Dr Wan Zukri Wan Abdullah, Faculty of Civil Engineering, Universiti Teknologi MARA and Rafizah Mohamed Nordin, Faculty of Built Environment, Universiti Teknologi MARA
Overview of AI and Data in Urban Mobility
In Malaysia, rapid urbanisation particularly in Kuala Lumpur, Johor Bahru and Penang, has intensified transportation challenges such as congestion, air pollution and limited public transport integration. Artificial intelligence or AI-driven applications are beginning to emerge, with initiatives like the Kuala Lumpur City Brain (developed with Alibaba Cloud) that use real-time video analytics and big data to manage traffic flow and emergency response. Malaysia’s Intelligent Transport System (ITS) initiatives also aim to integrate AI and IoT in highway tolling, traffic monitoring and predictive congestion management. However, the adoption of AI in Malaysia remains at an early stage compared to global benchmarks, largely due to gaps in data governance, interoperability between agencies and the high costs of system implementation.
Globally, leading smart cities such as Singapore, Barcelona and Pittsburgh have demonstrated how robust data management strategies can maximise AI’s potential in urban mobility. For example, Singapore leverages a comprehensive Internet of Things (IoT) network and AI-based analytics to reduce travel time by 20% and vehicle emissions by 15%. Barcelona employs AI-powered smart parking and pedestrian management systems to optimise mobility and enhance safety, while Pittsburgh’s adaptive AI traffic lights have cut vehicle idling by 40%. These global cases highlight the importance of data governance, multi-source data integration and advanced analytics, areas where Malaysia is still developing.
As Malaysian cities continue to urbanise, learning from international best practices while tailoring solutions to local constraints will be essential to achieving sustainable and efficient smart mobility. A smart city is a city that uses digital technology to enhance performance and well-being, reduce costs and resource consumption, and engage more effectively and actively with its citizens. The adoption of emerging technologies is a key factor in transforming traditional cities into smart cities. To develop smart cities, physical, digital and human systems must be effectively integrated into the built environment. Table 1 shows the comparison of AI in Smart City Transportation between Malaysia and Global Practices.
The Data Value Chain Framework
Smart city transportation systems rely on a continuous cycle of data collection, processing and application. This process can be explained through the Data Value Chain Framework, which has four phases: (1) Data Acquisition, (2) Transmission and Storage, (3) Processing and Analytics, and (4) Visualisation and Decision Support. Each phase contributes to building a foundation that allows AI applications to function effectively. As illustrated in Figure 1, data begins at the sensing level, is transmitted via cloud or edge systems, processed into insights, and finally presented through decision-support tools to urban planners, traffic managers and stakeholders.
Data Governance and Ethical Considerations
Strong data governance is essential to build trust, transparency and accountability in AI-powered transportation. Issues such as privacy, bias and data ownership must be addressed through clear regulatory frameworks. For instance, the European Union’s General Data Protection Regulation (GDPR) has set a global benchmark for data privacy, influencing smart city projects worldwide. Malaysia, however, is still developing its regulatory ecosystem, with efforts focusing on strengthening the Personal Data Protection Act (PDPA). As illustrated in Figure 2, countries with stronger data governance frameworks also rank higher in AI readiness for smart city contexts, highlighting the gap Malaysia needs to bridge to remain competitive in global AI-driven transportation. Without robust governance, public skepticism and ethical dilemmas may undermine AI adoption.
