Real-time merging traffic control for throughput maximization at motorway work zones

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weather-related crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.

Traffic crisis frequently happen because of traffic burden by the large number automobiles are on the path. Increasing transportation move and decreasing the average waiting time of each vehicle are the objectives of cooperative intelligent traffic control system. Each signal wishes to catch better travel move. During the course, signals form a strategy of cooperation in addition to restriction for neighboring signals to exploit their individual benefit. A superior traffic signal scheduling strategy is useful to resolve the difficulty. The several parameters may influence the traffic control model. So it is hard to learn the best possible result. Traffic light controllers are not expert to study from previous results. Due to this they are unable to include uncertain transformation of traffic flow. Reinforcement learning algorithm based traffic control model can be used to obtain fine timing rules by properly defining real time parameters of the real traffic scenario. The projected real-time traffic control optimization prototype is able to continue with the traffic signal scheduling rules successfully. The model expands traffic value of the vehicle, which consists of delay time, the number of vehicles stopped at signal, and the newly arriving vehicles to learn and establish the optimal actions. The experimentation outcome illustrates a major enhancement in traffic control, demonstrating the projected model is competent of making possible real-time dynamic traffic control.

Freeway work zones with lane closures can cause traffic disruption and congestion with increased travel time, safety risks, and emissions.Variable speed limit (VSL) control has been widely studied to mitigate the negative impacts of work zone lane closures.To ensure effective VSL control, accurate detection of traffic conditions is important.However, stationary sensors from distant upstream or merging areas of a work zone can only provide location-based detections, which may not be sufficient for effective VSL control.Therefore, this study proposes a VSL control system for a smart work zone with a weighted traffic density estimation algorithm using data from stationary sensors and connected vehicles (CV).With location-based traffic detections from distant upstream and merging areas of a smart work zone, data from CVs in locations between the stationary sensors are used to obtain the weighted density.This weighted density is then fed to a feedback VSL controller, which can dynamically define the appropriate speed limits to reduce the negative impacts of the work zone closures.The proposed VSL controller was implemented and evaluated under a calibrated freeway work zone environment using the traffic simulator SUMO.The results show that the system can provide accurate traffic density estimation and effectively improve traffic mobility, safety, and environmental sustainability near the work zone area.

Traffic control is very important for two-lane highway lane-closure work zone traffic management. Control of the open lane’s right of way is very similar to that of a two-phase signalized intersection. Thus, four control strategies including flagger control, pre-timed control proposed by Schonfeld, pre-timed control proposed by Webster, and actuated control are employed for possible use at work zones. Two primary methodologies, the mathematical delay model adopted from signalized intersections, and the simulation model calibrated with field data, are proposed. The simulation and mathematical results show that control strategies for two one-way road intersections could be used for two-lane highway lane-closure work zones. Flagger control after gap-out distance optimization prevails over all the other control strategies in terms of stopped delay, queue length, and throughput, under low or high volumes. Actuated control could be a good alternative for work zone areas due to its small queue length and large vehicle throughput under moderate volume conditions. Our findings may help to optimize the work-zone control strategy and improve operational efficiency at two-lane highway lane-closure work zones.

This paper presents an acyclic real-time traffic signal control model with transit priority based on a rolling horizon process for isolated intersections. The developed model consists of two components, including: an Improved Genetic Algorithm (IGA)-based signal optimization module and a microscopic traffic simulation module. The acyclic real-time traffic signal control model optimizes the phase sequence and the phase length with the aim to minimize the total delay of both transit vehicles and general vehicles for the next decision horizon. Numerical results show that the proposed IGA signal optimization module could provide a more efficient search for optimal solutions. The results also show that the acyclic real-time traffic signal control model outperforms the fixed-time control model. It prioritizes transit vehicles while minimizing the impact on the general vehicles.

This paper aims to evaluate the effects of work zone configuration, traffic flow, and heavy vehicle percentage on traffic delay at work zones by using a heterogeneous cellular automata (HCA) model. The activity area length and transition area length are two work zone configuration factors taken into account. The average traffic delay per work zone and the average traffic delay per 100m are the two indices used to express traffic delay at work zones. The graphical comparison results show that the transition area length has much bigger effects than the activity area length on traffic delay, especially in the light traffic condition. The function of average traffic delay per work zone is finally formulated that can be used to estimate the marginal effects of the activity area length, transition area length, traffic flow and heavy vehicle percentage.

Critical road user safety deficiencies within road work zones across Sri Lanka are addressed in this research, where the construction and maintenance of transportation infrastructure significantly influence risk exposure. A comprehensive nationwide analysis revealed that widespread non-compliance with the national guidelines on traffic control for road work areas primarily contributed to elevated hazards. Core safety challenges identified include inadequate traffic control measures, insufficient illumination, absence or inadequacy of warning signage, inconsistent enforcement of safety protocols, and adverse geographic or environmental conditions. Crucially, the study diagnosed a fundamental methodological gap: inconsistent risk assessment by consulting engineers due to varying levels of expertise, subjective judgment, and the lack of a standardized, quantitative framework. This inconsistency impedes accurate identification of risk levels (high, medium, low) and hinders effective mitigation planning. To overcome these limitations, this study introduces a novel, intelligent risk assessment platform leveraging machine learning (ML) for the automated, objective, and consistently classified work zone safety levels. The core innovation lies in integrating mathematical rigor with real-world engineering validation. The methodology employed a mixed-method strategy across 40 representative work zones. Data encompassed detailed field surveys capturing physical conditions, traffic parameters, and compliance metrics, alongside expert insights. Risk was quantified along three key dimensions: severity, exposure, and likelihood. These indices were derived using a weighted Likert-scale system applied to observed risk factors. The calculated risk index level was identified through a clustering algorithm (K-means), which was then applied to these indices to empirically define high, medium, and low-risk categories, providing a data-driven baseline. Subsequently, multiple supervised ML algorithms were trained and optimized using the field data and the cluster-derived risk labels as ground truth. The primary task was classification into the predefined risk categories based on the input risk factor assessments. Crucially, model validation incorporated rigorous comparison against independent safety evaluations conducted by experienced engineering professionals. This ensured the ML platform's outputs aligned with practical, on-ground engineering judgment, bridging the gap between algorithmic prediction and real-world applicability. Results indicated a concerning prevalence of medium to high-risk classifications across the surveyed zones, confirming significant non-compliance with national standards, particularly regarding signage, lighting, and protective barriers. Among the ML models evaluated, the random forest algorithm demonstrated superior performance, achieving the highest classification accuracy while maintaining strong interpretability through feature importance analysis, making it optimal for field deployment. This research delivers a validated, intelligent ML-based platform that provides scalable, consistent, and objective safety assessments for road work zones in Sri Lanka. It directly addresses the identified problem of inconsistent engineer evaluations by offering a standardized, data-driven methodology. The platform calculates a composite risk index for work zones by integrating predictions from a random work zone model with an audited work zone database. This index, derived from input risk factors, classifies work zone risk into three levels: low (0 ≤ index < 0.204), medium (0.204 ≤ index < 0.298), and high (0.306 ≤ index ≤ 1). Using the established classification levels and developed platform, a comprehensive risk identification system was formulated based on variations in the risk index and its relationship with key factor combinations. This approach enables a dual-path risk identification method that enhances accuracy and reliability. The platform functions as a powerful decision-support tool for engineers, policymakers, and contractors, facilitating targeted and data-driven safety interventions. By integrating advanced analytics with domain expertise validation, the study delivers a significant technological advancement in road safety management. This robust solution holds particular relevance for Sri Lanka's extensive ongoing and planned highway infrastructure projects, supporting more informed and effective safety strategies.

<b>Objective:</b> Work zone safety is one of the top priorities for transportation agencies. In recent years, a considerable volume of research has sought to determine work zone crash characteristics and causal factors. Unlike other non–work zone–related safety studies (on both crash frequency and severity), there has not yet been a comprehensive review and assessment of methodological approaches for work zone safety. To address this deficit, this article aims to provide a comprehensive review of the existing extensive research efforts focused on work zone crash-related analysis and modeling, in the hopes of providing researchers and practitioners with a complete overview.<b>Methods:</b> Relevant literature published in the last 5 decades was retrieved from the National Work Zone Crash Information Clearinghouse and the Transport Research International Documentation database and other public digital libraries and search engines. Both peer-reviewed publications and research reports were obtained. Each study was carefully reviewed, and those that focused on either work zone crash data analysis or work zone safety modeling were identified. The most relevant studies are specifically examined and discussed in the article.<b>Results:</b> The identified studies were carefully synthesized to understand the state of knowledge on work zone safety. Agreement and inconsistency regarding the characteristics of the work zone crashes discussed in the descriptive studies were summarized. Progress and issues about the current practices on work zone crash frequency and severity modeling are also explored and discussed. The challenges facing work zone safety research are then presented.<b>Conclusions:</b> The synthesis of the literature suggests that the presence of a work zone is likely to increase the crash rate. Crashes are not uniformly distributed within work zones and rear-end crashes are the most prevalent type of crashes in work zones. There was no across-the-board agreement among numerous papers reviewed on the relationship between work zone crashes and other factors such as time, weather, victim severity, traffic control devices, and facility types. Moreover, both work zone crash frequency and severity models still rely on relatively simple modeling techniques and approaches. In addition, work zone data limitations have caused a number of challenges in analyzing and modeling work zone safety. Additional efforts on data collection, developing a systematic data analysis framework, and using more advanced modeling approaches are suggested as future research tasks.

Traffic control at road intersections is based on traffic lights (TLs). The control mechanism typically used for traffic lights operates based on a periodic schedule to change the light (red/yellow/green). In many cases, a different schedule is used in late night/early morning hours. This fixed control mechanism does not adequately account for changing traffic conditions, and is unaware of/unresponsive to congestion. In this work, we propose a framework for dynamic traffic light control at intersections. The framework relies on a simple sensor network to collect traffic data and includes novel protocols for traffic flow control to handle congestion and facilitate flow. We show that our proposed algorithms have low overhead and are practical to employ in live traffic flow scenarios. Through extensive simulations, we demonstrate the benefits of our framework in optimizing traffic flow metrics, such as traffic throughput, average vehicle waiting time, and vehicle waiting line length.

Rapid urbanization and increasing ownership of private vehicles have led to severe traffic congestion, which has become a critical challenge in modern urban areas operated with signalized intersections. This study proposes and evaluates a novel AI-based composite framework for traffic signal control optimization, which integrates a Long Short-Term Memory (LSTM) model for traffic flow prediction, a Deep Neural Network (DNN) for vehicle speed estimation, and a Genetic Algorithm (GA) for signal phase optimization under open-loop and closed-loop objectives. It has been validated through high-fidelity microscopic simulations (i.e. VISSIM), calibrated with field data at the atypical six-leg intersection in Busan, Korea. This study found that while the overall performance of the framework for 24 hours exhibits clear trade-offs (i.e. slight total traffic throughput decrease of 2.2%), its primary value lies in its demonstrated ability to effectively mitigate severe congestion during peak hours (i.e. speed increase of up to 38.5% during the evening peak), delivering significant gains in traffic quality in need. This study also revealed that the system actively performs traffic flow redistribution as an intelligent traffic control strategy. The contributions of this study are threefold. First, it provides a methodological blueprint for an interpretable, modular control framework that decouples prediction processes from optimization, offering a transparent alternative to end-to-end Reinforcement Learning (RL) models. Second, this study offers crucial empirical insight for the evaluation of adaptive traffic control systems. Their true effectiveness may be best measured by peak hour performance rather than 24-hour averages. Finally, this study makes a conceptual contribution by identifying and quantifying the ‘reality gap’ between the predictions of an offline-trained optimizer and the outcomes of a high-fidelity simulation, highlighting a fundamental challenge for the field. HIGHLIGHTS A hierarchical AI framework is an alternative to reinforcement learning model Closed-loop outperforms open-loop in optimizing real time traffic signal control A speed stabilization objective is adopted over a speed maximization approach Leading to tangible gains in traffic quality during peak hour over 24-hour average

This study focuses on improving the air traffic control and management of local airspace in China. We develop five modules to establish a decision-support system, which constitutes the analysis and strategy layer of a local airspace digital twin. This integrated solution utilizes multi-source multimodal data, ranging from flight status, weather, to real-time air traffic control strategies. Specifically, the first module reviews the operational performance of the airspace and assesses the maximum capacity of an airspace under particular weather scenarios. The second module forecasts the departure times of all incoming flights to inform potential congestion. The third module is the core module simulating the local airspace operation under given weather conditions and control strategies. In the fourth module, we compare the simulation results with the assessed capacities, and suggest adjustments to control strategies proposed by air traffic controllers. The fifth module applies mixed integer programming to obtain the optimal air traffic control strategies.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Segment with lane drops are very important in freeway systems since they are major constrains to traffic flow and safety. The frequency of capacity reductions and higher safety risks is proportional to an increase in lane-changing actions, which worsen traffic congestion, decrease road capacity, and increase the risk of an accident. Traditional traffic management strategies that rely on physical structures and driver’s decision making often fail under such conditions. This paper provides a detailed lane change control strategy specific to freeway segments with lane reduction in the connected and autonomous vehicle (CAV) environment. The strategy combines both centralized and decentralized techniques to improve the vehicle’s lane-changing behavior and density. A cellular transmission model of lane-level is proposed for the centralized control of the linked vehicles based on the ratio of the driver compliance. The model derives the density equation and transforms the lane-changing problem before the work zone into merging traffic flow problem. The optimization model is developed based on the total trip time, density deviation, and total lane changes, with constraints on the cell reception capacity and lane changing ratios. Control parameters for lane change distribution are identified using genetic algorithms to solve the problem. For the decentralized control, a reinforcement learning solution is introduced which uses deep Q-networks (DQN) to improve lane-changing actions. The reward function takes into account the traffic efficiency and the impact of lane changing, and the continuous action space is discretized for application. The control mechanism is evaluated by the simulation of a work zone scenario that includes two restricted lanes on the Shanghai-Nanjing Expressway. It also shows that there is an improvement of 3% to 6% in traffic flow and velocity as compared to single-strategy approaches. The collaborative control strategy significantly enhances traffic flow and reduces congestion at bottlenecks and offers valuable information for future traffic control in CAV environments.&lt;/div&gt;&lt;/div&gt;

In highway work zones, lane-changing behaviour near bottlenecks is more urgent than in general segments, referred to as delayed mandatory lane-changing (DMLC). This study proposes an enhanced cellular work zone automata model (DMLC-EWCA) that incorporates DMLC position probability distribution for micro-simulation. Analysing 12 cases from China, the study shows that lane-changing behaviour varies by lane and is influenced by traffic flow and work zone configuration. Extreme value theory is applied to model DMLC position distribution, and lateral lane-changing rules in DMLC-EWCA are improved. The results highlight that traffic flow, speed, and transition area length are key factors influencing vehicle DMLC position. The DMLC-EWCA outperforms the previous CA model at both microscopic and macroscopic levels. A case study applying the DMLC-EWCA optimised transition area length, enhancing innermost lane usage and overall traffic efficiency. The DMLC-EWCA offers a more accurate approach for modelling traffic flow in work zones.