Urban centers around the world are facing common issues such as traffic congestion and frequent accidents. These “urban transportation diseases” not only deteriorate the commuting experience but also lead to substantial economic losses. According to estimates, congestion accounts for approximately 20% of urban residents’ disposable income, translating to an annual economic loss of about 250 billion RMB—roughly 5% to 8% of the city’s GDP¹. Alleviating congestion and enhancing traffic efficiency has thus become a critical issue in improving residents’ quality of life and income levels. Against this backdrop, artificial intelligence (AI) is rapidly emerging and progressively influencing the transportation sector. Intelligent transportation systems (ITS), empowered by AI, big data, and the Internet of Things (IoT), aim to reduce congestion, improve safety, save energy, and support innovations such as autonomous driving. For businesses, AI-driven smart transportation not only transforms the way cities operate but also reshapes industrial structures, opening up new opportunities.

Applications of AI in Intelligent Transportation Systems

By leveraging AI’s powerful data analytics capabilities, transportation authorities can process vast amounts of data collected from cameras, speed detectors, and other sensors in real time to forecast future traffic flows and alleviate congestion. For example, AI-powered adaptive traffic light systems can analyze real-time traffic data and adjust signal timings to match current traffic conditions. Neural networks and other algorithms can use historical data to predict short-term traffic states, enabling managers to take proactive measures. Through continuous machine learning, these models optimize urban traffic strategies dynamically and holistically. They offer accurate and efficient tools to support decision-making in signal control and scheduling.

One of the most prominent AI applications in transportation is autonomous driving. Using computer vision, deep learning, and sensor fusion technologies, self-driving cars can perceive their surroundings and make real-time decisions regarding route and speed. In assisted driving, AI-powered advanced driver assistance systems (ADAS) have already been widely adopted, offering functionalities such as collision warnings and intelligent navigation, thereby enhancing road safety and driver comfort. As technology matures, the global autonomous vehicle market is growing rapidly—from approximately US$1,921.1 billion in 2023 to US$13,632.4 billion in 2030, with a compound annual growth rate of 32.3%². This tremendous market potential has attracted significant investment from companies around the world. From Tesla in the U.S. to a new wave of EV startups in China, more automakers are incorporating autonomous and assisted driving technologies into their core offerings. For instance, Huawei’s ADAS-powered AITO brand helped elevate Seres’ stock price from under 30 RMB to over 120 RMB within a year. This trend not only reflects the industry’s technological trajectory but also reshapes consumer preferences, with ADAS becoming a key factor in purchasing decisions. Clearly, AI-empowered driving systems are shaping the future of the auto industry and paving the way toward full autonomy.

Opportunities in Digital Transformation

A significant benefit of digital transformation for enterprises is the substantial improvement in operational efficiency. By digitizing workflows and applying AI to analyze vast operational data, decision-makers can identify issues and optimize processes. In logistics and public transportation, AI-enhanced route planning systems can integrate real-time traffic, weather, and historical data to determine optimal routes and delivery sequences. In the realm of shared mobility, AI also drives efficiency gains. The platforms such as Uber can use machine learning to forecast supply-demand dynamics and intelligently match vehicles with passengers to reduce idle time and waiting periods. These data-driven optimizations enable companies to achieve more with fewer resources, improve service quality, reduce operational costs, and generate higher commercial value. In today’s digital economy, “data-driven decision-making” is essential for competitive advantage. Companies that can extract actionable insights from data using AI will significantly outperform traditional counterparts in efficiency and scalability.

AI-assisted smart transportation also offers opportunities for fulfilling corporate social responsibility and promoting sustainable development. In terms of safety, AI-powered monitoring and driver assistance systems can substantially reduce accident rates. Equipped with cameras, LiDAR, radar, and intelligent systems, vehicles can identify potential hazards, issue warnings, apply brakes automatically³, or even change lanes to avoid collisions. Statistics show such systems can reduce accident rates by 20% to 30%, protecting drivers, passengers, and pedestrians. Moreover, with AI optimizing traffic signals and route planning, average travel times and unnecessary detours are significantly reduced. In cities like Singapore, which have implemented AI-based traffic control, congestion has reportedly decreased by approximately 25%⁴, with a corresponding reduction in vehicle emissions.

In sum, AI-enabled digital transformation not only generates business value but also offers enterprises a chance to innovate for social good—improving road safety and minimizing environmental impact. For businesses committed to social responsibility and long-term sustainability, this presents a unique and valuable opportunity.

Challenges and Future Developments

Despite its promising outlook, AI-powered smart transportation faces several challenges in the digital transformation journey. For many small and medium-sized enterprises, adopting AI entails considerable investment in software development, hardware upgrades, and specialized talent. Since the rise of ChatGPT, AI development has become increasingly dependent on computing power. From large language models to advanced driving systems, companies are now showcasing how much they’ve spent on compute as a badge of capability. However, such compute costs are often beyond the reach of most small and medium-sized enterprises.

Fortunately, improvements in algorithmic efficiency offer an alternative path. The recent open-source AI model DeepSeek has gained popularity online precisely because of its low deployment cost. Developers have shown that the DeepSeek 7B⁵ model can be deployed locally with just 4,000 RMB in hardware, while the more powerful DeepSeek 70B model requires under 100,000 RMB. Local deployment also ensures data privacy—crucial for enterprise applications. In this way, open-source and efficient algorithms are making AI adoption increasingly accessible to smaller firms. Even without developing new models, companies can benefit greatly by recruiting AI professionals to fine-tune and apply existing open-source tools to their business contexts.

On another front, the fast-paced progress of AI has outpaced policy and regulatory developments, introducing legal risks that businesses must carefully manage and prepare for. For instance, although autonomous driving has been a hot topic for over five years, regulatory standards for different autonomy levels remain vague, and issues such as liability and legal status are still unresolved. This uncertainty may deter small and medium-sized enterprises from investing in R&D due to potential commercialization barriers. According to a recent survey, 50% of small businesses cite regulatory uncertainty as a major concern when considering AI adoption⁶. To address this, businesses should stay informed about policy trends, actively participate in industry associations and standards discussions, and advocate for their interests. Establishing dedicated compliance teams or seeking legal consultants can help ensure that products and services align with current regulations while remaining flexible enough to adapt to future policy shifts.

In conclusion, AI is becoming a powerful driver of intelligent transportation system development. For enterprises, embracing this trend of digital transformation offers numerous possibilities: alleviating traffic issues, generating significant socio-economic benefits, improving operational efficiency, reducing costs, and enhancing safety and sustainability. At the same time, it is essential to remain mindful of the challenges introduced by technological investment and regulatory compliance. By balancing innovation with responsibility, businesses can seize the transformative potential of AI and pursuit the long-term success.

Reference

[1] 王卉彤, & 劉傳明. (2020). 中國100個城市交通擁堵的空間格局研究. 財經研究, (1), 62–72.

[2] Keymakr. The Future of Mobility: AI in Automotive & Transportation, 2023​

[3] Bipartisan Policy Center. Small Business and AI: 2023 Poll Results, 2023

The work described in this article was supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies (AIFT).
(AIFT strives but cannot guarantee the accuracy and reliability of the content, and will not be responsible for any loss or damage caused by any inaccuracy or omission.)

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1. 王卉彤, & 劉傳明. (2020). 中國100個城市交通擁堵的空間格局研究. 財經研究, (1), 62–72. ↩︎
2. https://www.fortunebusinessinsights.com/zh/autonomous-vehicle-market-109045 ↩︎
3. The intelligent driving system and related equipment have significantly improved the accuracy and range of the automatic emergency braking system (AEB) in vehicles. ↩︎
4. Keymakr. The Future of Mobility: AI in Automotive & Transportation, 2023 ↩︎
5. The parameter scale (B = Billion) represents the complexity and learning capability of the model; generally, the more parameters there are, the stronger the understanding and generation abilities. ↩︎
6. Bipartisan Policy Center. Small Business and AI: 2023 Poll Results, 2023​ ↩︎