Ahmed Abdel-Rahim

Multimodal freight transportation networks are increasingly exposed to natural and human-made disruptions, yet prior research remains fragmented in how disruptions are represented, which modeling techniques are applied, and how results are validated, limiting comparability and actionable guidance for resilient planning. This study presents a PRISMA-guided systematic review of disruption modeling in multimodal freight networks. A total of 21 studies were identified and coded to address three research questions concerning (RQ1) which analytical and computational modeling techniques are applied; (RQ2) to what extent models represent cross-modal interdependencies, cascading failures, and recovery processes; and (RQ3) what validation, calibration, and empirical testing strategies are employed. The review shows that optimization-based approaches and hybrid frameworks dominate the literature, complemented by fewer network science and data-driven methods. Most studies model disruptions as node/link failures and/or capacity degradation using static single-event scenarios, and explicit representations of cascading effects, operational delay propagation, and time-evolving recovery trajectories remain relatively rare. While many studies rely on real network data, formal calibration and historical backtesting against observed disruption events are uncommon, and validation is primarily case study-based. These findings highlight the need for more dynamic resilience modeling, stronger uncertainty quantification, standardized reporting of performance and resilience metrics, and greater use of empirically grounded validation to improve the generalizability and decision relevance of multimodal freight resilience models.

Pedestrian and cyclist safety at urban intersections remains a critical challenge for transportation agencies, as vulnerable road users are significantly exposed to crash risks in complex traffic environments. Identifying high-risk locations and factors that contribute to crashes is essential for improving road safety. This study developed an explainable machine learning framework to predict motor vehicle-involved pedestrian and cyclist crash occurrence at urban intersections using five years of crash, geometric, operational, and socioeconomic data from a large set of urban intersections. Five supervised machine learning algorithms were trained and evaluated, including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest. The evaluated models demonstrated strong predictive performance overall, with accuracies approaching 91% and high discriminative capability. In particular, the Binary Logistic Regression and Random Forest models achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.961 and 0.964, respectively. To enhance transparency, SHAP values were used to quantify the contribution of predictors and examine feature effects at both the global and local levels. The results indicate that roadway hierarchy, intersection markings, and total entering volume are among the most influential determinants of crash likelihood, while socioeconomic variables exhibit weaker but interpretable effects.

This study examines bridge component deterioration patterns in Idaho, USA, using National Bridge Inventory (NBI) data collected from 1983 to 2023. A total of 5774 bridges were analyzed, representing 69 combinations of construction materials and structural system designs. The study focused on six common bridge types: prestressed concrete slab, prestressed concrete girder, concrete frame, concrete slab, concrete girder, and steel girder bridges. Kaplan–Meier survival analysis was applied to generate survival curves and assess deterioration over time. The results revealed that prestressed concrete girder, steel girder, and concrete girder bridges exhibited slower deterioration and retained higher condition ratings over time. In contrast, prestressed concrete slab and concrete slab bridges showed faster early deterioration, while concrete frame bridges experienced moderate deterioration patterns. A multiple regression analysis was also conducted to evaluate the influence of various factors on deck deterioration. Bridge age, freeze–thaw cycles, and snowfall days were found to be significant predictors of deterioration. In contrast, bridge length, span length, and average daily traffic (ADT) had minimal effects. The reduced model explained 89.4% of the variance in deterioration outcomes.

Cyclists must withstand significant wind force from passing vehicles. This study analyzed the magnitude of wind force generated by different vehicle types (sports utility vehicle, pickup truck, single unit truck, and semi-trailer truck), vehicle speeds (25, 40, and 60 mph), separation distances (2 ft, 4 ft, and 6 ft), and cyclist riding positions (racing and touring). Three research methods were used: field tests in controlled and natural environments, computational fluid dynamic simulations, and scaled-model wind tunnel experiments. The analysis focused on assessing longitudinal and transverse wind forces that are directed at the cyclist in parallel and perpendicular directions, respectively. All three methods showed the expected trend—that wind force increases with an increase in vehicle muzzle bluntness, vehicle speed, and cyclist proximity. Selected measurements and various models are provided. The wind force to knock a cyclist over (i.e., the flipping moment) was calculated for each test scenario (vehicle type, speed, and separation distance) and compared with the recommended limit (17 N) found in the literature. The semi-trailer truck generated flipping moments that exceeded the limit for all scenarios except 25 mph and 6 ft separation. All the vehicles, at all the speeds tested, exceeded the limit at 2 ft separation. This novel use of simulation and wind tunnel experiments provides groundwork for future research.

The adoption of cooperative intelligent transportation systems (cITSs) improves road safety and traffic efficiency. Vehicles connected to cITS form vehicular ad hoc networks (VANET) to exchange messages. Like other networks and systems, cITSs are targeted by attackers intent on compromising and disrupting system integrity and availability. They can repeatedly spoof false information causing bottlenecks, traffic jams and even road accidents. The existing security infrastructure assumes that the network topology and/or attack behavior is static. However, the cITS is inherently dynamic in nature. Moreover, attackers may have the ability and resources to change their behavior continuously. Assuming a static IDS security model for VANETs is not suitable and can lead to low detection accuracy and high false alarms. Therefore, this paper proposes an adaptive security solution based on deep learning and contextual references that can cope with the dynamic nature of the cITS topologies and increasingly common attack behaviors. In this study, deep belief networks (DBN) modeling was used to train the detection model. Binary cross entropy was used as a loss function to measure the prediction error. Two activation functions were used, Relu and Softmax, for input–output mapping. The Relu was used in the hidden layers, while the Sigmoid was used in the last layer to map the real vector to output between 0 and 1. The adaptation mechanism was incorporated into the detection model using a moving average that monitors predicted values within a time window. In this way, the model can readjust the classification thresholds on-the-fly as appropriate. The proposed model was evaluated using the Next Generation Simulation (NGSIM) dataset, which is commonly used in such related works. The result is improved accuracy, demonstrating that the adaptation mechanism used in this study was effective.

Charged-Coupled Device (CCD) Camera can be used as an optical position sensor to determine the location of the hot spot on the sensor surface in two dimensions. A CCD camera is a video camera with a coupled device (CCD), which is an integrated circuit transistor light sensor with a high quantum efficiency, minimal noise, large dynamic range, linear response, and little geometric distortion. The CCD camera is a digital camera that enables taking visual information and converting it into an image or video. CCD cameras are widely used due to their ability to take images in low light. The control system employed in this study is based on a novel and modern closed-loop algorithm that allows for precise tracking of the hotspot's location and speed used in the measurement of a specific physical process. The proposed control model is controlled around the brightest area using two controllers, the Proportional-Integral-Derivative controller (PID) and the Neuro-Fuzzy controller with feedback through the CCD sensor, as well as the use of a Brushless Direct Current (BLDC) motor for position and speed control with two feedback loops. To verify the validity of the proposed system, it was found that the algorithm will achieve a better rise time and reduce the maximum overshoot.