Samer H. Hamdar

Bio: Samer H. Hamdar is an academic researcher from George Washington University. The author has contributed to research in topics: Poison control & Traffic flow. The author has an hindex of 17, co-authored 60 publications receiving 993 citations. Previous affiliations of Samer H. Hamdar include George Washington University Virginia Campus & Northwestern University.

Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach

Abstract: Vehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. A connected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior,as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information.This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment.A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against Next Generation Simulation (NGSIM) data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed. Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model.

Modeling Driver Behavior as Sequential Risk-Taking Task

Abstract: Acceleration models are at the core of operational driving behaviors and include car-following models that capture interactions between a lead and following vehicles. The main assumption in these models is that the behavior of the following vehicle (e.g., change in acceleration) is related directly to a stimulus observed or perceived by the driver, defined relative to the lead vehicle (e.g., difference in speeds or headways). An important aspect missing from previous formulations pertains to the stochastic character of the cognitive processes used by drivers, such as perception, judgment, and execution while driving. A car-following model that reflects the psychological and cognitive aspects of the phenomenon and captures risk-taking behavior under uncertainty is explored and evaluated. In this model, Tversky and Kahneman's prospect theory provides a theoretical and operational basis for weighing a driver's different alternatives. The model is implemented and tested to assess its properties and those of the resulting traffic stream behavior.

Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework

Abstract: The objective of this paper is to quantify and characterize driver behavior under different roadway geometries and weather conditions. In order to explore how a driver perceives the rapidly changing driving surrounding (i.e. different weather conditions and road geometry configurations) and executes acceleration maneuvers accordingly, this paper extends a Prospect Theory based acceleration modeling framework. A driving simulator is utilized to conduct 76 driving experiments. Foggy weather, icy and wet roadway surfaces, horizontal and vertical curves, and different lane and shoulder widths are simulated while having participants driving behind a yellow cab at speeds/headways of their choice. After studying the driving trends observed in the different driving experiments, the extended Prospect Theory based acceleration model is calibrated using the produced trajectory data. The extended Prospect Theory based model parameters are able to reflect a change in risk-perception and acceleration maneuvering when receiving different parameterized exogenous information. The results indicate that drivers invest more attention and effort to deal with the roadway challenges compared to the effort to deal with the weather conditions. Moreover, the calibrated model is used to simulate a highway segment and observe the produced fundamental diagram. The preliminary results suggest that the model is capable of capturing driver behavior under different roadway and weather conditions leading to changes in capacity and traffic disruptions.

From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

Abstract: The authors investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (ie gain in travel times) and subjective dis-utilities for decelerations (ie loss in travel time) with a perceived probability of being involved in rear-end collision crashes Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory – prospect theory – into an efficient microscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment After model formulation, the authors explore different model disaggregate and aggregate characteristics making sure that fidelity is kept in terms of equilibrium properties Significant effort is then dedicated to calibrating and validating the model using microscopic trajectory data A modified genetic algorithm is adopted for this purpose while focusing on capturing inter-driver heterogeneity for each of the parameters Using the calibration exercise as a starting point, simulation sensitivity analysis is performed to reproduce different fundamental diagram regions and to explore rear-end collisions related properties In terms of fundamental diagram regions, the model in hand is able to capture traffic breakdowns and different instabilities in the congested region represented by flow-density data points scattering In terms of incident related measures, the effect of heterogeneity in both psychological factors and execution/perception errors on the accidents number and their distribution is studied Through sensitivity analysis, correlations between the crash-penalty, the negative coefficient associated with losses in speed, the positive coefficient associated with gains in speed, the driver’s uncertainty, the anticipation time and the reaction time are retrieved The formulated model offers a better understanding of driving behavior, particularly under extreme/incident conditions

From Existing Accident-Free Car-Following Models to Colliding Vehicles: Exploration and Assessment

Abstract: The study explores the specifications of microscopic traffic models that could capture congestion dynamics and model accident-prone behaviors on a highway section in greater realism than existing models currently used in practice (commercial software). A comparative assessment of several major acceleration models is conducted, especially for congestion formation and incident modeling. On the basis of this assessment, alternative specifications for car-following and lane-changing models are developed and implemented in a microscopic simulation framework. The models are calibrated and compared for resulting vehicle trajectories and macroscopic flow-density relationships. Experiments are conducted with the models under different degrees of relaxation of the safety constraints typically applied in conjunction with simulation codes used in practice. The ability of the proposed specifications to capture traffic behavior in extreme situations is examined. The results suggest that these specifications offer an im...

Influence of connected and autonomous vehicles on traffic flow stability and throughput

Abstract: The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.

Recent developments and research needs in modeling lane changing

Abstract: This paper comprehensively reviews recent developments in modeling lane-changing behavior. The major lane changing models in the literature are categorized into two groups: models that aim to capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles. The methodologies and important features (including their limitations) of representative models in each category are outlined and discussed. Future research needs are determined.

Incorporating human-factors in car-following models: A review of recent developments and research needs

Abstract: Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process This paper provides a comprehensive review of these two research streams It is necessary to consider human-factors in CF modeling for a more realistic representation of CF behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis While there are some excellent reviews of CF models available in the literature, none of these specifically focuses on the human factors in these models This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area