Smart Transportation

Definition and Technical Architecture

Smart transportation represents an advanced evolution of traditional Intelligent Transport Systems (ITS), fully integrating next-generation information technologies including the Internet of Things (IoT), cloud computing, big data analytics, Artificial Intelligence (AI), and 5G connectivity. Its core philosophy centres on harnessing advanced technologies to aggregate transport data, supporting end-to-end management and control across traffic administration, public transport, and commuter journeys, as well as the entire lifecycle of transport infrastructure development. This empowers the transport network with perception, connectivity, analytical, predictive, and control capabilities across urban, regional, and wider spatial-temporal scales.

Smart transportation can be understood as the practical application of Cyber-Physical Systems (CPS) in the mobility sector—an integrated framework combining holistic perception, communication, decision-making, and control. By seamlessly bridging computing power with real-world traffic operations, it ensures travel and haulage become safer, more convenient, highly efficient, greener, and more economical.

Evolution from ITS to Smart Transportation

The concept of smart transportation traces its roots back to Intelligent Transport Systems (ITS) in the late 1980s. China's intelligent transport sector has progressed through three distinct phases: technology adoption and absorption (1995–2005), integrated application and pilot demonstrations (2006–2014), and "Mobile Internet + Industrialisation" expansion (2015 to present). Essentially an advanced iteration of ITS, smart transportation leverages IoT and cloud computing to foster better synergy between motorists, vehicles, and road infrastructure while delivering more commuter-centric public transport services. This marks a qualitative leap from "signals to data, data to information, information to knowledge, and knowledge to intelligence."

Core Architecture and Key Technologies

The technical architecture of smart transport systems generally follows a four-tier framework: "Perception Layer – Transmission Layer – Platform Layer – Application Layer". At the perception layer, roadside infrastructure—including cameras, millimetre-wave (mmWave) radars, LiDAR sensors, and IoT devices—is deployed to deliver comprehensive situational awareness of traffic volume, vehicle dynamics, and road conditions. The transmission layer harnesses high-speed networks such as 5G and C-V2X to facilitate ultra-low-latency vehicle-to-everything (V2X) data exchange across vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-cloud (V2C) channels. At the platform layer, edge computing nodes work alongside cloud control platforms to process and fuse multi-source data, generating dynamic traffic control strategies and real-time navigation feeds. Finally, the application layer delivers intelligent decision-making tools and services tailored for traffic enforcement, daily commutes, and commercial logistics.

China's Practice of "Vehicle-Road-Cloud Integration"

At present, China's primary implementation roadmap for smart mobility centres on "Vehicle-Road-Cloud Integration". This model harmonises intelligent vehicles, smart road infrastructure, and robust cloud platforms to achieve bidirectional synergy. Taking Wuxi as an example, the city pioneered nationwide 100% networked coordination across 4,253 signalised junctions, establishing 147 coordinated "green wave" corridors spanning a total of 589 km and cutting round-trip vehicle travel times by 25%. In Suzhou, the Intelligent Connected Vehicle (ICV) Cloud Control Basic Platform has been linked across multiple districts, supplying autonomous and smart vehicles with live traffic light countdowns and real-time telemetry, while simultaneously harvesting roadside incident data to train driving algorithms.

In Tongxiang, vehicle-road-cloud infrastructure enables autonomous test vehicles to accurately anticipate traffic light phases via cloud data and pull away in tandem, even when their onboard line of sight is completely blocked by heavy lorries—marking a technological leap from "passive visual detection" to "proactive cloud awareness". In Fujian Province, pilot vehicle-road-cloud collaborative projects along the Shenhai Expressway (specifically the Fuquan and Quanxia stretches) utilise mmWave radar and sensor arrays for traffic flow forecasting, early hazard warnings, and coordinated traffic management.

Trends in Driverless and Autonomous Mobility

The ultimate goal of smart transportation is full autonomy and driverless mobility, creating a transport ecosystem capable of "thinking" and reacting like a human. In the future, smart transport networks will be underpinned by ubiquitous space-air-ground integrated data networks. Through agile planning and multi-modal integration, systems will deliver rapid cross-network responses, alongside tailored travel recommendations driven by combinatorial optimisation and human-machine interfaces—ensuring journeys are far more accurate in timing, precise in spatial routing, and refined in service quality for road users.

Technical Limitations and Safety Boundaries

Smart transportation serves as critical systemic infrastructure underpinning smart mobility and modern traffic governance. However, the efficacy and real-time reliability of its information services remain heavily reliant on telecommunication network coverage and stability, the resilient deployment of roadside sensing hardware, and cloud computing capacity. During adverse weather, telecommunication dropouts, or hardware faults, system performance may degrade or face temporary service disruptions. Furthermore, rolling out large-scale sensor grids, edge computing servers, and V2X roadside units demands substantial capital expenditure, with ongoing operations requiring continuous model retraining, software maintenance, and cybersecurity patches. Crucially, motorists must not become overly reliant on smart traffic alerts or recommended speed advisories to the point of neglecting active hazard perception; drivers remain fully and solely responsible for ultimate vehicle control and road safety.