Dynamic Right-of-Way for Transit Vehicles: Integrated Modeling Approach for Optimizing Signal Control on Mixed Traffic Arterials
Dynamic Right-of-Way for Transit Vehicles: Integrated Modeling Approach for Optimizing Signal Control on Mixed Traffic Arterials
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DOI：
10.3141/1731-05
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发表时间：
2000
影响因子：
1.7
通讯作者：
P. Duerr
中科院分区：
文献类型：
--
作者：
P. Duerr
许多城市主干道上的公共交通和一般交通由同一组信号控制，并且必须争夺共享道路空间。在这些情况下，交通车辆通常会面临相当大的延误，因为它们在交通站点的停留时间使它们脱离了一般交通流的协调绿波。尽管现有的控制系统允许对信号时序进行本地调整以提供传输优先级，但这些短期行为通常与网络控制方案相矛盾，并且可能排除优先级方案或严重扰乱交通流。引入了走廊控制系统的新概念——动态路权，利用综合模型进行评估和优化，满足公共交通和一般交通的需求。该控制系统的目的是（a）通过动态控制所有网络链路的流入和流出来减少两种运输方式之间的严重干扰，（b）每当交通车辆接近十字路口时提供绿色信号，以及（c）通过保持整体信号协调来最大限度地减少一般交通中断。通过将基于事件的模拟器与基于遗传算法的优化例程联系起来，计算延迟最小化多周期信号控制方案。在离线实验中，证明了大幅减少延迟的潜力。最后，提出了一种基于在线测量和从神经网络模型导出的控制修正函数来动态实施和调整这些控制方案的方法。
Public transit and general traffic on many urban arterials are controlled by the same set of signals and must compete for shared road space. In these situations, transit vehicles typically face considerable delays because their dwell times at transit stops remove them from the coordinated green wave for general traffic flow. Although existing control systems allow for local adjustments of signal timings to provide transit priority, these short-term actions often contradict the network control scheme and may preclude a priority scheme or significantly disrupt traffic flow. A new concept for a corridor control system is introduced—the dynamic right-of-way, which serves the demands of public transit and general traffic using an integrated model for evaluation and optimization. The control system is intended to (a) reduce critical interferences between both modes of transport by dynamically controlling inflow and outflow for all network links, (b) provide a green signal whenever a transit vehicle approaches an intersection, and (c) minimize general traffic disruption by maintaining overall signal coordination. Through linking an event-based simulator with a genetic algorithm-based optimization routine, delay-minimizing multicycle signal control schemes are calculated. In offline experiments, the potential for achieving substantial reductions in delays is demonstrated. Finally, a method is presented by which these control schemes are implemented and adjusted dynamically, based on online measurements and a control modification function derived from a neural network model.