Reliable Shortest Path Problems in Stochastic Time-Dependent Networks

This study investigates the time-dependent reliable shortest path problem (TD-RSPP), which is commonly encountered in congested urban road networks. Two variants of TD-RSPP are considered in this study. The first variant is to determine the earliest arrival time and associated reliable shortest path for a given departure time, referred to as the “forward” TD-RSPP. The second problem is to determine the latest departure time and associated reliable shortest path for a given preferred arrival time, referred as the “backward” TD-RSPP. It is shown in this article that TD-RSPP is not reversible. The backward TD-RSPP cannot be solved by the algorithms designed for the forward problem using the reverse search from destination to origin. In this study, two efficient solution algorithms are proposed to solve the forward and backward TD-RSPP exactly and the optimality of proposed algorithms is rigorously proved. The proposed solution algorithms have potential applications in both advanced traveler information systems and stochastic dynamic traffic assignment models.     

Cascaded Construction of Semi-Bent and Bent Functions

Based on the theory of quadratic forms over finite fields,a new construction of semi-bent and bent functions is presented.The proposed construction has a cascaded characteristic.Some previously known constructions of semi-bent and bent functions are special cases of the new construction.     

A discrete optimization approach for locating Automatic Vehicle Identification readers for the provision of roadway travel times

This paper develops an algorithm for optimally locating Automatic Vehicle Identification tag readers by maximizing the benefit that would accrue from measuring travel times on a transportation network. The problem is formulated as a quadratic 0–1 optimization problem where the objective function parameters represent benefit factors that capture the relevance of measuring travel times as reflected by the demand and travel time variability along specified trips. An optimization approach based on the Reformulation–Linearization Technique coupled with semidefinite programming concepts is designed to solve the formulated reader location problem. To illustrate the proposed methodology, we consider a transportation network that is comprised of freeway segments that might include merge, diverge, weaving, and bottleneck sections. In order to derive benefit factors for the various origin–destination pairs on this network, we employ a simulation package (INTEGRATION) in combination with a composite function, which estimates the travel time variability along a trip that is comprised of links that include any of the four identified sections. The simulation results are actually recorded as generic look-up tables that can be used for any such section for the purpose of computing the associated benefit factor coefficients. Computational results are presented using data pertaining to a freeway section in San Antonio, Texas, as well as synthetic test cases, to demonstrate the effectiveness of the proposed approach, and to study the sensitivity of the quality of the solution to variations in the number of available readers.     

基于混合系统模型预测控制的列车优化运行

为保证列车高效率运行,需要对自动驾驶系统进行高效设计。列车运行过程具有高度非线性化的特性,针对这一特性,通过引入整数变量,建立基于混合系统的列车混合逻辑动态模型,其次考虑列车运行过程的多重约束,通过二次规划求解控制律。同时为了降低控制器计算量,在模型预测控制的基础上引入了阶梯式控制策略。最后利用Matlab仿真软件使其跟踪一条即定曲线,对所提的控制策略进行数值仿真。结果表明,该控制器可以在降低计算量的基础上,保证列车准时高效跟踪既定曲线,当改变控制器参数时,对跟踪效果有较大的影响。仿真结果对本文采用的方法进行了有效验证。