Total Delay Research Articles

A Knee-Guided Evolutionary Algorithm for Multi-Objective Air Traffic Flow Management

Air traffic flow management plays a crucial role in efficient aviation. Most existing studies assume the flight speed as constant throughout the trip, leading to ineffective fixed-speed schedules. To address this issue, we propose a new problem model, which allows variable speed control to improve the flexibility and maneuverability of the management. In addition, we consider two conflicting objectives, which are minimizing the total flight delays and conflicts between flights, where the conflicts depend on the flight 4D trajectories (3D position plus time). To solve this new challenging problem, we propose a novel multi-objective evolutionary algorithm with new problem-specific individual representation and search operators. Specifically, the multi-chromosomes encoding scheme is designed to adapt to different types of operations. Then, to search the huge search space effectively, we develop a hybrid crossover operator that recombines the parents based on their flight routes. Furthermore, to balance the exploration and exploitation, we develop a new mutation strategy to utilize the heterogeneous search potential of different individuals. For exploitation, the knee individual in the Pareto front is improved by a new time shift operator for exploitation, and other non-dominated solutions are mutated by fixed-route mutation. For exploration, the dominated solutions are mutated randomly. To verify the effectiveness, we compare it with the real air traffic flow management schedules and the state-of-the-art algorithms on a range of real-world air traffic datasets. Extensive results show that the proposed algorithm can significantly outperform the baselines in generating safe and efficient 4D trajectories.

AB078. Patterns of treatment delay in patients with symptomatic metastatic epidural spinal cord compression.

Delayed treatment in symptomatic metastatic epidural spinal cord compression (MESCC) is significantly associated with poorer functional outcomes. In this study, we aim to identify the patterns of treatment delay in patients and factors predictive of postoperative ambulatory function. Retrospective review of patients with symptomatic MESCC treated surgically between January 2015 and January 2022. MESCC symptoms were categorized into symptoms suggesting cord compression requiring immediate referral and symptoms suggestive of spinal metastases. Multivariate analysis was performed to identify factors predictive of postoperative ambulatory function. Delays in treatment were identified and categorized into patient delay (onset of symptoms till initial medical consultation), diagnostic delay (medical consultation till radiological diagnosis of MESCC), referral delay (from diagnosis till spine surgeon review) and surgical delay (from spine surgeon review till surgery) and compared between patients. One hundred and seventy-eight patients were identified. In this cohort 92 (52.0%) patients were able to ambulate independently, and 86 (48.3%) patients were non independent. One hundred and thirty-nine (78.1%) of patients had symptoms of cord compression and 93 (52.3%) had neurological deficits on presentation. On multivariate analysis, pre-operative neurological deficits (P=0.01) and symptoms of cord compression (P=0.01) were significantly associated with post-operative ambulatory function. Mean total delay was 66 days, patient delay was 41 days, diagnostic delay was 16 days, referral delay was 3 days and surgical delay was 6 days. In patients with neurological deficits, there was a significant decrease in all forms of treatment delay (P<0.05). There was no significant decrease in patient delay, diagnostic delay and referral delay in patients with symptoms of cord compression. Both patients and physicians understand the need for urgent surgical treatment of MESCC with neurological deficits, however there is still a need for increased education and recognition of the symptoms of MESCC.

Exploring the Therapeutic Potential of Silicon and its Impact on Crude Plant Drug Production in North East India

Silicon (Si) exhibits a beneficial impact in mitigating both biotic and abiotic stress, yet its potential significance in the context of medicinal plant and crude plant drug production, particularly in the strongly acidic soils of North East (NE) India, remains underreported. This study aimed to assess the effects of Si application on medicinal plants, focusing on growth, physico-chemical and biological attributes under stressful conditions.The study highlighted the benefits of Si application, particularly through foliar application, on medicinal plants such as lemongrass [Cymbopogon flexuosus (Steud) Wats], ginseng (Panax ginseng), coriander (Coriandrum Sativum L.) and bananas. Foliar Si application demonstrated improvements in plant growth and yield, employing morphological, physiological and biological responses in lemongrass and ginseng while enhancing the growth and yield of coriander by improving relative water content, total phenolic and total flavonoid content under water stress. Si applications also resulted in increased fresh and dry weight of bananas, alongside improvements in biochemical parameters, like increased starch and total sugar content and delay in banana ripening through the reduction of cellulose and amylase activity. The study underscores the effectiveness of Si in enhancing the growth and yield of medicinal plants. Within the context of NE India, this report draws attention to the considerable challenges faced in cultivating well-suited medicinal plants and crude plant drugs, crucial for herbal healthcare and the future well-being of humanity. Consequently, Si’s role in fostering t

Delay aware resource allocation in ORAN through network optimization

AbstractA multi variable resource allocation problem is investigated in network environments, specifically focusing on the consideration of quality of service in open radio access network. The main objective is to minimise the combined latency of various servers while complying with network limitations. The delay of each server is represented by a non‐linear function that has exponentially based. This characteristic inherently brings non‐convexity into the objective function. In contrast, the constraints comprise various linear combinations of network variables, including resource block allocations, power consumption, and number of virtual machines. The purpose of these constraints is to guarantee that the allocation of resources adheres to practical limitations and upholds fairness among servers. Nevertheless, the inclusion of a non‐convex objective function significantly adds complexity to the optimisation problem and non‐convex behaviour, requiring specialised algorithms and techniques to identify solutions. Subsequently, the Lagrange multiplier method has been used to solve this problem mathematically. Numerically, three algorithms have been utilised and compared to solve the problem, these are active‐set, interior point and sequential quadratic programming. Note that the total delay as an objective function is based on the total power consumption of the servers. Previous to optimising the total delay, a delay model is proposed and compared with two research works that are based on experimental and real time data. The proposed model shows data matching with the other works and permits for more adaptation/integration with any other works that uses different servers’ characteristics and network parameters.

Analysis and optimization of a Stochastic Petri Net for air-rail intermodal transportation.

Air-rail intermodal transportation (ARIT) plays a crucial role in China's intermodal transportation system. This study aims to model and optimize issues such as inefficiency and complexity in China's ARIT freight transportation using Business Process Reengineering and Stochastic Petri Nets theories. The Petri Net (PN) model for incoming freight transport in ARIT is based on actual operations, employing a new method involving Stochastic Petri Nets and isomorphic Markov Chains theory for performance analysis. Performance analysis results help intuitively identify areas needing optimization. Based on optimization principles, elements such as railway container packaging are improved, resulting in an optimized PN model for ARIT. Finally, data analysis shows that the optimized ARIT model reduces total delay by 7.7% compared to the original. This demonstrates that the new method, combining Markov Chain performance analysis and optimization principles, is feasible and effective for ARIT optimization.

Oversaturated intersections: A real-world assessment of polynomial fluid queue models

This paper investigates the challenges of quantifying stochasticity and dynamics in traffic states, crucial to efficient transportation systems, particularly during oversaturated conditions at signalized intersections. We build upon Newell's polynomial fluid queuing-theoretic ordinary differential equation (ODE) model as an example of model order reduction, with a focus on time-dependent stochastic arrival rates and departure rates. These assumptions facilitate dynamic arrival rate approximation using polynomial functions, which, when integrated with queue lengths, yield total delay per episode. Validation of Newell's polynomial fluid-queue based dynamic patterns is sought by analyzing cumulative cycles during peak periods, segmented based on residual queue values. A comprehensive real-world experiment employs emerging high resolution video detector cameras to capture various queue lengths, revealing recurring congestion patterns. Real-time arrival and departure count during peak periods further allow for the creation of cumulative curves, enhancing our understanding of congestion dynamics at signalized intersections under partially oversaturated conditions. This foundational understanding is essential for applying deep reinforcement learning in such environments, where the goal is to observe environment states to maximize expected long-term reward.

Optimization of Memristor Crossbar’s Mapping Using Lagrange Multiplier Method and Genetic Algorithm for Reducing Crossbar’s Area and Delay Time

Memristor crossbars offer promising low-power and parallel processing capabilities, making them efficient for implementing convolutional neural networks (CNNs) in terms of delay time, area, etc. However, mapping large CNN models like ResNet-18, ResNet-34, VGG-Net, etc., onto memristor crossbars is challenging due to the line resistance problem limiting crossbar size. This necessitates partitioning full-image convolution into sub-image convolution. To do so, an optimized mapping of memristor crossbars should be considered to divide full-image convolution into multiple crossbars. With limited crossbar resources, especially in edge devices, it is crucial to optimize the crossbar allocation per layer to minimize the hardware resource in term of crossbar area, delay time, and area–delay product. This paper explores three optimization scenarios: (1) optimizing total delay time under a crossbar’s area constraint, (2) optimizing total crossbar area with a crossbar’s delay time constraint, and (3) optimizing a crossbar’s area–delay-time product without constraints. The Lagrange multiplier method is employed for the constrained cases 1 and 2. For the unconstrained case 3, a genetic algorithm (GA) is used to optimize the area–delay-time product. Simulation results demonstrate that the optimization can have significant improvements over the unoptimized results. When VGG-Net is simulated, the optimization can show about 20% reduction in delay time for case 1 and 22% area reduction for case 2. Case 3 highlights the benefits of optimizing the crossbar utilization ratio for minimizing the area–delay-time product. The proposed optimization strategies can substantially enhance the neural network’s performance of memristor crossbar-based processing-in-memory architectures, especially for resource-constrained edge computing platforms.

Factors of cancer diagnosed family and medical system for delay in diagnosis and treatment of esophageal cancer

Introduction. The esophagus cancer patients needs early diagnosis to achieve better prognosis. The delay barriers increase progression of diseases to higher stages. The aim of work was to identify and explore the barriers in diagnosis of esophagus cancer and chart the time duration in this process. Material and Methods. The study was conducted in Jinnah Hospital Lahore (JHL), a teaching care hospital of Allama Iqbal Medical College, during a period of one year from July 2019 to July 2020. There were 49.42 % female and 50.57 % male patients. Results. Mean age found to be 56 years. Mean Total delay (TD ) time was 206 days and median Total delay (TD ) time in days were 197 days with 25th and 75th (157, 246) percentiles respectively. Longest delays were found in 51–60 age group and age related (r=0.94) significantly to delays (p=0.0004). Initial symptom of weight loss shows maximum delay and symptoms are highly correlated (r=0.94) with delay (p=0.004). The low income group presents maximum delay. Mean patient delay (PD ) time in days were 142 days with 135 Median and (88, 195) 25th and 75th percentile respectively. Mean, Median, 25th and 75th percentiles for Physician delays (PhyD) times were (20, 18, 16, 23) respectively. Treatment delay (TD ) time (Mean, Median) in days were (18, 18.52) with 25th and 75th (14, 22) percentiles. The education significantly reduces the delays with correlation (r=0.91) and significance (p=0.02). Mean system delay (SD ) time were 25 days and median were 24 days with 25th and 75th (20, 31) percentiles. Conclusion. Illiteracy, financial problems, and unavailability of information related to the cancer and health care system, very limited number of health facilities remains the main causes of these delays.

Cross-sectional study on evaluation of diagnostic interval in children with newly diagnosed paediatric cancers and factors affecting it

BackgroundDiagnostic interval/delay (DI) denotes the duration between first presentations to healthcare provider to the definitive diagnosis of cancer. Prolonged DI is a major contributor to deaths among children with malignancies in low-middle-income countries (LMICs). The data on DI and factors affecting it are limited from LMIC. MethodsThis cross-sectional study enrolled patients/children<12 years with proven/suspected malignancies. Children already on definitive chemotherapy, who died before a definitive diagnosis and with benign tumours were excluded. The parents or caregivers were interviewed individually using a customized questionnaire. Various intervals in referral chain like patient interval/delay (PI), DI and treatment interval/delay (TI) and factors affecting DI were evaluated. ResultsOut of 120 eligible children; 79 with median age 43 months (interquartile range [IQR]: 28–81) were analyzed. Haematological malignancies (n = 40) and non-haematological malignancies (n = 39) were equally distributed. Median total delay was 74 days (14–88 days). Median DI was 17 days (IQR: 8–54). The main contributor to delay was due to referral delay by primary and secondary physician (p < 0.05). The median PI was 2 day (IQR: 0–7.5) and median TI was 1 day (IQR: 0–4). Prolonged DI (DI > 30 days) was seen in 33 (41.8%) children. Trial of alternative medicine was the only significant factor associated with prolonged DI in univariate and multivariate analysis with odds ratio of 6.24. Other demographic, socioeconomic, health-seeking journey and disease-related factors were not found to be associated with prolonged DI. ConclusionsSignificant delay exists in paediatric cancer management in LMIC. Augmentation of physician and parental awareness is the key to decrease these delays.

Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning.

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.

Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

Employing machine learning techniques for estimating the differential code biases of GPS satellites

ABSTRACT In this study, the capabilities of Machine Learning (ML) are exploited to predict the Differential Code Biases (DCBs) of Global Positioning System (GPS) satellites from the broadcast Total Group Delays (TGDs), satellite numbers, satellite block types, and Sunspot numbers. Firstly, a detailed analysis of the DCB and TGD values over five years is provided. Then, different ML models are trained and tested. The results showed that the bagged trees, the rational quadratic Gaussian Process Regression, and the Feed-Forward Neural Networks (2 hidden layers) models can be used efficiently to predict the DCB values of GPS satellites.

Digital Twin-Enhanced Adaptive Traffic Signal Framework under Limited Synchronization Conditions

Unmanned traffic signal control is regarded as a sustainable intelligent management methodology. However, it faces the challenge of unpredictable traffic flow due to stochastic arrivals. The digital twin (DT) has emerged as a promising approach to address the challenges of time-varying traffic demand in urban transportation. Previous studies of DT-based adaptive traffic signal control (ATSC) methods all assume ideal synchronization conditions between the DT and the physical twin (PT). It means that the DT can immediately figure out the next neglecting limitation of realistic conditions, i.e., discrepancies between the DT and PT and computational ability. This paper proposes a DT-ATSC framework aimed at reducing the total delay at isolated intersections under limited synchronization conditions. The framework contains two parts: (1) a cell transmission model-based intersection simulation model featuring less computational consumption and the parameter self-calibration mechanism; and (2) scheme searching algorithms that can guide the DT to create optimization-oriented signal timing scheme candidates. Three options are provided for the scheme searching algorithms, i.e., grid search (GS), the genetic algorithm (GA), and Bayesian optimization (BO). A testing platform is created to validate the effectiveness of the proposed DT-ATSC. Experimental results indicate that the proposed DT-ATSC-BO outperforms the DT-ATSC-GA and DT-ATSC-GS. Meanwhile, the average vehicle delay of the DT-ATSC-BO is up to 53% lower than that of the current adaptive signal control method, which indicates that the proposed DT-ATSC has achieved the expected effect. Moreover, compared to the previous related work, the proposed DT-ATSC framework is more likely to be able to be applied in realistic situations because synchronization issues are incorporated in the design of the DT-ATSC by assuming a limited margin time for a decision.

Resilient enhancements of routing protocols in MANET

Resilient of routing processes is one of the biggest challenges for data transmission in mobile networks without infrastructure. Communication under current routing protocols is through a communication path that, although the shortest, may not perform satisfactorily in terms of resilient. Routing and communication within such a path may take place using nodes that are malicious or inappropriate in the communication process due to malicious or poor technical state. This paper presents a new algorithm for various uses of mobile ad hoc networks not only in edge networks with infrastructure but also with the possibility of being used in the cloud solutions. We have modified decentralized blockchain technology and artificial intelligence using deep learning methods that have been implemented in routing processes. The objective of this algorithm was to select the most resilient communication path from the source to the destination node. Such a communication path selection consisted of selecting the nodes that were most suitable in terms of resilience, where the selection nodes was provided through a network and technical parameters. The key quality of service metrics, throughput, total delay, number of delivered signaling and data packets and the ratio between them were used to evaluate the proposed resilient routing algorithm. Modified resilient routing protocols achieved improvement in all the analyzed parameters compared to the original routing protocols. The improvement in these parameters led to an increase in the resilience of the routing process based on the actual data obtained from each node in the network and previous communications.

The Effect of Knife Wear and Sharpening Mode on Chipper Productivity and Delays

The production of wood chips can be achieved using different types of wood chippers whose productivity can be influenced by many factors including proper knife management. Research was conducted to determine the productivity of the new Diamant chipper in chipping air-dried tops stacked at a roadside landing and to compare the efficiency of dry sharpening and wet sharpening in restoring chipper productivity, the time required by dry sharpening with that of knife replacement, and the cost of dry sharpening to knife change in real-life conditions. To clearly define the influence of knife management, a model of the effect of knife wear on chipper productivity was produced. Analysis of variance was used to check the significance of any differences in chipping and total time consumption per cycle. Multiple regression was used to express the relationship between chipping time consumption per cycle and the cumulated mass processed by a set of knives—the latter taken as an indicator of knife wear. The study lasted 10 full workdays, included a total of 136 truckloads or 3560 t of fresh wood chips (or green tons = gt), and resulted in the average productivity of 59.0 gt per productive chipping hour (excluding all delays) or 39.4 gt per machine scheduled hour (including all delays). Delays represented 37% of total worksite time. Knife management (dry sharpening or change) accounted for 30% of the total delay time due to raw material contamination. Dry sharpening took 30% less time than a full knife change. As wear accumulated and knives lost their edge, the chipping time per cycle increased from 25 in the first cycle (full truck load) to 38 min in the third cycle. The presented study offers robust productivity figures, together with a reliable estimate of the productivity losses caused by knife wear, and could help improve knife management in order to increase chipper productivity as well as reduce unnecessary delays.

Train dispatching program for high-speed railway station based on genetic algorithm

In case of train delays, centralized traffic control system become disabled, and the workload of dispatchers increases dramatically. Based on genetic algorithm, the author designs a program to appropriately reschedule trains in terms of delays, minimizing the total delay time and changes of gate. The author transformed the initial problem to a compromised combinatorial optimization model, with total delay time, changes of gate and conflicting routes as objectives. The high weighting in conflicting routes ensures efficiency and high probability of obtaining a feasible solution. With discreate variants, the author designs special coding and evolving method suitable for this problem. Using a special treatment for conflicts and initializing chromosomes, the program can construct new timetable quickly given the scheduled timetable, predicted arrival time and order of trains (optional), which promotes the efficiency and security of dispatching in high-speed railway stations. The method was tested with a synthetic data of Shanghai-Kunming section of Hangzhou East Railway Station.

Uncertainty disturbance estimator control for delayed linear systems with input constraint

This paper proposes a novel Uncertainty and Disturbance Estimator (UDE) based controller for Multi-Input Multi-Output (MIMO) systems. The controller handles input constraints, time-varying matched uncertainties in state and input matrices, unknown input–output delays, and partial state measurement. The UDE error dynamics are augmented with a time-varying variable to handle input constraints effectively. The proposed controller employs a Proportional Integral Observer (PIO) and a state predictor to accurately estimate the future state of the system with only partial state measurement. We develop sufficient conditions for system stability in tracking a reference model while only an estimation of the total delay is available. Numerical simulations and experiments on a multi-rotor platform validate the proposed controller’s effectiveness, demonstrating improved tracking performance and stability compared to existing methods. The proposed UDE-based controller, with a PIO and state predictor, is capable of handling various matched uncertainties and disturbances in MIMO systems with input constraints and partial state measurement. It offers improved tracking performance and stability compared to existing studies.

Exploring the first-stage ignition and model optimization in the comprehensive study of n-dodecane oxidation

n-Dodecane is commonly employed as a surrogate for investigating the combustion characteristics of jet and diesel fuels. Enhancing comprehension of its combustion behavior and developing accurate chemical kinetics models for simulating combustion is of paramount importance in engine development. This study focuses on a detailed exploration of n-dodecane oxidation kinetics under low-temperature conditions and presents a novel dataset concerning the first-stage ignition delay time. A broad spectrum of experimental conditions is investigated, encompassing a range of temperature (600 ∼ 1350 K), pressure (5 ∼ 20 atm), equivalence ratios (0.5 ∼ 1.0), and dilution gases (N2 and Ar). Additionally, combustion experiments in a pure oxygen environment are performed, contributing valuable data to existing research. To enhance the precision of the chemical reaction kinetics model of n-dodecane, this study integrates updated rate coefficients obtained from the latest theoretical calculations for specific reaction classes. The improved rate rule provides a more accurate reference for the construction of the chemical reaction kinetics model of long straight alkane. The resulting improved model excels in accurately predicting both the first-stage ignition delay time and the total ignition delay time under a wide range of operational conditions. Additionally, the model performance is rigorously evaluated through a comprehensive assessment against a diverse array of datasets gathered from various literature references. The results show that, in contrast to the previously proposed model, this enhanced model provides highly reliable predictions over a broad range of parameters.

Integrated scheduling of production and material delivery for the intelligent manufacturing system

This paper addresses the issue of integrated scheduling of production and material delivery in an intelligent manufacturing system. The system includes multiple flexible flowlines and a fleet of automated guided vehicles (AGVs), whose capacity is shared among the flowlines. Multiple orders from different customers are processed on the flowlines, with AGVs delivering materials in different batch sizes based on varying product quantities of orders and limited load capacity. The problem is formulated as a mixed-integer linear programming model to minimise total order delays and the number of used AGVs. To handle the complexity caused by the uneven temporal coupling relationship between production and material delivery, a bi-level neighbourhood search solution algorithm is proposed. This algorithm uses variable neighbourhood search for production scheduling and adaptive large neighbourhood search for material delivery scheduling. Extensive numerical experiments using real-case-based instances are conducted to demonstrate the satisfactory performance of our proposed algorithm. Furthermore, we emphasise the advantages of integrated scheduling compared to traditional sequential scheduling across various problem settings.

Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Optimization of green signal timing for each phase at a signalized intersection in an urban area is critical for efficacious traffic management and congestion mitigation. Many algorithms are developed, yet very few are for cities in developing nations where traffic is characterized by its heterogeneous nature. While some of the recent studies have explored different variants of Max Pressure (MP) and Reinforcement Learning (RL) for optimizing phase timing, the focus is limited to homogeneous traffic conditions. In developing nations, such as India, control systems, like fixed and actuated, are still predominantly used in practice. Composite Signal Control Strategy (CoSiCoSt) is also employed at a few intersections. However, there is a notable absence of advanced models addressing heterogeneous traffic behavior, which have a great potential to reduce delays and queue lengths. The present study proposes a hybrid algorithm for an adaptive traffic control system for real-world heterogeneous traffic conditions. The proposed algorithm integrates Max Pressure with Reinforcement Learning. The former dynamically determines phase order by performing pressure calculations for each phase. The latter optimizes the phase timings for each phase to minimize delays and queue lengths using the proximal policy optimization algorithm. In contrast to the past RL models, in which the phase timing is determined for all phases at once, in the proposed algorithm, the phase timings are determined after the execution of every phase. To assess the impact, classified traffic volume is extracted from surveillance videos of an intersection in Ludhiana, Punjab and simulated using Simulation of Urban Mobility (SUMO). Traffic volume data is collected over three distinct time periods of the day. The results of the proposed algorithm are compared with benchmark algorithms, such as Actuated, CoSiCoSt, and acyclic & cyclic Max Pressure, Reinforcement Learning-based algorithms. To assess the performance, queue length, delay, and queue dissipation time are considered as key performance indicators. Of actuated and CoSiCoSt, the latter performs better, and thus, the performance of the proposed hybrid algorithm is compared with CoSiCoSt. The proposed algorithm effectively reduces total delay and queue dissipation time in the range of 77.07%–87.66% and 53.95%–62.07%, respectively. Similarly, with respect to the best-performing RL model, the drop in delay and queue dissipation time range from 55.63 to 77.12% and 22.13 to 43.7%, respectively, which is significant at 99% confidence level. The proposed algorithm is deployed on a wireless hardware architecture to confirm the feasibility of real-world implementation. The findings highlight the algorithm’s potential as an efficient solution for queues and delays at signalized intersections, where mixed traffic conditions prevail.