创新背景

低碳出行对保护环境和节约能源都是至关重要的一环，而城市交通问题是影响低碳出行的重要因素。完善城市交通管理，对于城市发展和低碳出行都至关重要。

创新过程

AI和城市管理结合，利用物联网、大数据、云计算等技术解决城市管理问题已经作为新兴产业在不断向前发展。2019年，西安打造了中国首个AI接管“情指勤督宣”实战应用的西安交警城市大脑指挥中心，为国内“AI+城市管理”做出示范。

“AI+城市管理”在促进城市问题尤其是高动态的交通管理问题上发挥了重要作用，它也为减少排放、低碳出行提供了新可能。人们的出行往往和生活必需采买、工作、上学、娱乐挂钩，AI在监控城市全方面运行状态的同时，可以为出行提供充分有效的实时数据。

在交通高峰期时提醒非必要出行的人群可以暂缓行动或者采取步行、自行车等绿色出行方式，规划合适的路线，为不同目的人群提供错峰出行方案。同时利用高强度算法，为人群出行目的和方式计算数据，提醒每周至少一次轻松休闲时段的低碳出行，错开使用汽车、公交车等高排放的交通工具，确保城市大多数人都能交错循环低碳出行，以最合适的方式和路线完成出行目的。

利用计算机视觉、物联网和AI保证资源利用合理，最大限度减少浪费并控制出行安全和车辆数量，规划特定道路疏通急用事件和大众出行造成的交通拥堵，真正达到错峰、合理、绿色、安全出行。此外，AI可以实时监控城市温室气体和废物排放量，为城市管理提供及时、有效的监控数据，促进城市排放得到合理控制，最大化能源和环境保护效率。

创新关键点

AI+城市管理控制城市能源利用和排放，促进低碳出行发展。

 "AI + urban management" to create a low-carbon travel model

The combination of AI and urban management, the use of Internet of Things, big data, cloud computing and other technologies to solve urban management problems has been developing as an emerging industry. In 2019, Xi'an built the first Xi'an Traffic Police City Brain Command Center in China where AI took over the actual combat application of "Emotional Guidance, Diligence, Supervision and Propaganda", setting a model for domestic "AI + city management".
"AI + urban management" has played an important role in promoting urban issues, especially high-dynamic traffic management issues, and it also provides new possibilities for reducing emissions and low-carbon travel. People's travel is often linked to the necessities of life to buy, work, go to school, and entertainment. AI can provide sufficient and effective real-time data for travel while monitoring the overall operation status of the city.
Remind people who are not necessary to travel during peak traffic hours to suspend their actions or take green travel methods such as walking and bicycles, plan appropriate routes, and provide staggered travel plans for people with different purposes. At the same time, high-intensity algorithms are used to calculate data for the purpose and mode of crowd travel, reminding at least once a week of low-carbon travel during relaxed leisure time, staggering the use of high-emission vehicles such as cars and buses, and ensuring that most people in the city can stagger the cycle. Low-carbon travel, complete the travel purpose in the most suitable way and route.
Use computer vision, Internet of Things and AI to ensure reasonable resource utilization, minimize waste and control travel safety and the number of vehicles, plan specific roads to clear emergency events and traffic congestion caused by public travel, and truly achieve staggered, reasonable, green, and safe travel . In addition, AI can monitor urban greenhouse gas and waste emissions in real time, provide timely and effective monitoring data for urban management, promote reasonable control of urban emissions, and maximize energy and environmental protection efficiency.