The response time of emergency vehicles (EV) is critical to saving lives. In this paper, we address the two challenges to the reduction of EV response time: 1) path-searching for a reliable estimated time of arrival (ETA) in ever-changing traffic conditions and 2) elastic signal preemption to reduce the negative impact on the whole traffic flow introduced by prioritizing the EVs. While traditional path-searching methods aim to minimize only the mean value of ETA, our approach minimizes a combination of its mean and variance, as we found that the variance of speed has a significant impact on path searching. The simulation shows that our approach generates paths with reliable ETA, i.e., lower variance with the same level of accuracy. Furthermore, we formulate the elastic signal preemption (ESP) as a set of quadratic programming (QP) problems to find non-intrusive signal schedules to the fast track EVs through junctions without stopping. When the ESP module was applied in the real world, results showed about 30% reduction of response time with little impact on the overall traffic conditions.

Traffic signal is the key method for city traffic control. Existing signal control systems use the loop detector data as the main input which is nearsighted in terms of both space and time. Lacking of effective data collection methods has hindered the development of more sophisticated models. It is therefore very hard to develop an optimization model considering all signals in a region or even a city. In this paper, we will introduce our method for large scale traffic signal optimization, which is the major module of Alibaba's city brain solution. By integrating multiple data sources to sense the whole city's traffic conditions, a layered model is developed based on the divide-and-conquer paradigm to gradually apply different types of data-driven optimization algorithms. It is a paradigm shift to use big data to improve a traditionally closed signal control system, and its effectiveness has been proven in a field test in Shanghai city.