假期出行数据往往是观察消费活力与技术落地的双重窗口。在今年清明期间，出行平台发布的数据不仅反映了人流回归，更揭示了人工智能技术在交通调度深处的实际渗透。从运力调配到用户交互，智能化正在重塑传统的出行供给侧，为行业带来了新的增长变量。
On April 7, Didi Chuxing released its 2026 Qingming Festival Travel Highlights. Data shows that with the combined demand for returning home and traveling, the national taxi demand increased by nearly 15% year-on-year. Among them, cross-regional tourism consumption showed strong performance, with cross-regional taxi demand rising by 41% compared to before the holiday. Self-driving tours and AI ride-hailing have become the two major keywords of this holiday.
Key Highlights: AI Ride-Hailing Shifts from “Novelty” to “Mainstream”
This year’s most significant change lies in the deep penetration of AI technology:
Explosive Growth: In the week before the holiday, the number of users using Didi’s AI ride-hailing increased by 37 times compared to the beginning of the year.
High Demand During the Holiday: AI ride-hailing demand continued to rise during the Qingming Festival, with orders increasing by 86% compared to regular days.
Preferred by Younger Generations: The post-00s generation has become the main force in AI travel, accounting for more than 40%.
User Portrait: Not Just “Fast”, but Also “Understanding Me”
The AI assistant “Xiaodi” is reshaping the ride-hailing experience. Users can match personalized services with a single sentence command (such as “I need to take the elderly out and want to walk less”):
Popular Tags: “Fast and cheap,” “Fresh air,” and “The nearest car” remain the top three demands.
Increased Personalization: Tags such as “SUV for multiple people,” “Large trunk,” and “Frequently drives long distances” have seen a significant increase in demand.
Complex Planning: AI ride-hailing orders with stopover points increased by 111%, showing that users have become accustomed to letting AI plan multi-stop pickup and drop-off routes.
Travel Trends: “Returning Home + Outing + Micro Vacation” Combined
With increasingly efficient transportation connections, the Qingming holiday has shown clear characteristics of being composite:
Direct Connections: Orders for taxi rides directly from airports and train stations to scenic spots increased by 25% year-on-year.
“Flower-Rich” Cities: Taxi demand for parks and botanical gardens is strong. In second-tier and above cities, Baoding, Dalian, Dongguan, Shenzhen, Kunming, and other cities show the most prominent “flower appreciation” trends.
Popular Destinations: Cities like Yinchuan, Lanzhou, Urumqi, and Changsha saw the highest growth in taxi demand.
Energy Supply: Car Rentals and Charging Demands Rise Together
Data from
Outstanding Cities: Car rental orders in areas such as Yili, Lanzhou, and Yinchuan grew the fastest nationwide.
Family Travel Proportion: Orders for family self-driving trips increased by 58%, with more than 60% of families trying car rentals for the first time.
Power Supply Needs: Didi charging demand increased by 45%, and fuel demand rose by 40% compared to regular days. Cities such as Wuxi and Wuhan had the highest charging popularity during the flower-chasing season.
Conclusion: AI Opens Up a New “Convenient” Life
这一系列数据背后，折射出出行行业正从单一的运力竞争转向生态服务能力的较量。人工智能不仅优化了匹配效率，更通过理解用户意图创造了新的需求场景，使得“打车”这一行为具备了更强的可编程性与个性化特征。
与此同时，能源网络与交通网络的协同效应日益显著。租车需求与充电热力的同步上涨，表明基础设施的完善正在消除新能源出行的里程焦虑。未来，随着大模型技术在调度系统中的进一步深入，出行服务将更加精准地匹配供需，为构建更绿色、高效的智能交通体系提供坚实的数据支撑。
