Vehicle Delay Research Articles

Explicit coordinated signal control using soft actor–critic for cycle length determination

AbstractExplicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor–critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor–critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination.

Intelligent emergency traffic signal control system with pedestrian access

With the integration of artificial intelligence and traffic systems, intelligent traffic systems are utilizing enhanced perception coverage and computational capabilities to provide data-intensive solutions, achieving higher levels of performance than traditional systems. This paper combines the D3QN algorithm from deep reinforcement learning with practical issues and proposes an intelligent emergency traffic signal control system based on Deep Reinforcement Learning (DRL). The system takes into account pedestrian movement and utilizes real-time traffic data and environmental information to model traffic flow and road conditions within a novel state space. It employs the Dueling Double Deep Q-Network (D3QN) to optimize signal control strategies. The system dynamically adjusts signal timings to enhance operational efficiency at intersections. By using the Weibull distribution to simulate realistic traffic congestion and actual traffic data from Shanyin Road in Hangzhou for validation, the results demonstrate that this method converges faster and is more stable compared to other methods, significantly reducing traffic congestion. Furthermore, by incorporating pedestrian movement, this method reduces pedestrian waiting times by 44.736% during peak periods and 22.95% during off-peak periods, while maintaining comparable vehicle queue lengths, delay times, and carbon dioxide emissions. This approach shows the potential improvement of smart urban mobility and resolving intersection congestion challenges.

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

Optimal Traffic Control for a Tandem Intersection With Improved Lane Assignments at Presignals

This article focuses on the optimal traffic control strategy for a tandem intersection with pre­signals. First, the impact of a lane assignment scheme at presignals on the operational performance of the tandem intersection is analyzed. An improved lane assignment method is thus proposed to avoid setting the lanes with the same traffic flow direction adjacent to each other as far as possible. Second, vehicle delay models behind the presignals and in the sorting area are established, respectively. Third, considering green times of presignal phases and sorting area lengths on all approaches as variables, an optimization model is proposed to minimize the average vehicle delay. Finally, a case study is conducted on a congested intersection in Changchun, China, to validate the proposed method. The effects of the proposed improved lane assignment scheme on the tandem intersection are analyzed compared with the traditional lane assignment scheme. The results show that the former can decrease average vehicle delay by 22.9% and increase intersection capacity by 18.6%.

Real-world black carbon emissions of gasoline vehicles at urban intersections

To gain insight into the real-world black carbon (BC) emission patterns at urban intersections, we employed a portable emission measurement system (PEMS) to assess the BC emissions from three light-duty gasoline vehicles along a designated urban route. The BC emission factors were evaluated, and the contributions of vehicle operating conditions at the intersections to the total BC emissions were quantified. The results show that the BC emission factors in an intersection area range from 0.045 to 0.291 mg/km, higher than those at an intersection's connecting sections. The average BC emission factor at signalized intersections is 17.07% higher than at roundabouts. Emission contribution analysis of driving conditions showed that in a state of non-free traffic flow, acceleration and deceleration conditions are responsible for 39% ∼ 56% and 24% ∼ 33% of BC emissions at intersections, respectively. We also found that vehicle specific power (VSP) and delay time are almost equally crucial for BC emissions from vehicles at intersections in the multiple linear regression. Our research reveals the significant impact of vehicle delay time and acceleration conditions on black carbon emissions at urban intersections, further emphasizing the importance of smooth (congestion-free) intersection operation and steady driving in reducing urban black carbon emissions.

Traffic Signal Timing Scheme Based on the Improved Harris Hawks Optimisation

With the continuous increase of urban vehicles, traffic congestion becomes severe in the metropolitan areas and higher car utilisation areas. The traffic signal timing scheme can effectively alleviate traffic congestion at intersections. We need to make a profound study in the traffic signal timing. An optimisation model is established, which not only takes the average delay time of vehicles, the number of vehicle stops and the traffic capacity, but also takes the exhaust emissions as the evaluation indexes. The model is too complex and involves too many variables to be solved by using multi-objective programming. Thus, the Harris Hawks Optimisation (HHO) with few parameters and high search accuracy was used to solve the model. To avoid the disadvantages of poor search performance and easy to fall into local optimisation of the Harris Hawks Algorithm, multi-strategy improvements were introduced. The experimental effects show that during the peak hours of traffic flow, the improved algorithm can reduce the average vehicle delay by 36.7%, the exhaust emission by 31.2% and increase the vehicle capacity by 41.6%. The above indicators have also been upgraded during the low peak stage.

Intersection control based on Fine-grained Phases for connected and automated vehicles

Traditional traffic signal control is one of the most widely used urban traffic control methods, due to its high efficiency, robustness, and simplicity. However, in the era of connected and automated vehicles (CAVs), it is prospective to enhance traffic control precision and achieve vehicle coordination in a more efficient manner. Focusing on an isolated intersection, this study proposes an innovative concept of Fine-grained Phase (FP) for CAV-enabled traffic control, which realizes more refined right-of-way allocation than traditional signal phases while preserving the simplicity and scalability. The development of FP jointly optimizes traffic control strategy and intersection layout design to fully utilize time-space resources, and multiple forms of FP could be generated in response to various traffic demands; meanwhile, vehicle coordination is achieved in an offline–online manner to balance solution quality and efficiency. Under the FP-based control, each lane is assigned with periodical entry time points using FP design results, then vehicles are supposed to catch up with the appropriate time points to fulfill their trips. Theoretical analyses are conducted under stationary vehicle arrival patterns, where the average delay and the intersection capacity associated with different forms of FP can be analytically acquired. For the implementation of FP-based control, we construct a general offline–online framework, consisting of an offline model that optimizes the geometry of the conflict zone at intersections and the control strategy of vehicles simultaneously, which is formulated as a mixed-integer linear programming problem, and an online FP adoption mechanism that can adapt to varying demand patterns. Under the framework, limited online computation is required, facilitating its practical real-time implementation. Simulation studies under a variety of demand settings show that FP-based control is advantageous over benchmark methods in reducing vehicle delay and also improving intersection capacity in most cases; even for cases with extremely imbalanced demands, FP-based control can admit comparable throughput, while the average delay is much smaller. In addition, results of computation time reveal that FP-based control could realize real-time implementation with negligible computation burden.

Enhancing Traffic Sustainability: An Analysis of Isolation Intersection Effectiveness through Fixed Time and Logic Control Design Using VisVAP Algorithm

This paper addresses the limitations of the fixed-time approach in traffic signal control, which can lead to bottlenecks and inefficiencies. Proposing an alternative algorithm based on design logic control, the study integrates data from inductive detectors and non-linear traffic flow rates to optimize signaling plans. Analytical models are developed for both fixed and semi-actuated traffic signal control approaches, with PTV Vissim software (version 8, 64 bit) used for simulation. The design logic control dynamically adjusts signaling plans, determining the duration of the green interval for the secondary road based on arrival traffic flow. In the absence of traffic, it eliminates the green interval, advancing to the next phase, thereby reducing cycle time. This dynamic adjustment follows a conditional “if-then” statement, optimizing traffic signal operation. The design logic control algorithm was tested in a real isolation intersection with four scenarios, using non-linear traffic flow rate data for one peak hour. Results demonstrated that the proposed design logic control, based on the Semi-Actuated Traffic Signal Control (SATSC) approach, outperformed the commonly used Fixed-Time Signal Control (FTSC) with overall reduction of queue lengths by 39.6% and reduction of vehicle delays by 51.3%. The findings suggest its viability as a solution for many cities, contributing to a more sustainable traffic system.

Vehicle Delay Model for Signalized Midblock Crosswalk Under Mixed Traffic Conditions

For signalized locations, delay is one of the key parameters in assessing the level of service and redesigning the traffic signals. Around the world, Highway Capacity Manual (HCM) and Webster delay models are commonly used in calculating the vehicle delay at signalized intersections, notwithstanding that these models assume homogeneous traffic conditions and lane discipline. Furthermore, it is worth noting that previous studies have mostly focused on developing delay models for signalized intersections, whereas signalized midblock crosswalks were overlooked by past researchers. In view of this, the current study presents a modified vehicle delay model for signalized midblock crosswalks (SMCs) under mixed traffic conditions. Mixed traffic conditions refers to diverse vehicles, complex interactions, and no lane discipline on the road—conditions which are usually noticed in developing nations. The HCM vehicle delay model is modified in the current study by incorporating factors related to mixed traffic conditions and SMCs. To test the accuracy of the proposed model, it was validated with the field delay using data collected at other SMCs in Hyderabad, India. Field delay was calculated by plotting the measured queue length against the signal cycle length. The performance metrics of root mean square error (RMSE), mean absolute percentage error, Nash–Sutcliffe efficiency, RMSE-observations standard deviation ratio, and percent bias were used to test the accuracy of the proposed model. Results revealed that the proposed model yielded the lowest error rates and the highest correlation coefficients between modeled and actual vehicle delay. The maximum error between the field delay and the modeled delay was found to be less than 5%.

Multi-objective arterial coordination control method based on induction control and vehicle speed guidance

The fixed green wave speed and staged statistical flow used in arterial signal coordination are not adaptable to the fluctuations in vehicle travel speed and traffic flow on roads, resulting in a mismatch between the signal scheme and the optimal green wave speed and traffic flow demand. This discrepancy negatively impacts the efficiency of intersection traffic. In traditional signal control systems, the cycle and green light timing are typically set independently. However, such a setting method poses problems in practical operation. In this paper, we combine vehicle arrival and vehicle location information, and consider the interaction of speed guidance and dynamic signal optimization to construct a model. This study is developed along the following steps: in the vehicle-road coordination environment, based on the MAXBAND model, a global coordination scheme is obtained, incorporating the speed guidance method; then, based on the vehicle saturation of the inlet lane of the arterial intersection, a multi-objective optimization model for arterial signal coordination under vehicle speed guidance is established based on global coordination with the maximum green wave bandwidth and the minimum delay of arterial vehicles, the minimum number of arterial stops and the minimum delay in the minor direction road as the optimization objectives. Based on global coordination, adopting an integrated control mechanism of cycle and green light timing allows for dynamic adjustments according to real-time traffic conditions. The improved multi-objective particle swarm algorithm is chosen to solve the model, and the simulation environment is built based on the COM interface of VISSIM software and C# platform. Three adjacent intersections of Ganjiang Middle Road in Nanchang are selected as case studies, and the methods in this paper are compared with the current timing scheme, the MAXBAND method and the optimization scheme under speed guidance only, respectively. The results show that the model proposed in this paper achieves significant optimization effects on the indicators of arterial delay, arterial stopping times and the delay of minor roads.

A Study of Efficiency and Economic Value of Grade Separated Construction Project: Case Study of Grade Separated Without Ramp across The Sri Ayutthaya Intersection Bangkok

This research was conducted to analyze the efficiency and economic value of grade separated construction projects in a large city due to heavy traffic volumes. This is consistent with the current situation where traffic volumes are continually increasing. In order to reduce fuel consumption, accidents and travel time. In this research, the efficiency and economic value of grade separated without ramp across Sri Ayutthaya intersection will be studied, Bangkok (Thailand), is considered in the form of an underpass through the intersection by using the VISSIM program to create and develop micro simulation traffic models. Then, the economic cost-benefit analysis of the project is evaluated by considering the economic worthiness of the project from a comparison between the case with the project and without the project. The economic worthiness of the project is analyzed by using Net Present Value (NPV), Benefit-Cost Ratio (B/C ratio) and Internal Rate of Return (IRR). From the results of this study, it was found that after developing the current area, which is a ground level intersection into an underpass. Traffic at intersections will be more efficient. And helps reduce traffic congestion at intersections. This is because the average delay of each type of vehicle decreased after the project. Performance measures at intersections can be summarized as the average vehicle delay in the project start year (2027) for motorcycles decreased from 364.48 seconds/vehicle to 85.93 seconds/vehicle, passenger cars decreased from 369.35 seconds/vehicle to 78.42 seconds/vehicle, trucks decreased from 282.19 seconds/vehicle to 78.96 seconds/vehicle, and buses decreased from 420.47 seconds/vehicle to 65.07 seconds/vehicle. And it can be concluded that the project of grade separated construction in the form of an underpass across Sri Ayutthaya Intersection. It is economically worthwhile and suitable for investment. Because the net present value (NPV) in the 15th year of the project (2041) is equal to 1,752.18 million baht, which is greater than 0, the benefits-costs ratio (B/C Ratio) in the 15th year of the project (2041) is equal to 3.61, which is greater than 1, and the internal rate of return (IRR) at the 15th year of the project (2041) is equal to 42%, which is greater than 12%.

Real-time schedule adjustments for conflict-free vehicle routing

Conflict-Free Vehicle Routing Problems (CFVRPs) arise in manufacturing, transportation and logistics applications where Automated Guided Vehicles (AGVs) are utilized to move pallets and containers. A peculiar feature of these problems is that collision avoidance among vehicles must be considered explicitly. To make things more complex, the uncertainty affecting both travel times and machine ready times often results in vehicle delays or anticipations that require real-time modifications to the fleet nominal plan. In this paper, the determination of such modifications (schedule adjustment problem in CFVRPs) is modeled as a sequential decision problem for which we develop a tailored fast exact algorithm suitable for any objective function that is non-decreasing in the arrival times. Computational results show that optimal solutions can be found within at most 3.3 milliseconds for instances with up to 300 vehicles with improvements of various performance measures up to 74% compared to state-of-the-art solution algorithms.

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes.

To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

Optimization of isolated intersection signal timing and trajectory planning under mixed traffic environment: The flexible catalysis of connected and automated vehicles

With the development of connected and automated vehicles (CAVs) enables real-time interaction between intersection signals and vehicle trajectories. The full use of this technological breakthrough will help traffic managers improve the performance of intersections. Most previous research focuses on the 100% CAV environment and the single lateral or longitudinal optimization of CAVs. Based on the flexible characteristics of CAVs, this paper proposes that CAV is regarded as the catalyst of vehicle platoon in mixed traffic environment and considers the uncertainty of CAVs and CHVs’ interaction in the real-world, which can promote the generation of controllable platoon through the “catalytic” mode of cooperative, accelerated, or direct lane-changing, and cooperative or direct overtaking. Simultaneously, a two-stage optimization model of mixed traffic trajectory and signal timing is proposed. Stage I: Based on the predicted vehicle platoon information, a dynamic NEMA signal timing scheme without duplicate structures is generated to minimize vehicle delays. Stage II: Based on the timing scheme, the generation of controllable and stable platoon and vehicle trajectory optimization model are established to minimize vehicle emissions. Dynamic programming with NEMA signal groups as sub-states is designed to solve the proposed model. The performance of the proposed model under different scenarios is investigated through numerical experiments and compared with benchmark models. Results show that the proposed model will outperform the benchmark models regarding average vehicle delay and emission under more realistic traffic demands. The average vehicle delay can be reduced by 54.32% and 7.33%, and the average vehicle emissions can be reduced by 19.1% and 0.8%, respectively. Meanwhile, the sensitivity analysis of CAV market penetration shows that the proposed model can perform satisfactorily at 20% CAV market penetration. Notedly, with increased market penetration, the proposed model will obtain better performance.

The dynamics of traffic congestion: Data from a freeway Electronic Toll Collection system

Electronic Toll Collection systems installed at regular intervals along freeways provide a tremendous amount of individualized vehicle data useful for traffic analyses and predictions. We demonstrate how real data from the Taiwan freeway system enables us to monitor the evolution of traffic congestions on the fundamental diagram, and find that congestion is characterized by a loopy trajectory such that the area enclosed by the loop increases with the severity of congestion. There is a power law relating the area of the loop to the drop of influx during the congestion period. Data extracted from the time delays of individual vehicles show that the area enclosed by the loop in the fundamental diagram is a measure of the economic loss due to congestion. The use of the loss area to understand various highway characteristics is also explored.

Methodology for Monitoring Border Crossing Delays with Connected Vehicle Data: United States and Mexico Land Crossings Case Study

International trade is a critical part of the United States economy. Land border crossings between the United States and Mexico accounts for a large proportion of the USD 779 billion in trade between these two countries. Monitoring and managing the operations of these land border crossings is critical for ensuring efficient trade and providing appropriate security. This paper examines the opportunity to use connected vehicle data to monitor the travel time delay of passenger vehicles crossing the border for system level assessment across 26 border crossing locations over an analysis period of 25 days in August 2020. A sample size of 51,341 trips from the US to Mexico and 41,708 trips from Mexico to the US were used in this study. Furthermore, 97% trips to the US and 76% trips to Mexico experienced delays. The average delay was 34 min for trips to the US compared to only 2 min for trips to Mexico. In terms of the predictability of border crossing times, there was also substantial variation by direction. The interquartile range of vehicle delay from the US to Mexico was 2 min, while the interquartile range of delay for vehicles travelling from Mexico to the US was 46 min. Border crossings were also ranked using four performance metrics—trip counts, median delay, delayed trip counts and total delays in vehicle hours. Methods for summarizing delay trends by time of the day and day of the week to identify time windows of interest are also presented. Land border crossing operations have a significant influence on security and economic efficiency. We believe the techniques presented in this paper provide a scalable methodology for providing near real-time factual data on border crossing delays that provide important information for land border transport-managing stakeholders to make informed management decisions that balance security and economic efficiency.

Simulation analysis of school road traffic characteristics

This study aims to identify key factors and sensitive intervals affect the school road traffic characteristics. We collect traffic data from the parking area and the school road (400-700 meters). The simulation is calibrated to ensure the error of outputs are within 1.5%. A sensitivity analysis method is proposed, it makes the multifactor comparable. The sensitivity factors of vehicle delay, queue length, and average speed are 1.44, 2.03, and 0.28 in school road, and the bottleneck road are 3.07, 4.44, and 0.65. The traffic indicators change more concentrated in bottleneck road but greater in school road. 6 scenarios are created to analyze school road traffic characteristics. Traffic flow (TF), number of parking spaces (NPS), and stopping time (ST) are selected as variables. Scenarios 1-3 are univariate, and scenarios 4-6 are bivariate. TF is the key factor with a sensitivity interval of [1300,1700].

Study on the Management and Planning Methods of Transportation to and from Primary and Secondary Schools in Urban and Rural Areas

The transportation of urban and rural primary and secondary schools leads to traffic congestion in some sections, and the traffic order is chaotic, which seriously affects the travel of surrounding residents and greatly increases the additional traffic delay of pickup. How to effectively manage and plan the transportation of urban and rural primary and secondary schools and rationally plan the channelization of transportation roads have become a topic of great concern to the society. This paper will take several primary and secondary schools with representative traffic environment as the research object, analyze the behavior mechanism of the pick-up process, propose coping strategies for the school pick-up congestion problem under different road conditions, and plan a reasonable road channelization scheme and intersection design scheme. Finally, through the VISSIM simulation software, the proposed scheme and the original traffic environment are simulated and analyzed, and the vehicle delay before and after the improvement is compared, so as to find out whether the traffic planning and management scheme given in response to the school address in different traffic environments is reasonable. Through the simulation results, it is found that it is beneficial to reduce vehicle delay and avoid traffic jam by changing bidirectional to unidirectional on road section. In the vicinity of the intersection, one-way is the main way, and planning multiple drop-off points is helpful to solve the traffic congestion caused by school pick-up.

Does the minimization of the average vehicle delay and the minimization of the average number of stops mean the same at the signalized intersections?

Signal timings at signalized intersections are frequently optimized by considering commonly used vehicle delay models. It is generally believed that reducing the average number of stops can also decrease the average vehicle delay. Therefore, the aim of this research is to address the question: “Can similar performance outcomes be achieved through the Minimization of Average Vehicle Delay (MAVD) and the Minimization of Average Number of Stops (MANS)?” The first phase of the study entails the creation of two distinct signal timing optimization models based on the Akcelik average vehicle delay and average number of stops models. Subsequently, scripts were developed in MATLAB to identify the optimal signal timings for both approaches utilizing the Differential Evolution Algorithm. In the third phase, 30 traffic scenarios were generated, each varying in overall traffic volumes at the intersection. Subsequently, the signal timings derived from the MAVD and MANS approaches were applied independently to these scenarios, and performance indicators (average vehicle delay and average number of stops) were compared. The results reveal that the utilization of MANS-based signal timings instead of MAVD may lead to an increase in average vehicle delays of up to 113.55%. Additionally, it is demonstrated that when MAVD-based signal timings are applied instead of MANS, the average number of stops can increase by up to 16.28%. Finally, it is concluded that as the overall traffic volume at the intersection increases, these growth rates tend to decrease.