Extending Max-Pressure Control for Traffic Network Operations

交通信号配时是在信号交叉口分配通行权的一种方式，它会显著影响城市交通流量并造成延误。本课题通过数学证明和实验结果，提出了一种新的自适应信号定时（最大压力控制）方法。最大压力控制使用车辆队列长度信息来优化信号定时。本研究解决了现有工作中阻碍实施和限制实际效益的关键基本限制。该项目还包括与明尼苏达州亨内平县合作进行试点部署研究，以评估其十字路口的潜在效益。最后，它将扩展数学，提出替代路线选择，以最大限度地减少总拥堵，并最大限度地提高乘车服务（如Uber和Lyft）的客流量。从这个项目中获得的数学知识将通过研究生课程和博士生指导直接教授。为了提高公众对所研究问题的认识，我们将向K-12学生开展外展活动。本项目将在现有的存储转发队列模型中引入现实的交通流假设，即对流量、队列溢出和先入先出行为的高密度限制。由于最大压力信号定时通常依赖于传感器来估计队列长度，它将研究替代数据源来计算队列压力，即道路旅行延误。在这些任务中，该项目将寻找新的Lyapunov函数并修改最大压力控制以保持最大稳定性结果。仿真结果将用于研究当用户根据新的信号时序改变路由时对拥塞的影响。该项目将通过与明尼苏达州亨内平县合作，在其十字路口进行微观模拟和试点部署，进一步调查实际效益。最后，利用排队模型将吞吐量最大化方法扩展到系统最优动态交通分配和按需出行中的乘客服务。从本质上讲，本研究将把最大压力控制有吸引力的分析特性转化为更精确的模型，以便在城市中有效实施，实现实践效益。成功的基于李亚普诺夫的最大稳定性证明的智力挑战是重大的。此外，将最大压力方法应用于具有类似建模目标的其他交通工程问题将扩大整个交通运输领域的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Traffic signal timing-ways to distribute right of way at signalized intersections-can significantly affect vehicle flows and create delays in urban cities. This project develops a new adaptive method for signal timing (max-pressure control) through mathematical proof and experimental results. Max-pressure control uses vehicle queue length information to optimize signal timing. This research addresses key fundamental limitations with existing work that discourage implementation and limit the practical benefits. This project also includes a pilot deployment study by partnering with Hennepin County in Minnesota to evaluate potential benefits on their intersections. Finally, it will extend the mathematics to suggest alternative route choices to minimize total congestion, and maximizing passenger flow in ridesharing services such as Uber and Lyft. Mathematical knowledge gained from this project will be directly taught through graduate school courses and Ph.D. student mentoring. To improve public awareness of the studied problems, outreach activities will be conducted to K-12 students.This project will introduce realistic traffic flow assumptions into existing store-and-forward queueing models, namely high-density restrictions on flow, queue spillback, and first-in-first-out behavior. Since max-pressure signal timing normally relies on sensors to estimate queue lengths, it will investigate alternative data sources to calculate queue pressure, i.e. road travel delays. During these tasks, this project will search for new Lyapunov functions and modify the max-pressure control to retain the maximum-stability results. Simulation results will be used to study the effects on congestion when users change their routes in response to new signal timings. The project will further investigate actual benefits by partnering with Hennepin County in Minnesota to perform microsimulation and a pilot deployment on their intersections. Finally, the project will extend the throughput-maximizing approach to system optimal dynamic traffic assignment and passenger service in mobility-on-demand by using queueing models for those problems. Essentially, this research will bring the attractive analytical properties of max-pressure control into a more accurate model to move towards effective implementation in urban cities to realize the benefits in practice. The intellectual challenges of successful Lyapunov-based proofs of maximum-stability are significant. In addition, applications of the max-pressure approach to other transportation engineering problems with similar modeling goals will broaden the impacts across the transportation field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.