Browsing by Author "Fadhloun, Karim"

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    Modeling Human And Machine-In-The-Loop In Car-Following Theory
    Fadhloun, Karim (Virginia Tech, 2019-10-29)
    Most phenomena in engineering fields involve physical variables that can potentially be predicted using simple or complex mathematical models. However, traffic engineers and researchers are faced with a complex challenge since they have to deal with the human element. For instance, it can be stated that the biggest challenge facing researchers in the area of car-following theory relates to accounting for the human-in-the-loop while modeling the longitudinal motion of the vehicles. In fact, a major drawback of existing car-following models is that the human-in-the-loop is not modeled explicitly. This is specifically important since the output from car-following models directly impacts several other factors and measures of effectiveness, such as vehicle emissions and fuel consumption levels. The main contribution of this research relates to modeling and incorporating, in an explicit and independent manner, the human-in-the-loop component in car-following theory in such a way that it can be either activated or deactivated depending on if a human driver is in control of the vehicle. That would ensure that a car-following model is able to reflect the different control and autonomy levels that a vehicle could be operated under. Besides that, this thesis offers a better understanding of how humans behave and differ from each other. In fact, through the implementation of explicit parameters representing the human-in-the-loop element, the heterogeneity of human behavior, in terms of driving patterns and styles, is captured. To achieve its contributions, the study starts by modifying the maximum acceleration vehicle-dynamics model by explicitly incorporating parameters that aim to model driver behavior in its expression making it suitable for the representation of typical acceleration behavior. The modified variant of the model is demonstrated to have a flexible shape that allows it to model different types of variations that drivers can generate, and to be superior to other similar models in that it predicts more accurate acceleration levels in all domains. The resulting model is then integrated in the Rakha-Pasumarthy-Adjerid car-following model, which uses a steady-state formulation along with acceleration and collision avoidance constraints to model the longitudinal motion of vehicles. The validation of the model using a naturalistic dataset found that the modified formulation successfully integrated the human behavior component in the model and that the new formulation decreases the modeling error. Thereafter, this dissertation proposes a new car-following model, which we term the Fadhloun-Rakha model. Even though structurally different, the developed model incorporates the key components of the Rakha-Pasumarthy-Adjerid model in that it uses the same steady state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. Besides offering a better fit to empirical data, the Fadhloun-Rakha model is inclusive of the following characteristics: (1) it models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profile; and (5) it explicitly captures driver perception and control inaccuracies and errors. Through a quantitative and qualitative evaluation using naturalistic data, the new model is demonstrated to outperform other state-of-the-practice car-following models. In fact, the model is proved to result in a significant decrease in the modeling error, and to generate trajectories that are highly consistent with the observed car-following behavior. The final part of this study investigates a case in which the driver is excluded and the vehicles are operating in a connected environment. This section aims to showcase a scenario in which the human-in-the-loop is deactivated through the development of a platooning strategy that governs the motion of connected cooperative multi-vehicle platoons.
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    NeTrainSim: A Network Freight Train Simulator for Estimating Energy/Fuel Consumption
    Aredah, Ahmed; Fadhloun, Karim; Rakha, Hesham A.; List, George (2023-01-10)
    Although train simulation research is vast, most available network simulators do not track the instantaneous movements and interactions of multiple trains for the computation of energy/fuel consumption. In this paper, we introduce the NeTrainSim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. Trains are modeled as a series of moving mass points (each car/locomotive is modeled as a point mass) while ensuring safe following distances between them. The simulator considers the motion of the train as a whole and neglects the relative movements between the train cars/locomotives. Furthermore, the powers of the different locomotives are transferred to the first locomotive as such a simplification result in a reduced simulation time without impacting the accuracy of energy consumption estimates. While the different tractive forces are combined, the resistive forces are calculated at their corresponding locations. The output files of the simulator contain pertaining information to the train trajectories and the instantaneous energy consumption levels. A summary file is also provided with the total energy consumed for the full trip and the entire network of trains. Two case studies are conducted to demonstrate the performance of the simulator. The first case study validates the model by comparing the output of NeTrainSim to empirical trajectory data using a basic single-train network. The results confirm that the simulated trajectory is precise enough to estimate the electric energy consumption of the train. The second case study demonstrates the train-following model considering six trains following each other. The results showcase the model’s ability in relation to maintaining safe-following distances between successive trains. Finally, the NeTrainSim is demonstrated to be scalable with computational times of O(n) for less than 50 trains (n) and O(n2) for higher number of trains.
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    NeTrainSim: a network-level simulator for modeling freight train longitudinal motion and energy consumption
    Aredah, Ahmed S.; Fadhloun, Karim; Rakha, Hesham A. (2024-04-09)
    Although train modeling research is vast, most available simulation tools are confined to city- or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the NeTrainSim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator’s performance. The first case study validates the model by comparing NeTrainSim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safe-following distances between successive trains. The next study highlights the simulator’s ability to resolve train conflicts for different scenarios. Finally, the suitability of the NeTrainSim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.
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    Towards a Comprehensive Bicycle Motion Behavior Model and Naturalistic Cycling Dataset
    Alazemi, Fahd (Virginia Tech, 2022-05-25)
    Most of the existing bicycle flow traffic research is limited to characterizing the longitudinal motion of bicyclists based on the assumption that there is no significant differences between the dynamics of a single-file bicycle traffic and the longitudinal motion behavior of cars. This research reparametrizes an existing car-following model to describe bicycle-following and motion behavior. Furthermore, the lack of naturalistic data has limited the validation of this model. This research aims at developing a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycle traffic and provides a methodology for extracting naturalistic cycling data from video feeds for use in safety and mobility applications. In this study, The Fadhloun-Rakha (FR) bicycle-following longitudinal motion model was extended through complementing it with a lateral motion strategy; thus allowing for overtaking maneuvers and lateral bicycle movements. For the most part, the following strategy of the FR model remains valid for modeling the longitudinal motion of bicycles except for the activation conditions of the collision avoidance strategy which are modified in order to allow for overtaking when possible. The proposed methodology is innovative in that it makes use of the intersection of certain pre-defined regions around the bicycles to decide on the feasibility of angular motion along with its direction and magnitude. The resulting model is the first point-mass dynamics-based model for the description of the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions, and it is the only existing model that is sensitive to the bicyclist physical characteristics and the bicycle and roadway surface conditions given that the used longitudinal logic was previously validated against experimental cycling data. In relation to the development of the naturalistic cycling dataset, the used videos come from a dataset collected in a previous Virginia Tech Transportation Institute study in collaboration with SPIN in which continuous video data at a non-signalized intersection on the Virginia Tech campus was recorded. The research applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles with a high precision level. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined.