Dynamic programming-based method for extraction of license plate numbers of speeding vehicles on the highway

In the last decade, vehicle identification systems have become a central element in many applications involving traffic law enforcement and security enhancement, such as locating stolen cars, automatic toll management, and access control to secure areas. As a method of vehicle identification, license plate recognition (LPR) systems play an important role and a number of such techniques have been proposed. In this paper, we describe a method for segmenting the main numeric characters on a license plate by introducing dynamic programming (DP) that optimizes the functionality describing the distribution of the intervals between characters, the alignment of the characters, and the threshold difference used to extract the character blobs. The proposed method functions very rapidly by applying the bottom-up approach of the DP algorithm and also robustly by minimizing the use of environment-dependent image features such as color and edges.     

有限理性视野下出行者出行方式选择分层Logit模型研究

在传统分层Logit模型的基础上,放松出行者完全理性的假设,基于有限理性满意决策准则,建立了多方式有限理性分层Logit(BRNL)模型,来描述出行者的方式选择行为.模型假设当方式间的成本差在无差异区间内时,出行者依据偏好或随机进行方式选择;否则,出行者将选择出行成本更小的交通方式.此外,提出了一种嵌套的相继平均算法对模型进行求解.最后,通过一个数值算例验证了模型的合理性和算法的有效性.研究结果表明,出行者并不总是选择出行成本最小的交通方式,其选择行为与出行者的理性程度和偏好有关,且理性程度越低,出行者对方式间的成本差越不敏感.