B(A∈R~(m×n),B∈R~m,X∈R~n)的两种迭代格式:(Ⅰ)X~(K+1)=EX~(K)+H;(Ⅱ)X~(K+1)=GX~(K)+F,给出了递推算法。当矩阵A和B不断增加新数据时,不必按新数据计算(Ⅰ)和(Ⅱ)中的相应矩阵,而是在新旧矩阵间建立了递推关系,减少了计算量和计算机内存。     

立体摄影测量泄流水舌外形

本文阐述了立体摄影测量中直接线性变换的原理。结合二滩工程泄流雾化模型,进行了立体摄影量研究,成功地测得了高速泄流水舌的形状、范围。     

S-175集装箱船舶波浪载荷的船模试验与线性切片理论计算的比较

本文就日本S-175集装箱船舶的波浪载荷进行了船模试验与线性切片理论计算的比较,发现线性切片理论对于波浪载荷沿船长的纵向分布以及中拱与中垂的不同分量都与试验值存在着相当大的差异,仅纵向运动较为满意;揭示了对于在高浪级下快速舰船的波浪载荷,特别是砰击载荷应致力于开展非线性理论和试验研究。     

测量控制网优化设计中建模与求解的一般方法

本文在目前国际公认的求解带约束非线性规划最为有效的方法之一－逐步二次规划（ＳＱＰ）法的基础上，结合目标规划原理，导出一套适用于求解一般的多目标优化模型的逐步线性目标规划（ＳＬＧＰ）法，基于ＳＱＰ法和ＳＬＧＰ法的先进性，又提出解决一般测量控制网优化设计问题的总体思路和方法，并给出了新的建模方法。