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Local and Iterative Searches for Combined Signal Control and Assignment Problem Implementation and Numerical Examples

机译:本地和迭代搜索组合信号控制和分配问题实现和数字示例

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Traffic signal-setting policies and traffic assignment procedures are mutually dependent. The combined signal control and traffic assignment problem deals with this interaction. With the total travel time minimization objective, gradient-based local search methods are implemented. Deterministic user equilibrium is the selected user route choice rule, Webster's delay curve is the link performance function, and green-time-per-cycle ratios are decision variables. Three solution codes are implemented, resulting in six variations. Three solution codes are implemented, resulting in six variations including intersections with multiphase operation and overlapping traffic movements. For referrence, the iterative approach is also coded, and all codes are tested in four example networks at five demand levels, the results show the numerical gradient estimation procedure performs best, although the simplified local searches reduce the large network computational burden. Demand level as well as network size affects the relative performance of the local and iterative approaches. As demand level becomes higher, (a) in the small network, the local search tends to outperform the iterative search, and (b) in the large network, the opposite result is obtained.
机译:流量信号设置策略和流量分配过程是相互依赖的。组合信号控制和流量分配问题处理此交互。利用总旅行时间最小化目标,实现了基于梯度的本地搜索方法。确定性用户平衡是所选择的用户路由选择规则,韦伯斯特的延迟曲线是链路性能函数,而绿色时周期比是决策变量。实现了三个解决方案代码,导致六种变体。实现了三个解决方案代码,导致六个变体,包括具有多相操作和重叠业务移动的交叉点。对于引荐,还编码迭代方法,并且在五个需求水平中,在四个示例网络中测试所有代码,结果显示了数值梯度估计过程最佳,尽管简化的本地搜索降低了大的网络计算负担。需求水平以及网络大小影响本地和迭代方法的相对性能。随着需求水平变得更高,(a)在小网络中,本地搜索倾向于优于迭代搜索,并且(b)在大网络中,获得相反的结果。

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