首页> 外文会议>International symposium on transportation and traffic theory >Progression Optimization in Large Scale Urban Traffic Networks: A Heuristic Decomposition Approach
【24h】

Progression Optimization in Large Scale Urban Traffic Networks: A Heuristic Decomposition Approach

机译:大规模城市交通网络的逐步优化:一种启发式分解方法

获取原文
获取外文期刊封面目录资料

摘要

Progression methods are widely used for optimization of traffic signal system operation in arterials and in grid networks. The methods provide robust solutions for traffic control as well as a multitude of design alternatives that are not readily available in other models. Solution procedures were developed in recent years using mixed-integer linear programming methods. While these methods can produce optimal solutions, they are computationally demanding and inefficient for traffic control applications. This paper describes a heuristic decomposition procedure for the optimization of the variable bandwidth netwrok progression problem. The procedure does not merely exploit the mathematical formulation of the mixed-integer linear program, but is primarily based on the traffic characteristics of the network. The network is decomposed into priority sub-networks which facilitates the accelerated determination of the optimal values for the integer variables. The heuristic improves dramatically the efficency of the computation, by at least a factor of 1/100. This enables to handle larger-scale networks, similar to the ones found in many metropolitan areas. Overall, more efficient computational procedures result in the ability to obtain improved solutions and, ultimately, lead to improved performance of the traffic network.
机译:渐进方法被广泛用于动脉和网格网络中交通信号系统操作的优化。该方法为交通控制提供了可靠的解决方案,以及其他模型中不易获得的众多设计替代方案。近年来,使用混合整数线性规划方法开发了解决方法程序。尽管这些方法可以提供最佳解决方案,但它们对交通控制应用的计算要求很高且效率低下。本文描述了一种启发式分解程序,用于优化可变带宽网络工作问题。该过程不仅利用混合整数线性程序的数学公式,而且主要基于网络的流量特性。该网络被分解为优先级子网络,这有助于加速确定整数变量的最佳值。启发式算法极大地提高了计算效率,至少提高了1/100倍。这使它能够处理大规模的网络,这与许多大都市地区的网络类似。总体而言,更有效的计算过程可以提高解决方案的能力,并最终改善交通网络的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号