Simulation‐Based Optimization Method for Impact Evaluation to Work Zones in Large‐Scale Networks

Construction work zones are inevitable parts of daily operations at roadway systems. They have a significant impact on traffic conditions and the mobility of roadway systems. The traffic impacts of work zones could significantly vary due to several interacting factors such as work zone factors (work zone location and layout, length of the closure, work zone speed, intensity, and daily active hours); traffic factors (percentage of heavy vehicles, highway speed limit, capacity, mobility, flow, density, congestion, and occupancy); road factors (number of total lanes, number of open lanes, and pavement grade and condition); temporal factors (e.g., year, season, month, weekday, daytime, and darkness); weather conditions (rainy, sunny, and snowy); and spatial factors (road lane width, proximity, and number of ramps). Utah Department of Transportation (UDOT) is continuously collecting and storing project-related data. Due to the significant impact of work zones on traffic conditions, they are interested in evaluating the impacts of work zone attributes on mobility and traffic conditions of roadway systems within the state of Utah. Such an analysis will help the UDOT personnel better understand and plan for more efficient work zone operations, select the most effective traffic management systems for work zones, and assess the hidden costs of construction operations at work zones. To help UDOT address this problem, we propose a robust, deep neural network (DNN) model capable of evaluating the impacts of the factors mentioned earlier on the mobility conditions of Utah roadway systems. DNNs can capture all the relationships between input variables and output compared to traditional machine learning algorithms. The results of this project show that work zone features have an important effect on the traffic condition. In the end, the performance of the model is evaluated using three different measures, including R2 score, RMSE, and MAE. Comparing the measurement to previously conducted research, it is the first study that has attempted to investigate the effect of work zone features on hourly traffic volume.

Evaluation of emission level increment in urban and sub urban highway work zones

Many work zones exist in the urban road network and have a great negative impact on city traffic. Finding the optimal work zone schedule can minimize the negative impact of work zones on traffic. This paper focuses on the day-based work zone scheduling problem in the urban network. Existing studies on the day-based work zone scheduling problem do not consider the progression of day-to-day traffic from a non-equilibrium state to an equilibrium state during the construction period. For the first time, this paper proposes a model for day-based work zone scheduling by introducing a day-to-day traffic assignment model, in which the target of the optimization problem is minimizing the increase in travel cost caused by work zones. Numerical examples are presented to explore the variations of the optimal construction sequence for different work durations, crew numbers, and model parameter values. Some new findings are obtained in the paper. When the construction duration of each work zone is relatively short (for example, less than 20 days), the optimal scheduling will obviously change with the work duration; when all of the construction durations increase to a threshold (for example, 60 days), the optimal construction sequence will no longer change. An optimal crew number exists that can minimize the increment of travel cost caused by work zones. During the construction period, the total travel cost in the network can be decreased by guiding travelers to change their original travel habits.

The impact of work zones on mean travel time and travel time reliability has gained attention as agencies focus on performance measurement. Travel time reliability in work zones often has been difficult for agencies to quantify because of the time and expense required to collect travel time data. Several private-sector companies have begun to sell travel time data; this availability has created an opportunity to examine cost-effectively work zone impacts on travel time reliability on a broader basis. The Virginia Department of Transportation recently acquired probe vehicle-based travel time data for 2010 from a private-sector data provider. These data were used to calculate measures of travel time reliability at 15 work zones and to examine factors that affected travel time reliability. Travel time reliability was quantified with 95th percentile travel time, a buffer index, and a planning time index. The work zones experienced a statistically significant degradation across all measures of travel time reliability as compared with baseline conditions. Work zone mean buffer index, planning time index, and 95th percentile travel time rates were higher by 48%, 18%, and 16%, respectively. Although lane closures occurred during off-peak periods, work zones that involved lane closures experienced increases in their mean buffer index, planning time index, and 95th percentile travel time with rates of 67%, 23%, and 22%, respectively. Annual average daily traffic per lane and the number of access points per mile were found to have the most obvious relationships with declines in reliability at work zones.

Pakistan has a population of over 199 million and total road network of approximately 264,400 kilometers that serve about 16.2 million vehicles of all types. According to WHO estimates, there were approximately 30,000 annual Road Crash Fatalities (RCF) in Pakistan in year 2010. Work zone crashes account for significant proportion of all traffic crashes in Pakistan due to higher crash rate as compared to other parts of the highway network. Highway work zone is referred to road area where highway construction, maintenance or activity related to utility maintenance takes place. Workers in highway work zone are exposed to a variety of hazards and face risk of injury and death from construction equipment as well as passing motor vehicles. Safety measures and better understanding of risks involved while moving through the work zone have significant effects on the overall safety climate at work zones. Mismanaged work zone traffic increases the travel time delays, safety issues, vehicle operating costs (VOC) and other associated costs. Performance measures for the work zone traffic management includes user costs (travel time delay, crash cost, VOC), incident response and clearance time, queue length and community complaint. Different work Zone Traffic Management methodologies are being practiced worldwide and extensive research has been carried out at international level on country specific highway work zone safety but in Pakistan very few studies have been carried out on highway work zones. This research aims to critically analyze the impacts of the different Work Zone Traffic Management methods and recommend the best option for Expressways / Motorways in Pakistan. For the purpose of research, the author has selected a section of Motorway (M-2). Two different work zone traffic management methods are being followed in rehabilitation of M-2 .i.e. Parallel / Adjacent Method and Median Cross-Over Method. Performance measures selected for purpose of analysis are Travel Time Delay, Crash Cost (Safety) and VOC and data collected on section of M-2. After critical analysis, it is concluded that Parallel/ Adjacent Method results in lesser Travel Time delay and lesser VOC due to higher operating speeds and thus has higher savings, whereas Median Cross over Methods yield more Crash Cost savings. Keeping in view the driving habits / skills, poor maintenance of vehicles in Pakistan and risk of fatal accidents involved in parallel /adjacent method, median cross over method is preferred.

Traffic distribution across highways is a critical aspect of traffic analysis and management. Proper and homogenous lane distribution enhances safety and minimizes accidents. This study aimed to investigate lane flow distribution on an Egyptian freeway. The recently constructed Cairo–Banha freeway is 39 km with four travel lanes in each direction. Based on 2022 traffic data, the methodologies employed in this study included analysis and validation of lane flow distribution across both travel directions. The Longitudinal control model (LCM), which incorporates driver behaviors into fundamental traffic diagrams, was then calibrated for the entire freeway and each individual lane. Additionally, comparisons with other studies were conducted in order to contextualize the findings and highlight variations in driver behaviors. Results revealed significant variability in lane flow distribution, as indicated by the traffic flow distributions and speed distributions, as well as the optimal values for flow, density, and speed determined in the study. Unlike the Highway capacity manual (HCM), which generally assumes uniform lane distribution under standard conditions, the Cairo–Banha freeway exhibited nonuniform lane distribution. This variability underscores the need for localized traffic models and microsimulations for improved analyses of traffic distribution. Furthermore, these findings can be utilized to mitigate and manage the unequal traffic distributions of the freeway to enhance its capacity and increase the safety of road users. The first of its kind in Egypt, this study serves as a proof of concept for the application of the LCM to Egyptian roadways, laying the foundation for future studies to refine traffic models and address region-specific traffic dynamics.

Improving traffic management through consideration of uncertainty and stochastics in traffic flow

National Defense Traffic and Transport Networks (NDTTN) is an important research object in transportation engineering and characteristics of its topology lay the foundations for national defense traffic networks programming and logistic management. This paper mainly studies the complexity of traffic and transport networks and related problems. The scale-free behavior of NDTTN is investigated and the example analysis is given. According to the flux restriction and the chain failure in the traffic networks, a traffic and transport networks breakdown model based on the node loads is established so as to validate the robustness of traffic and transport networks. Furthermore, the increase mechanism of traffic and transport networks is deep analyzed, and then a scale-free NDTTN model based on the local better choice is proposed. By simulation, it is proved that the general performance of traffic and transport networks constructed with the new model is optimized.

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

In the optimization of mixed urban and freeway networks, most existing control strategies implement centralized controllers that are not appropriate for real-time control in large-scale traffic networks due to the huge computational complexity. In this article, a distributed control framework for the cooperative control of mixed urban and freeway traffic networks is developed. First, an integrated traffic model is presented to characterize the interactions between the urban and freeway networks. In addition, a partitioning method is proposed, and then, the mixed traffic network is divided among urban agents and freeway agents for the easy implementation of the distributed control framework. The optimization problem of the whole network can then be decomposed into a number of suboptimization problems based on the partition of the network. In the proposed distributed control strategy, each agent solves its own optimization problem independently with local information and transmitted information from neighboring agents to seek the Nash equilibrium. Finally, the computational efficiency and the effectiveness of the distributed integrated control strategy of mixed traffic networks are evaluated through a numerical example.

Urban road maintenance is an important part of urban traffic management. However, in modern cities, road maintenance work needs to occupy some traffic resources; therefore, unreasonable road maintenance schemes often lead traffic networks to unexpected large-scale congestion. In this paper, a dynamic programming model is proposed in order to minimize the delay caused by road maintenance scheme. This model can obtain a globally optimal maintenance scheme which contains the decisions and sequence for every stage of maintenance. Each stage of this model can be boiled down to a discrete network design problem. This model helps make suggestions for the traffic managers with the request of minimizing the delay caused by the maintenance scheme. This paper uses two examples to illustrate this method, one is a small-scale Nguyen-Dupuis network, and the other one is a larger scale Sioux-Falls network.

Traffic demand management is an effective way to solve the problem of urban traffic congestion. Due to the effects of the social economic development, population quality, scientific and technological level and other related factors, the existing traffic demand management systems are lack of effective coordination and management mechanism. Although the traffic demand management measures have achieved some results, but due to lack of effective coordination and management mechanism between them, resulting in poor practical application. By analyzing and summarizing the current situations of traffic demand management, the paper explores an innovative traffic demand management mechanism based on comprehensive traffic planning and modern information technology and economic means. Under the new demand management mechanism, the urban transportation system runs more orderly, which can help to largely increase residents' travel satisfaction and social and economic benefits. On the basis of analyzing and summarizing the current research status of traffic demand management, this paper conducts innovative research on traffic demand management mechanism based on the implementation effect of existing traffic demand management. In view of the existing shortcomings in the systematic and scientific traffic demand management, and insufficient traffic law enforcement quality problems, the paper explores the innovative traffic demand management mechanism based on comprehensive transportation planning, modern information technology and economic means.

The primary goal of Strategic Highway Research Program 2 (SHRP 2) reliability research is to improve the reliability of highway travel times by reducing the frequency and effects of events that cause travel times to fluctuate unpredictably. Seven potential sources of unreliable travel times—that is, events that cause variable travel times—have been identified: traffic incidents, work zones, demand fluctuations, special events, traffic control devices, weather, and inadequate base capacity. Traffic incidents alone are a major cause of delay. This report presents the results of a project that developed a training program for traffic incident responders and managers. The training program described in this report contains two components: training of trainers and incident responder training. The course material provides extensive training on the core competencies for interdisciplinary traffic incident response. It is designed to help responders understand and implement the national unified goal for traffic incident management: responder safety; safe, quick clearance; and prompt, reliable, and interoperable communications. The training methods include a variety of adult-learning techniques, including interactive seminar, case study analysis, tabletop role-play and scenario, and field practicum. The training was developed for delivery through a 2-day intensive format or a modular (single lesson per session) format. The train-the-trainer curriculum is designed to facilitate cost-effective cultivation of qualified trainers across the country. Core multidisciplinary competencies were identified with input from a group of experts in traffic incident management. These competencies provided a framework from which the curriculum was built and design documents created. After development of the course materials, formative evaluation was conducted by holding two pilot training sessions, and the input from participants was incorporated into the final materials. This report is intended to help transportation agencies move forward in addressing nonrecurring traffic congestion and delivering more reliable travel times on their highway networks.

Study on the network acoustics environment effects of traffic management measures by a bilevel programming model

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.