Human-driven Vehicles Research Articles

Phase transitions in a heterogeneous lattice hydrodynamic model involving both communication distance and memory time duration differences

An in-depth analysis of vehicle heterogeneity characteristics in intelligent heterogeneous traffic flows, involving connected autonomous vehicles and human-driven vehicles, is essential to reveal the evolutionary mechanism of congestion and to understand its interaction among traffic factors. It has significant theoretical value in guiding the future field deployment and testing of large-scale connected autonomous vehicles through the systematic analysis of the complex dynamic characteristics of intelligent heterogeneous traffic flows from a traffic engineering perspective, as well as traffic management and control in intelligent heterogeneous traffic flow environments. In this study, the lattice hydrodynamics model is applied to construct a corresponding traffic flow model for the differences of communication capability and historical information memory capability for heterogeneous vehicles. The roles of these heterogeneous properties and their interactions are investigated. Through linear stability analysis and nonlinear analysis, we in this study explore the influence of key traffic factors involving the differences of communication capability and historical information memory capability contributing to ameliorating intelligent heterogeneous traffic dynamics, and numerical simulations are carried out to verify the accuracy and reliability of theoretical analysis. An interesting discovery is that longer memory duration gets better, but the optimizing impact of memory duration on the system being marginally decreasing.

Bifurcation and phase transitions in heterogeneous non-lane-discipline-based car-following model integrating cooperative feedback control under automated and human-driven vehicles environment

Automated vehicles (AVs) equipped with adaptive cruise control (ACC) possess different driving performances from human-driven vehicles (HDVs). AVs will have an unusual impact on the traffic flow, especially in non-lane-discipline-based (NLD) environment. Therefore, we create a heterogeneous NLD car-following model (HNLD-CFM) integrating cooperative feedback control under AVs and HDVs environment. The velocity differences between two preceding vehicles and the current vehicle are adopted for the cooperative feedback control signal. The stability condition of the HNLD-CFM is inferred through control theory, which is closely related to the proportion of AVs, lateral separation of vehicles and control signal. Moreover, bifurcation phenomena are been investigated via theory analysis and simulation. Furthermore, numerical simulation clarifies that traffic congestion can be effectively suppressed and energy consumption is reduced by increasing the proportion of AVs and lateral separation of vehicles, besides a control signal.

Modeling and analysis of heterogeneous traffic flow with mixed regular and connected human-driven vehicles considering driver stochasticity

Modeling and analysis of heterogeneous traffic flow with mixed regular and connected human-driven vehicles considering driver stochasticity

A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial

The connected and automated vehicle (CAV) is able to acquire the global intelligence in advance by communicating with other CAVs and roadside units (RSU), thus integrated speed control has the potential of easing the traffic wave, reducing the fuel consumption and emissions. In this paper, a dynamic temporal and spatial speed control framework is proposed to optimize the travel speed of CAVs along the signalized arterial under the mixed traffic flow including CAVs and human driven vehicles (HDVs). A speed control optimization method is proposed to minimize the number of stops of the ego CAV and its follower HDV with considering the signal status and queuing. A secondary speed control method based on the dynamic control areas is introduced in the mentioned framework to guide the CAV to the targeted positions. The corresponding dynamic variable parameter model is then designed to optimize the operational parameters of the corresponding control area to minimize the total fuel consumption of all vehicles under different market penetration rates. Finally, the simulation platform of Urban Mobility (SUMO) is used to test the proposed speed control strategy. The results indicate that the total stop delays are reduced by 60.9 % and saving 6.5 % total fuel consumption under the 30 % penetration rate.

Decentralized human-like control strategy of mixed-flow multi-vehicle interactions at uncontrolled intersections: A game-theoretic approach

A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers’ actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles

Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles

Assessing the impact of driver compliance on traffic flow and safety in work zones amid varied mixed autonomy scenarios

The safety of work zones is a critical issue for drivers, transportation agencies, and governing authorities. In particular, the vehicles that perform lane changes in the proximity of the work zones involving lane closure, pose a significant threat to the safety of the public and the work zone workers, as they need to complete a forced merging. Yet, there is no comprehensive simulation framework to examine the work zone traffic safety under different compliance distributions of the drivers to the warning delivery for the work zone in a mix-autonomy operation of autonomous and human-driven vehicles. To fill this void, we present an integrated microsimulation framework to assess the correlation between the number of vehicles that perform late merge at the taper (LMT) and traffic mobility and safety under different empirical compliance distributions of the drivers to the warning delivery for the downstream work zone.We employ different work zone configurations to illustrate the relationship between late merges at the taper and performance indicators for traffic mobility and safety of the work zone under a variety of work zone configurations. Simulation results show that compliance distribution significantly impacts the number of late merges at the taper (LMTs) and thereby traffic safety and efficiency. Our findings demonstrate that when human-driven vehicles exhibit high compliance behavior to the merging warning signs, it can offset the impact of the lower percentage of market penetration rate (MPR) levels for autonomous operation to achieve comparable traffic safety and efficiency. We further employ the conflation of microsimulation observations and data-driven models to design a regression model to predict LMTs as an indicator for traffic conditions using the work zone configuration as input variables. In particular, we address the heterogeneity induced by the compliance distribution of drivers by sampling the data points from the distribution to capture the diversity in compliance behaviors of the drivers. Our findings can provide insights for practitioners and researchers regarding the optimal compliance distribution using the performance measurements demonstrated in this work.

Analyzing Riders’ Behavioral Adaptation to Driving Patterns of Advanced Autonomous Vehicles: A Virtual Reality Simulation Study

The necessity of human supervision and intervention during autonomous driving has long been a topic of controversial discussion. From a developer’s perspective, it is expected that users will readily adapt to well-calibrated autonomous driving systems (ADS) due to their superior performance in dynamic driving tasks (DDT) compared to conventional human-driven vehicles. However, when passengers experience an autonomous vehicle (AV), there may be an adjustment period during which they modify their behavior to accommodate the driving patterns of the ADS. Additionally, some passengers might not adapt to autonomous driving at all, highlighting potential limitations in the current ADS development strategy. This work studies the dynamics of human-automation interaction and introduces an “objective method”, which employs a Virtual Reality (VR)-enabled simulation approach for in-depth behavioral analysis concerning riders’ behavioral adaptation to autonomous driving. Specifically, we examined how participants interacted with and intervened in Level 4 ADS operating under conservative, moderate, and aggressive driving patterns in a fully autonomous environment. A realistic urban road network was recreated in VR, integrated with traffic microsimulation to generate various driving scenarios. Twenty-seven participants completed driving tasks across different AV modes, with their intervention behaviors analyzed in relation to traffic conditions and AV aggressiveness. Key findings include: (1) Participants showed higher intention to intervene but lower actual intervention rates under aggressive AV modes compared to moderate and conservative modes, suggesting quicker adaptation to more challenging driving scenarios. (2) Interventions generally proved unnecessary and sometimes detrimental to overall traffic performance in a full-AV environment. (3) Aggressive AV modes significantly improved traffic efficiency, with a 40% increase in average travel speed and a 53% reduction in waiting time. However, human interventions posed the greatest challenge to achieving optimal traffic conditions. This research provides insights into the complex dynamics of human-AV interaction and adaptation, offering valuable implications for AV interface design, implementation strategies, and public acceptance of autonomous driving technologies.

Capturing impacts of travel preference on connected autonomous vehicle adoption of risk-averse travellers in multi-modal transportation networks

ABSTRACT This paper proposes a reliability-based combined modal split and traffic assignment model for the multi-modal transportation network with uncertainties in which the travellers' travel preferences, vehicle ownership, and the characteristics of connected autonomous vehicles (CAVs) are considered. Travellers are classified into captive travellers and choice travellers according to their preference for travel modes and vehicle ownership. The choice travellers need to make a tradeoff among the out-of-pocket cost, comfort, and travel time reliability and make decisions between continuing to commute by subway or purchasing a human-driven vehicle (HV) or CAV. The dogit-nested-logit model is adopted to characterise the effect of travel preference on the adoption of CAVs of the choice travellers, and the multinomial logit model is adopted to capture the effect of the differences between HV and CAV on information quality and value of travel time reliability on the path choice behaviour of travellers. The problem is formulated as an equivalent variational inequality model based on the properties of the equilibrium state, and its equivalency and solution existence are discussed. A path-based algorithm is employed for solving the problem, which combines the Monte Carlo simulation-based method and the column generation method. Numerical experiments are presented to illustrate the properties of the proposed models and validate the algorithm.

An internal stochastic car-following model: Stochasticity analysis of mixed traffic environment

This paper investigates the impact of adaptive cruise control(ACC) vehicles on the stochasticity of human driving behavior by constructing a stochastic car-following model of human-driven vehicles (HDVs). Utilizing NGSIM dataset, the relationship between acceleration variance and space headway is analyzed, and a novel stochastic car-following model with headway is proposed to capture the internal stochasticity of drivers. Furthermore, the interaction between HDVs and AVs is explored by discussing stochasticity and stability in mixed traffic flow, using the proposed HDV model. The model parameters are calibrated based on NGSIM dataset and the simulation results indicate that the proposed stochastic car-following model can effectively reproduce the generation and propagation of traffic shocks without lane changes. Additionally, the simulations reveal that as the penetration rate of AVs increases in a lower range (0%–50%), the stochasticity of HDVs and stability in mixed traffic flow is substantially reduced. However, at higher penetration rates, increases in the AV penetration rate have a limited effect on the stochasticity of human driving behavior and the stability of mixed traffic flow. Concurrently, under conditions of low penetration rates, a smaller AV platoon size contributes more effectively to enhancing the stability of traffic flow and suppressing the stochastic behavior of HDVs. This research provides new insights for optimizing traffic flow control with automated vehicles.

Modelling connected and autonomous bus on dynamics of mixed traffic in partially connected and automated traffic environment

Since connected and autonomous buses (CABs) have great potential of being operated in partially connected environment and raise great impact on mixed traffic flow, this study develops an analytical model for modeling system dynamics of mixing connected and autonomous vehicles (CAV), human-driven vehicles (HV), and CABs in both cases of dedicated bus lane and non-dedicated bus lane, in which the impact of CABs is specifically quantified under varied levels of connected vehicle penetration. A Vissim-Matlab simulation evaluation is also developed for validating the model’s effectiveness. The results demonstrate that the proposed model performs well with an accuracy of over 85%, and it can be up to 95% in case of non-dedicated bus lane when the inflow rate is higher than 2000veh/h. Besides, the impacts of CABs’ volume, bus dwelling time, and bus stop location on the performance of mixed traffic flow are verified in two CAB operating environments. The obtained results can be used as the basis for planning and operating CABs in partially connected environment.

A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios

Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.

Efficiency and fuel consumption of mixed traffic flow with lane management of CAVs

The mixed traffic flow of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will exist on highways for a long time, as the deployment of CAVs is gradual. To reduce the negative impact of HDVs on CAVs, the deployment of dedicated lanes has been considered an effective solution. Along with the dedicated lanes, three different lane management strategies will be formed, which are (C, H) strategy (CAVs dedicated lanes and HDVs dedicated lanes), (C, G) strategy (CAVs dedicated lanes and general lanes), and (G, H) strategy (general lanes and HDVs dedicated lanes). To evaluate the influence of dedicated lane settings on mixed traffic flow comprehensively, this paper proposes a framework for evaluating road segment efficiency and fuel consumption by considering lane management strategies. First, the possible traffic flow equilibrium states under three lane management strategies are discussed, and the characteristics of five car-following modes in mixed traffic flow are analyzed. Then, a mixed traffic flow capacity model considering platoon size is introduced to the traditional BPR function to establish a speed estimation model for mixed traffic flow and a fuel consumption estimation model for mixed traffic flow. Next, the traffic flow distribution model at the lane level in a steady state is derived for different lane management strategies. Based on the traffic flow distribution model, the speed estimation model and the fuel consumption estimation model for mixed traffic flow, which consider lane management strategies, are proposed. Finally, a numerical simulation is conducted to analyze the effects of different lane management strategies and configuration schemes on road segment efficiency and fuel consumption. The results of numerical experiments show that (1) at the same traffic demand, the operational speeds of vehicles under the (C, H) strategy and (G, H) strategy tend to increase and then decrease with the increase in the penetration rate of CAVs. While the speed of the vehicle under the (C, G) strategy increases with the increase in the penetration rate of CAVs. (2) Compared with the baseline strategy, all three management strategies can improve the operating efficiency of vehicles under certain traffic conditions. (3) At the same traffic demand, the average fuel consumption under the three strategies tends to decrease first and then increase slightly as the penetration rate increases. Increasing the number of dedicated lanes under specific traffic conditions can significantly increase the fuel consumption reduction rate under each strategy. At the same penetration rate, this advantage diminishes with the increase in traffic demand. (4) The increase in platoon size favors the efficiency of vehicle operations under different strategies. However, as platoon size increases, the marginal benefit of increasing platoon size becomes smaller and smaller. In addition, the average fuel consumption of vehicles has a low sensitivity to platoon size, and increasing platoon size may not always reduce fuel consumption.

A dynamic priority bus lane strategy in heterogeneous traffic environments

Allowing connected and automated vehicles (CAVs) to drive in bus lanes can enhance the utilization efficiency of exclusive bus lanes. However, this could also lead to a certain degree of heightened bus operating delays. In a connected and automated environment, vehicle-to-vehicle (V2V) communication is essential for implementing dynamic management strategies for bus lanes. This study focuses on urban arterial roads as the research scenario. Based on the characteristics of the car-following and lane-changing behaviors of human-driven vehicles (HDVs) and CAVs, a simulation platform for heterogeneous traffic flow is established. Four strategies for bus lane control are proposed: exclusive bus lanes (EBLs), bus and CAV mixed lanes (BCMLs), dynamic bus lanes (DBLs), and dynamic priority bus lanes (DPBLs). Through simulation experiments, an analysis is conducted to compare traffic operation characteristics under different bus lane strategies, assess the advantages of these strategies, and determine the traffic density conditions for their implementation. The results indicate that in comparison to the EBL strategy, the other strategies enhance traffic flows but result in different levels of travel delays for buses. Compared to the DBL and BCML strategies, the DPBL strategy is applicable under a broader range of traffic density conditions. Additionally, sensitivity analysis revealed that the effects of the V2V communication distance and bus flows on the applicability of DPBL strategies are relatively minor. These results provide a theoretical basis and practical guidance for future bus lane management.

Mean-square exponential stabilization of mixed-autonomy traffic PDE system

Control of mixed-autonomy traffic where Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) coexist on the road has gained increasing attention over the recent decades. This paper addresses the boundary stabilization problem for mixed traffic on freeways. The traffic dynamics are described by uncertain coupled hyperbolic partial differential equations (PDEs) with Markov jumping parameters, which aim to address the distinctive driving strategies between AVs and HVs. Considering that the spacing policies of AVs vary in mixed traffic, the stochastic impact area of AVs is governed by a continuous Markov chain. The interactions between HVs and AVs such as overtaking or lane changing are mainly induced by impact areas. Using backstepping design, we develop a full-state feedback boundary control law to stabilize the deterministic system (nominal system). Applying Lyapunov analysis, we demonstrate that the nominal backstepping control law is able to stabilize the traffic system with Markov jumping parameters, provided the nominal parameters are sufficiently close to the stochastic ones on average. The mean-square exponential stability conditions are derived, and the results are validated by numerical simulations.

Evaluation of Conventional Surrogate Indicators of Safety for Connected and Automated Vehicles in Car Following at Signalized Intersections

The driving behaviors of connected and automated vehicles (CAVs) will differ from those of human-driven vehicles (HDVs) because the CAVs’ driving decisions are controlled by computers. Because of the limited amount of crash data for CAVs, researchers have relied on surrogate measures of safety to assess their safety impacts. However, they often use the same safety indicators for CAVs that were used for HDVs, raising questions about the adequacy of the safety indicators for CAVs. This study aims to investigate the suitability of using conventional safety indicators for CAVs. To achieve this, we evaluated eight safety indicators used for CAVs in the literature: time-to-collision (TTC), post-encroachment time (PET), time-exposed TTC, time-integrated TTC, deceleration rate to avoid a crash (DRAC), crash-potential index, rear-end-collision risk index, and potential index for collision with urgent deceleration (PICUD). For the evaluation, we first simulate CAVs on an approaching lane of signalized intersections using the acceleration-control algorithm. The algorithm replaces the HDV trajectories with CAVs for mixed simulations where HDVs and CAVs coexist. Analyzing the simulation output, we examined the safety indicators for the various car-following scenarios and the CAV proportions. The findings suggest that PET and PICUD can yield different safety implications for CAVs because of their small-gap car-following characteristics. Ignoring such characteristics may lead to interpreting the small-gap car-following situations as simply dangerous traffic interactions for CAVs. The car-following experiments indicate that TTC, PET, and DRAC are insufficient in measuring the safety implications when successive vehicles operate at similar speeds for CAVs.

A recursive framework of vehicle trajectory planning at mixed‐traffic signalized intersections

AbstractThis study aims to introduce a new strategy for anticipating the behaviour of human‐driven vehicles (HDVs) and designing trajectories for connected and automated vehicles (CAVs) at signalized intersections under mixed traffic scenarios. To tackle the challenge of unreliable HDV trajectory predictions stemming from driving unpredictability, a recursive framework is developed. This framework integrates real‐time tracking data from both traffic detectors and CAVs, continuously updating HDV predictions. The proposed approach employs the updated predictions to formulate optimal control problems recursively to optimize or adjust CAV trajectories, enhancing travel and energy efficiency. Besides, the recomputing of CAV trajectories will only be conducted when the variation in predictions rises to a certain threshold, balancing efficiency and computing consumption, inspired and modified based on MPC methods. The application of the Pontryagin maximum principle aids in finding solutions efficiently by transforming necessary conditions into a system of equations and consolidating elementary unconstrained and constrained arcs. Numerical simulations were carried out to evaluate the performance of the proposed recursive framework, revealing its superiority over the one‐time approach, particularly in isolated intersections with high traffic demands. Additionally, the recursive framework exhibited more robust and effective enhancements throughout the road network.

Automated or human: Which driver wins the race for the passengers’ trust? Examining passenger trust in human-driven and automated vehicles following a dangerous situation

Automated vehicles (AVs) provide numerous advantages over manually operated vehicles, but the extent of these benefits depends on whether we engage with AVs safely and efficiently. To achieve such interactions with AVs, an appropriate – or calibrated – level of trust in AVs especially during critical scenarios, is fundamental. The trust level also impacts individuals' decisions regarding the utilisation of AV technology. This study investigates trust calibration and factors that influence how trust develops in AVs compared to human drivers. Two groups of participants underwent a driving simulation, experiencing either a ride in a human-driven taxi or an AV, during which a dangerous situation occurred. Before, during and after the simulation, the passengers’ trust was measured.Pre-simulation trust was higher in the human driver than in the AV, but this difference disappeared after the simulation. Noticeably, during the simulation trust did not differ between the groups. Instead, the critical situation significantly influenced trust: following the dangerous incident, trust levels in both conditions dropped but recovered until the simulation ended. Additionally, self-esteem, which has been associated with trust in the past, was investigated. However, no significant relationship between self-esteem and trust was found in this study. Overall, the findings indicate that the dangerous situation prompted heightened caution among participants. A process of trust calibration was initiated in which the participants’ trust was highly susceptible to the driving style of the driver/AV. Moreover, the comparable evolution of trust in the human-driven vehicle and the AV, sheds light on the dynamics underlying attitudes towards AVs.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.