Extending Max-Pressure Control for Traffic Network Operations

交通信号配时-在信号控制的十字路口分配通行权的方式-可能会显著影响车流，并在城市中造成延误。本项目通过数学证明和实验结果，开发了一种新的信号定时(最大压力控制)的自适应方法。最大压力控制利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的关键基本限制，这些限制阻碍了实施并限制了实际好处。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好处。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，并最大化拼车服务的客流，如优步和Lyft。从这个项目中获得的数学知识将通过研究生院的课程和博士生辅导直接教授。为提高公众对所研究问题的认识，我们将向K至12年级的学生开展外展活动。这项计划将在现有的存储转发排队模型中引入现实的交通流假设，即高密度的流量限制、队列溢出和先进先出行为。由于最大压力信号计时通常依赖传感器来估计排队长度，因此它将调查其他数据来源来计算排队压力，即道路行驶延误。在这些任务中，该项目将搜索新的Lyapunov函数并修改最大压力控制以保持最大稳定性结果。仿真结果将被用来研究当用户根据新的信号定时改变他们的路线时对拥塞的影响。该项目将通过与明尼苏达州的亨内平县合作，在他们的交叉口进行微观模拟和试点部署，进一步调查实际效益。最后，该项目将通过使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的乘客服务。从本质上讲，这项研究将把最大压力控制的诱人的解析性质纳入到一个更准确的模型中，以便在城市中有效实施，在实践中实现好处。基于李亚普诺夫的最大稳定性证明的成功在智力上的挑战是巨大的。此外，将最大压力方法应用于其他具有相似建模目标的交通工程问题将扩大整个交通领域的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.