ai intelligence in public transport

　　I. Core Challenges of AI Implementation in Public Transportation in 2025

　　Multi-source data governance challenges: Public transportation data encompasses on-board sensors (such as location and speed), platform monitoring, passenger flow statistics, and road condition information. Unstructured data accounts for over 70%, and data standards vary across departments (bus companies, traffic management, and operations and maintenance units), resulting in integration efficiency of less than 25%. Privacy protection for sensitive data such as passenger facial recognition and travel trajectories conflicts with application requirements, and data cleansing and annotation costs account for 40%-50% of total AI project investment.

　　Technical adaptation pressures for dynamic scenarios: Urban roads present complex conditions such as mixed traffic, sudden weather changes (heavy rain, smog), and signal interference, resulting in false alarm rates of up to 18%-22% for AI visual recognition. On-board AI equipment must withstand vibration (≤5g) and a wide temperature range (-40°C to 70°C). Conventional equipment has a failure rate exceeding 25%, and the initial investment in specialized equipment is 30%-50% higher than for standard equipment, placing significant cost pressure on small and medium-sized operators.

　　Talent and system synergy gap: The supply-demand ratio for talent with combined "AI technology + transportation operations" capabilities is 1:10. Over 35% of projects face inadequate compatibility between AI models and dispatching and ticketing systems. Furthermore, the lack of clear regulations defining responsibilities for AI decisions (such as automatic lane changes and emergency dispatch) raises significant concerns among businesses about their adoption.

　　Barriers to standardization and regional differences: Currently, there are no unified industry standards for AI applications in public transportation. Passenger flow prediction models and safety identification thresholds vary significantly across cities, making cross-regional data sharing and coordinated dispatching difficult. By the end of 2024, only 28% of urban agglomerations will have achieved interconnected public transportation AI systems.

　　II. Core Technology Support Solution

　　Cloud-Edge-Device Collaborative Intelligent Architecture:

　　Edge Layer: On-board edge computing gateways (computing power ≥ 10TOPS, supporting 5G-A transmission), roadside AI cameras (including millimeter-wave radar fusion modules), and platform-based intelligent terminals are deployed on-site to achieve local real-time inference (response time ≤ 200 milliseconds) and adapt to dynamic road conditions and equipment environments.

　　Cloud Layer: A large-scale traffic model training platform is built, optimizing algorithms based on multi-city operational data, responsible for global scheduling (such as cross-line capacity allocation) and model iteration.

　　Device Layer: A lightweight model is adapted to passenger mobile phones and driver terminals, achieving real-time information synchronization and reducing data transmission costs by over 60% compared to pure cloud-based architectures.

　　Multimodal Fusion Perception Technology: Integrating computer vision, millimeter-wave radar, GPS, and meteorological data, deep learning algorithms are used to construct a four-dimensional monitoring model of "vehicle-pedestrian-road-environment." For example, combining AI visual recognition with radar ranging enables millisecond-level decisions for pedestrian avoidance at intersections without traffic lights, increasing recognition accuracy to over 95% and reducing the false alarm rate to below 8%.

　　A low-code AI platform specifically designed for transportation: This platform provides a visualization tool for "passenger flow annotation - model fine-tuning - deployment iteration." It comes with 15 pre-configured templates for tasks such as "peak capacity prediction," "abnormal behavior identification," and "equipment fault diagnosis." It supports operators uploading local route data to optimize models, reducing the scenario adaptation cycle from two months to two weeks. The local deployment version allows data to be stored off the cloud, complying with privacy protection requirements.

　　A full-process intelligent collaborative system: This system establishes a closed-loop mechanism for "perception - decision - execution - feedback." The AI system can link traffic lights and onboard control systems for dynamic scheduling. Federated learning technology makes cross-enterprise data "available but invisible." This platform combines explainable AI algorithms to generate operational reports, complying with policies and regulations such as the "Implementation Opinions on 'Artificial Intelligence + Transportation'."

　　III. Key Scenarios and Application Practices

　　Intelligent Scheduling and Capacity Optimization:

　　Based on historical passenger flow data (such as morning and evening peak station traffic) and real-time data (GPS location, traffic congestion index), AI models dynamically predict passenger flow changes and generate capacity adjustment plans 30 minutes in advance. Harbin Bus, after deploying a local large-scale model, integrated weather and urban activity data to formulate time-based operation plans, reducing peak wait times by 15% and increasing capacity utilization by 22%. Shenzhen's AI-powered buses utilize vehicle-road collaboration technology to achieve real-time traffic light status awareness and dynamically adjust speeds, improving operational efficiency by 30%. The system has accumulated over 200 days of safe operation and served 41,000 passengers.

　　Active Driving Safety Prevention and Control:

　　Onboard AI devices collect real-time driver behavior data (such as fatigue driving patterns and frequency of sharp turns) and vehicle status (brake and steering system parameters) to build a "driver profile" and equipment health model. When fatigue driving or equipment anomalies are detected, an audible and visual alarm and seat vibration reminder are triggered within 10 seconds and synchronized to the backend monitoring center. After implementation in one city's public transportation system, the rate of man-made accidents decreased by 68%, and the accuracy of equipment failure warnings reached 92%.

　　Intelligent Passenger Service Upgrades:

　　AI cameras at platforms measure passenger density in real time, and push notifications such as "platform congestion" and "vehicle arrival countdown" to passengers via the app. Intelligent customer service integrates information from 99 routes and supports dialect recognition (e.g., "Where to sit on bus No. 5?"). It handles over 70% of routine inquiries (such as route inquiries and lost and found assistance), improving the response efficiency of human customer service by 50%. Face recognition and personalized stop reminders are now available on some routes, improving travel convenience for elderly passengers by 40%.

　　Hub Safety and Emergency Response:

　　Subway hubs have deployed multimodal AI monitoring systems to identify anomalies such as "carrying dangerous goods" and "stampede risk," with a recognition latency of ≤300 milliseconds. Upon discovery of a potential hazard, security personnel are immediately linked to the broadcasting system, and emergency evacuation routes are generated and sent to passengers' mobile phones. After implementation at one hub, emergency response time was reduced from 5 minutes to 1 minute, and the incidence of safety incidents was reduced by 75%.

　　IV. Development Trends 2025-2027

　　Scaling Implementation of the Integrated Transportation Model: In accordance with the policy requirements of seven government departments, a comprehensive transportation model system spanning bus, subway, and shared bikes will be established by 2027, enabling door-to-door travel planning (e.g., the optimal combination of bus, subway, and bike sharing), and improving multi-modal connection efficiency by over 35%.

　　Deep Integration of Vehicle, Road, and Cloud: 5G-A (latency ≤ 10 milliseconds) enables real-time interoperability between onboard terminals, roadside equipment, and cloud platforms. AI models can predict intersection conflicts 500 meters in advance, automatically triggering avoidance commands, improving traffic safety by 40% and reducing congestion duration by 25%.

　　Multimodal Interaction and Personalized Services: The AI system will integrate voice, gesture, and expression recognition technologies to provide customized services (such as voice guidance and barrier-free boarding reminders) for the elderly and disabled. It will also recommend "commuter-specific routes" based on travel habits, increasing passenger satisfaction by 30%.

　　Green AI and Low-Carbon Operations: Low-energy AI models reduce computing power requirements by 70% through parameter pruning. Combined with photovoltaic-powered roadside equipment, system energy consumption is reduced by 45%. AI optimizes vehicle start-stop and speed, increasing the range of electric buses by 15% and reducing carbon emissions per passenger trip by 20%, in line with the dual carbon goals.