Future Mobility

Adverse climate actions and their impact on human well-being and the urban environment have induced significant degradation of urban infrastructure and transportation networks, disrupting societies and communities to such an extent that existing reactive transport systems may fail to withstand. Moreover, due to the computation complexity of infrastructure dependencies, most of the existing studies on transport network resilience primarily aim at specific disruptive events while rarely considering system resistive, absorptive, and restorative capacities, but may not assess the occurrence of compound failures that can propagate to other critical infrastructures. Not limited to the mobility of human traffic alone, the changing climate also has an effect on the utilities, including the mobility of essential commodities, which may also include the energy sector. The performance of the mobility sector is also typically dependent on the subsurface and soil infrastructure. In general, it is critical to design resilient transportation systems that are reactive and able to recover from disruptions (short and long) to an operational level-like scenario before the disruption by employing innovative research to create a digital twin and deploy a responsive and time-dependent resilience model using Artificial intelligence (AI) capabilities. On the other hand, sensors can detect anomalies, mainly when the values of critical variables go above a threshold. Big data applications cannot predict future behaviour despite their ability to provide real-time situations. Simulation-based approaches, including early detection and warning systems, ensure climate resilience and safe future mobility.

• AI-based multimodal transport resilience model (Lead PI – Susilawati)

• Interdependencies resilient infrastructure that considers compound failures and critical infrastructures interdependencies (Lead PI – Susilawati)

• Urban Transformation Network (Lead PI – Susilawati)

• AI-based traffic data generation and model enhancement for dynamic traffic status inference with insufficient data (Lead PI – Ziyuan Pu)

• A hybrid framework for the localised early detection and assessment of the fate of flammable gas leaks in soil (Lead PI – M E Raghunandan)

• Mechanism governing the influence of pharmaceutical wastes on water retention and hydraulic conductivity of clays (Lead PI – M E Raghunandan)

• A novel Incremental Learning with PCA Convolutional Neural Network (ILPCNN) for fine-grained vehicle model recognition (Lead PI - Tan Chee Pin)

• Development of shared autonomous vehicle model with public transport for first and last mile connectivity (Lead PI – Susilawati)

• Dynamic user equilibrium with macroscopic fundamental diagram for disaster management and emergency evacuation (Lead PI – Susilawati)

• Seismic hazard assessment of soil deposits from Peninsular Malaysia: Fundamentals of Earthquake Resistant Design (Lead PI – M E Raghunandan)

• Scientific and technological approach to assess the impact of elevated canopy walkway and Bus Rapid Transit (BRT) on traffic in Bandar Sunway (Lead PI - Tan Chee Pin)

Current Achievements