Extending Max-Pressure Control for Traffic Network Operations

交通信号配时在信号交叉口分配通行权的方法可以显著影响城市中的车辆流量并造成延误。本计画借由数学证明及实验结果，发展出一种新的信号配时自适应方法（最大压力控制）。最大压力控制使用车辆排队长度信息来优化信号配时。这项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的试点部署研究，以评估其交叉路口的潜在效益。最后，它将扩展数学，以建议替代路线选择，以最大限度地减少总拥堵，并最大限度地提高Uber和Lyft等拼车服务的客流量。从这个项目中获得的数学知识将直接通过研究生院课程和博士学位教授。学生辅导为了提高公众对研究问题的认识，本研究将对K-12学生进行外展活动。本项目将在现有的存储转发调度模型中引入现实的交通流假设，即高密度流量限制、队列溢出和先进先出行为。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，因此它将调查替代数据源来计算队列压力，即道路行驶延迟。在这些任务中，本项目将寻找新的李雅普诺夫函数，并修改最大压力控制，以保持最大稳定性的结果。仿真结果将被用来研究当用户改变他们的路线，以响应新的信号定时对拥堵的影响。该项目将通过与明尼苏达州亨内平县合作，在其交叉路口进行微观模拟和试点部署，进一步调查实际效益。最后，本计画将利用系统最佳化动态交通分配与乘客服务的模型，将最大化系统通过量的方法延伸至这些问题。从本质上讲，这项研究将把最大压力控制的有吸引力的分析特性带入一个更准确的模型，以便在城市中有效实施，从而实现实践中的利益。基于李雅普诺夫的最大稳定性的成功证明的智力挑战是显著的。此外，将最大压力方法应用于其他具有类似建模目标的交通工程问题将扩大其在交通领域的影响。该奖项反映了NSF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.