Amr Oloufa

Accurate early-stage assessment of building energy and carbon performance is essential for informed sustainable design yet remains challenging due to limited design detail and simulation effort. This study presents a Building Information Modeling–Machine Learning (BIM-ML) framework for predicting office building energy and carbon performance at early design stages using simulation-based datasets. A reduced-factorial Design of Experiments (DOE) generated 210 parametric office building models for Orlando, Florida (ASHRAE Climate Zone 2A), complemented by additional climate scenarios. Systematic variations in geometry, envelope, building systems, and operational schedules produced a dataset with 14 independent variables and five performance indicators: Energy Use Intensity, Operational Energy, Operational Carbon, Embodied Carbon, and Total Carbon. Four regression methods—Linear Regression, Model Tree (M5P), Sequential Minimal Optimization Regression, and Random Forest—were trained and evaluated using 10-fold cross-validation. Random Forest showed the strongest overall predictive performance. Feature-importance analysis identified HVAC system type, Window-to-Wall Ratio, and operational schedule as the most influential parameters, while geometric factors had lower impact. Cross-climate analysis and validation with measured data from two university office buildings indicate that the framework is adaptable and generalizable, supporting reliable early-stage evaluation of energy and carbon performance.

A Building Information Modeling (BIM)-based Machine Learning (ML) framework was developed to predict the energy performance of office buildings at the early design stage. The framework provides a reproducible and data-driven workflow that shortens simulation time while maintaining accuracy. Revit and Insight were integrated with statistical modeling in Weka to create an automated and regionally adaptable process derived from BIM-generated data. A reduced-factorial Design of Experiments (DOE) guided the generation of 210 parametric simulations representing base, generalization, and stress-test models for Orlando, Florida. Each model combined geometric, envelope, system, and operational variations, forming a dataset of 14 independent parameters and two dependent energy metrics: Energy Use Intensity (EUI) and Operational Energy (OE). Four regression algorithms—Linear Regression (LR), M5P, SMOReg, and Random Forest (RF)—were trained and validated through 10-fold cross-validation. All models achieved R² values above 0.95, with the RF model reaching the highest overall accuracy under default parameter settings, with R² > 0.97 and mean absolute errors below 5% across both metrics, EUI and OE. Feature-importance analysis identified HVAC system type, window-to-wall ratio, and operational schedule as the most influential variables. Results confirm that BIM-ML integration enables rapid and reliable energy-performance prediction, supporting informed, energy-efficient design decisions in the earliest phases of the building lifecycle.

Traffic crashes remain a critical public safety issue and are among the leading causes of mortality worldwide. Understanding, analyzing, and forecasting crash trends are essential for implementing effective countermeasures and reducing injury severity. In response to the growing number of crashes and their associated economic and social costs, this study presents a geospatial analytical framework for prioritizing and classifying roadway segments based on crash trends. The framework focuses on a major freeway corridor in the United States, covering a four-year period across 20 counties. This methodology employs spatiotemporal analysis, which integrates both spatial (geographic) and temporal (time-based) dimensions to better understand how crash patterns evolve over time and space. A central component of the analysis is Space–Time Cube (STC) modeling, a three-dimensional GIS-based visualization, and an analytical approach that organizes data into spatial locations (x and y) across a sequence of temporal bins (z-axis) to reveal patterns that may not be evident in a two-dimensional analysis. Additionally, emerging pattern analysis, specifically Emerging Hotspot Analysis (EHA), is used to identify statistically significant trends in crash frequency over time. The results indicate a significant spatial clustering of crashes, with high-risk segments predominantly located in densely populated urban areas with high traffic volumes. Crash hotspots were classified into five distinct categories: persistent, intensifying, new, sporadic, and diminishing, enabling transportation agencies to tailor interventions based on temporal dynamics. The proposed geospatial framework enhances decision making for roadway safety improvements and can be adapted for use in other regional corridors to support infrastructure investment and advance public safety.

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems.

This study evaluates the impact of dedicating an existing highway lane as an Exclusive Truck Platooning Lane (ETPL). Using real-world traffic data, the analysis highlights operational efficiency and environmental benefits before and after ETPL implementation, focusing on Florida’s Strategic Intermodal System (SIS) corridors. The study reallocates the existing rightmost lane, prioritizing freight traffic to improve performance. Advanced simulation tools such as VISSIM and MOVES, combined with GIS-based spatial analysis, were employed to assess travel time and emissions impacts under various scenarios. These included alternate speeds, traffic volumes, and lane configurations. The results demonstrated that ETPL implementation significantly reduced travel time and emissions for freight trucks. The findings also revealed a minor increase in passenger vehicle travel time; however, the associated costs were negligible compared to the substantial savings achieved through improved freight efficiency. Emission reductions were visualized through GIS-generated heat maps across the study area, with the greatest benefits observed at lower speeds. This methodology provides actionable insights for decision-makers, identifying high-priority corridors for targeted interventions and resource allocation, ultimately enhancing traffic flow and reducing environmental impacts.

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

This research reports on the development of a model to measure the value of a given traffic strategy for freight truck traffic through the perspective of multiple involved agencies. The model was designed as an Analytical Hierarchy Process (AHP) model, wherein each perspective was broken down into sub-criteria in subsequent tiers. These sub-criteria would then be compared to each other to determine the overall weights of each perspective in evaluating different traffic control strategies. The model was created to determine the value of four specific perspectives: the Economic, Safety, Environmental, and Mobility Perspectives of a given traffic control strategy. These perspectives were tested through the surveying of 24 experts within the state of Florida involved in the field of transportation, who were consulted to determine the weights of each perspective and its criteria using pairwise comparisons. Once completed, the model was then tested to evaluate the overall priority of three alternatives: one in which no strategy was employed, one in which a strategy was employed at a speed of 55 mph, and one in which it was employed at 75 mph. The results showed that safety was selected as the most important perspective among all the consulted experts. Since the strategy of implementation at 55 mph was deemed the safest, it was calculated as the most sustainable alternative, despite its lower speed and efficiency lowering the economic and mobility priorities. This supports the implementation of the AHP model in upper-level decision making, as it allows management to determine the best decision that will placate all lower-level parties, preventing conflict and increasing overall workplace efficiency.

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.