Muhamad Azfar Ramli

Electrifying ports transfers energy use from fossil fuel to electricity, but annual energy projections alone do not establish the grid connection and substation capacities required during coincident vessel service events. This study develops a capacity planning framework that converts pathways for throughput, vessel mix, equipment electrification, onshore power supply (OPS), terminal migration and flexibility into ensembles of annual hourly demand trajectories. A multiscale graph state-space model with conformal prediction (MGSS-CP) is used as the mapping from operational events to load rather than recursively forecasting one hour ahead for ten years. Annual maxima provide capacity quantiles, capacity gaps and reinforcement deferral. On the chronological 2025 hold-out, MGSS-CP attains a one-hour mean absolute error of 1.06 MW and an R2 of 0.957, while its 90% interval coverage remains 0.899 to 0.901 across the evaluated horizons. Under the fully electrified pathway, annual demand reaches 625.7 GWh and the central scenario peak reaches 122.6 MW by 2035. Scaling annual energy at the 2025 load factor gives 102.0 MW. It therefore understates the peak by 20.6 MW and the additional capacity above 2025 by about 40%. Alternative event placement raises the peak to about 129 MW, whereas an assumed 18% peak shaving case lowers it to about 100.5 MW and indicates 22.1 MW of gross reinforcement deferral. The results show why port capacity planning needs a validated hourly representation of operational events, explicit long-term pathways and uncertainty statements appropriate to each horizon.

Unplanned train service disruptions are a major concern for cities with high public transport ridership. Train service disruptions causes rapid degradation of level of service provided by transit systems. Understanding the travel choices made by transit passengers during service disruptions is a crucial step in managing such disruptions. Using two revealed preference datasets from Singapore that include 1) data from a questionnaire survey that asked participants about their travel plan choices during a major service disruption and 2) transit smart card data that provided the transit routes taken by commuters during a major service disruption, this study models the decision making process of commuters faced with unplanned train service disruption. We propose a three stage sequential decision making framework, that include three separate Multinomial logit (MNL) models to predict the decision to travel, the mode choice and the transit route choice. The data and the estimated models show that the commuters have high preference to go on with their journey plan using alternative public transit routes, in particular by using a bridging bus service to substitute the disrupted segment of the train network. The estimated models can be used to design effective mitigation strategies for unplanned train service disruptions.

Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin-Destination demand matrices from flow counts alone. Previous network tomography-based demand estimation techniques have been limited by the network size. The amount of partitioning changes the Origin-Destination estimation optimisation problems to different levels of computational difficulty. Different approaches to utilising the partitioning were tested, one which degenerated the road network to the scale of the partitions and others which left the network intact. Applied to a subnetwork of England's Strategic Road Network and other test networks, our results for the degenerate case showed flow and travel time errors are reasonable with a small amount of degeneration. The results for the non-degenerate cases showed that similar errors in model prediction with lower computation requirements can be obtained when using large partitions compared with the non-partitioned case. This work could be used to improve the effectiveness of national road systems planning and infrastructure models.

Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model incorporating the network structure of stations, differences in line capacities, and travel demand. Our method provides a holistic assessment of network resilience and allows for straightforward comparisons of different scenarios including rail expansions and changes in demand. Applying our methodology to multiple configurations of Singapore’s rapid transit system, we demonstrate its effectiveness in capturing the impact of planned future lines. We also showcase through simulated studies how tipping points in resilience arise when demand varies. Furthermore, we demonstrate that system resilience could be unintentionally reduced by redistributing commuting demand to peripheral areas. Our methodology is easily applied to other rapid transit systems around the world.