VTechWorks staff will be away for the winter holidays starting Tuesday, December 24, 2024, through Wednesday, January 1, 2025, and will not be replying to requests during this time. Thank you for your patience, and happy holidays!

Browsing by Author "Kang, Kyungwon"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Enhancing Freeway Merge Section Operations via Vehicle Connectivity
    Kang, Kyungwon (Virginia Tech, 2019-11-12)
    Driving behavior considerably affects the transportation system, especially lane-changing behavior occasionally cause conflicts between drivers and induce shock waves that propagate backward. A freeway merge section is one of locations observed a freeway bottleneck, generating freeway traffic congestion. The emerging technologies, such as autonomous vehicles (AVs) and vehicle connectivity, are expected to bring about improvement in mobility, safety, and environment. Hence the objective of this study is to enhance freeway merge section operations based on the advanced technologies. To achieve the objective, this study modeled the non-cooperative merging behavior, and then proposed the cooperative applications in consideration of a connected and automated vehicles (CAVs) environment. As a tactical process, decision-making for lane-changing behaviors is complicated as the closest following vehicle in the target lane also behaves concerning to the lane change (reaction to the lane-changing intention), i.e., there is apparent interaction between drivers. To model this decision-making properly, this study used the game theoretical approach which is the study of the ways in which interacting choices of players. The game models were developed to enhance the microscopic simulation model representing human driver's realistic lane-changing maneuvers. The stage game structure was designed and payoff functions corresponding to the action strategy sets were formulated using driver's critical decision variables. Furthermore, the repeated game concept which takes previous game results into account was introduced with the assumption that drivers want to maintain initial decision in competition if there is no significant change of situations. The validation results using empirical data provided that the developed stage game has a prediction accuracy of approximately 86%, and the superior performance of the repeated game was verified by an agent-based simulation model, especially in a competitive scenario. Specifically, it helps a simulation model to not fluctuate in decision-making. Based on the validated non-cooperative game model, in addition, this study proposed the cooperative maneuver planning avoiding the non-cooperative maneuvers with prediction of the other vehicle's desired action. If a competitive action is anticipated, in other words, a CAV changes its action to be cooperative without selfish driving. Simulation results showed that the proposed cooperative maneuver planning can improve traffic flow at a freeway merge section. Lastly, the optimal lane selection (OLS) algorithm was also proposed to assist lane selection in consideration of real-time downstream traffic data transferred via a long-range wireless communication. Simulation case study on I-66 highway proved that the proposed OLS can improve the system-wide freeway traffic flow and lane allocation. Overall, the present work addressed developing the game model for merging maneuvers in a traditional transportation system and suggesting use of efficient algorithms in a CAV environment. These findings will contribute to enhance performance of the microscopic simulator and prepare the new era of future transportation system.
  • Thumbnail Image
    A Repeated Game Freeway Lane Changing Model
    Kang, Kyungwon; Rakha, Hesham A. (MDPI, 2020-03-11)
    Lane changes are complex safety- and throughput-critical driver actions. Most lane-changing models deal with lane-changing maneuvers solely from the merging driver’s standpoint and thus ignore driver interaction. To overcome this shortcoming, we develop a game-theoretical decision-making model and validate the model using empirical merging maneuver data at a freeway on-ramp. Specifically, this paper advances our repeated game model by using updated payoff functions. Validation results using the Next Generation SIMulation (NGSIM) empirical data show that the developed game-theoretical model provides better prediction accuracy compared to previous work, giving correct predictions approximately 86% of the time. In addition, a sensitivity analysis demonstrates the rationality of the model and its sensitivity to variations in various factors. To provide evidence of the benefits of the repeated game approach, which takes into account previous decision-making results, a case study is conducted using an agent-based simulation model. The proposed repeated game model produces superior performance to a one-shot game model when simulating actual freeway merging behaviors. Finally, this lane change model, which captures the collective decision-making between human drivers, can be used to develop automated vehicle driving strategies.