The urban micro-renewal process has entered the phase of building complete communities. The "15-minute living circle" is not just a spatial planning layout; it critically tests the response precision at the governance frontlines. The key to technology truly serving the construction of modern people's cities lies in a deep transformation from algorithm-driven efficiency to a governance logic of resident co-governance. As urban road intelligences descend from main arteries to neighborhood alleyways, roadside edge servers transform into "digital stewards," operating quietly in daily scenarios such as school pick-ups, express delivery, senior mobility, and square dancing.
Urban road intelligences are extending from main roads to residential doorsteps, showing a clear trend of community-level integration. For a long time, smart transportation construction has focused primarily on urban main roads. Due to factors like cost, size, and deployment conditions, high-mounted cameras, millimeter-wave radar, and edge computing nodes have struggled to cover the narrow branch roads of older communities. The Central Urban Work Conference called for building modern people's cities that are innovative, livable, beautiful, resilient, civilized, and smart. The "15th Five-Year Plan for Urban Renewal" emphasizes "improving community-embedded service facilities in cities and promoting the expansion and upgrade of the 15-minute convenient living circle construction." Driven by policy and technological progress, urban road intelligences are accelerating their deployment into residential communities.
The application of a new generation of roadside edge servers has made the "one pole, one brain" smart road construction model a reality. These devices integrate multiple facilities like light poles and surveillance poles into a single unit, powered by existing streetlight power distribution. They can perform data recognition and judgment locally at the pole, eliminating the need to send everything back to the cloud, significantly reducing deployment and operational costs. Consequently, smart transportation is beginning to cover community spaces previously inaccessible due to economic constraints. For example, low-traffic sections like those around older neighborhoods and back alleys can now achieve basic functions like streetlight energy saving, charging station management, and security alerts with lower investment, truly breaking through the "last mile" of smart transportation.
The Road Source Large Model can be understood as an AI brain that "reads the road." By identifying traffic flow, pedestrian flow, stopping, lingering, and abnormal events, it provides real-time decision-making basis for various governance links. Currently, related applications are being deployed in multiple locations. For instance, in Haimen District, Nantong City, Jiangsu Province, guidance robots and roadside nodes are deployed at school gates to switch guidance modes based on peak traffic flow and dynamically adjust temporary parking areas. In Duofu Community, Shenyang City, Liaoning Province, fall detection algorithms are deployed on edge AI devices to promptly monitor the safety of elderly residents. In Huaqiangbei, Shenzhen City, Guangdong Province, by identifying illegal parking of delivery and takeaway vehicles, the system sends gentle persuasion messages to guide riders to correct their behavior. This shows that algorithmic deployment can both improve community governance efficiency and preserve the warmth of urban governance.
The "Roadside Steward" is built on the core concept of algorithms being benevolent rather than replacing, constructing a governance logic of multi-stakeholder co-governance. The long-term effectiveness of institutional rules lies in engaging stakeholders in rule-making. Algorithmic rules are a new form of institutional rules. The allocation of algorithmic decision-making power must fully involve community residents to elevate the level of modern grassroots governance. The core essence of community micro-renewal is resident participation and resident sharing. The priority setting for roadside edge node functions must also be scientifically determined through broad resident participation.
The community co-governance model for roadside edge nodes can be divided into four stages. First, formulate a community needs list. Led by the community Party organization, a resident council meeting is convened where stakeholders such as the homeowners' committee, property management company, and merchant representatives jointly identify pain points in community road governance and determine governance priorities. Second, determine the functional priority of the roadside edge server. Through mini-programs or offline voting, residents vote on the functions to be implemented by the edge nodes, forming a community-level functional configuration plan. Third, enable algorithmic fine-tuning participation. Community youth with relevant professional backgrounds are invited to participate in annotating training data, allowing the community fine-tuned version of the Road Source Large Model to better align with local habits. Fourth, establish a feedback mechanism for governance effectiveness. Operational data is published quarterly, and the community resident council meeting is reconvened to assess the weight of functions requiring adjustment.
The computing center drives governance from fragmented perception to holistic intelligent governance, playing a core role in community coordination. A single roadside edge node solves local problems. To generate synergistic effects within the "15-minute living circle," a district-level intelligent computing center must be established as a coordination hub, preventing risks of data misuse from over-centralization.
An "1+N" (1 for district computing center, N for community edge nodes) community architecture is being built. Each community deploys several roadside edge servers for real-time response, while a small intelligent computing center is positioned at the district level, coordinating coverage of 3-5 surrounding communities. It mainly performs three functions. First, multi-node coordination. When a community edge node detects road congestion, it can coordinate with neighboring nodes to adjust signal timing or issue detour suggestions. Second, district-level fine-tuning of the urban Road Source Large Model. Based on the uniqueness of urban residential communities, the model can handle special situations like sudden tourist surges in peak seasons or abrupt changes in morning and evening peaks during the school season. It conducts localized retraining by aggregating anonymized data from each community edge node, forming micro-models adapted to the seasonal characteristics of the district. Third, promote cross-departmental data sharing. Connect data platforms from street offices, urban management, traffic police, sanitation departments, etc., to prevent duplicate construction of edge equipment by various departments. The key to the effective implementation of this architecture lies in clarifying the construction and operation entity of the street-level computing center at the district level. Professional operation can be entrusted to city investment companies or leading tech enterprises, and cross-departmental data sharing agreements should be signed to ensure the technical architecture is deeply embedded in the institutional mechanisms of urban governance.
The lesson from this reform is that the "temperature interface" of technological governance needs institutional implementation. The effectiveness of urban road intelligences at the community level depends on whether a robust "temperature interface" is established, allowing algorithms to hear public opinion and data to see people's needs.
First, establish an "algorithm hearing" system. For functional adjustments of edge nodes that affect the immediate interests of community residents, a community hearing should be held. The algorithm development team explains the technical principles, fully solicits opinions from community residents, and a vote decides whether to modify the function parameters. Second, create a digital social worker position. Each community is equipped with a full-time social worker who understands both technology and local residents. This worker explains the working principles of edge nodes to community residents, collects usage feedback, and assists elderly residents in operating functions like one-click calling and electronic fences. Third, establish a community micro-renewal project fund. A certain percentage of data operation revenue is explored for use in community micro-renewal projects like adding benches or planting greenery, forming a virtuous cycle of "technology gains feeding back into the community."
Technology is merely a means; governance is the end goal. Urban roadside edge servers should not just be outward-facing "surveillance eyes"; they must also become inward-listening "ears for co-governance." Only then can the governance frontiers of the "15-minute living circle" be truly reshaped, building a new order of algorithmic social governance that is perceptible, participatory, and accountable to residents.