Data from:Evaluating Mobility Impacts of Construction Work Zones on Utah Transportation System Using Machine Learning Techniques

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

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.

Inferring heterogeneous treatment effects of work zones on crashes

In order to evaluate the driving risk of the highway construction and maintenance work zone, identify the dangerous sections and improve the level of road traffic safety, considering the traffic conditions, speed and deceleration were used to evaluate the driving risk by cloud transformation and cloud reasoning technology. Based on the traffic flow theory, the traffic volume, speed and deceleration were chosen as the three evaluation indexes, and determine the certainty degree of evaluation index in the concept of cloud model, and the rule base of driving risk evaluation in highway was established and the risk could be evaluated by this method. The research results quantify the impact of traffic volume, speed and acceleration on the driving risk at the highway construction and maintenance work zone, which provides a good reference value for the future road safety design.

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.

ABSTRACTWork zones for road maintenance in traffic networks can significantly impact the traffic distribution and route choice behaviour of travellers. This study proposes an approach to evaluate and predict the broad‐scale effects of work zones on large‐scale traffic networks. For the requirement of the efficient evaluation of the various impacts of work zones on traffic networks, this study defines the road maintenance sensitivity factor (RMSF) to represent the joint impact of work zones. A simulation‐based optimization method for calibrating the RMSF is formulated. The original objective function is replaced by an analytical metamodel that builds the approximate relationship between the RMSFs and traffic flow distribution with the effect of work zones. A derivative‐free trust‐region algorithm is used to obtain the optimal solution. Numerical experiments are conducted on a small test network and a large‐scale freeway network. The proposed method shows the accuracy and effectiveness with tight computational resources than the simultaneous perturbation stochastic approximation method in both experiments, giving the RMSF results and map the traffic redistribution of large‐scale networks with work zones accurately and efficiently, which can help traffic managers to optimize maintenance plans and traffic management measures with the assistance of the traffic management system.

The higher contribution of traffic delay to environmental impacts is urging the highway agencies to take work zone management into the maintenance schemes decision-making. Aiming to understand the role of work zone management in user CO2 emissions reduction, this paper firstly developed a practical methodological framework of traffic delay-related CO2 emissions caused by highway maintenance based on a popular life cycle user cost analysis approach in regard of the microscopic vehicle operation analysis. The method was applied in an actual freeway flexible pavement with 15-year design life in Shaanxi Province, China, covering three types of preventive maintenance, correction maintenance, and rehabilitation. In addition, the impacts of key inputs of proposed method on work zone user CO2 emissions results were checked. The results show that traffic delay attributes to 29.4% of total CO2 emissions of the life cycle of highway pavement maintenance, and 51.8% of work zone user CO2 emissions result from preventive maintenance, especially from micro vehicle operations including speed change and queue near work zone (62% of total work zone user CO2 emissions). The work zone management alternative strategies related to less traffic volume or higher highway capacity including vehicle type limitation and the limited work zone speed have an advantage in reducing the work zone CO2 emissions over changing work zone length or work zone timing. The findings in this paper may present a useful tool and reference for robustly supporting the decision-making on highway maintenance carbon mitigation in work zone traffic.

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.

Highway work zone safety has been a concern nationwide and will likely draw ever increasing attention as more highway funds are invested in the maintenance of existing highways. To improve work zone safety, the Indiana Department of Transportation (DOT) established a special fund for work zone patrolling, and this study was commissioned to help the Indiana DOT achieve the maximum safety benefits within its budget constraint. With help from the Indiana DOT, a survey of project engineers was conducted to collect work zone information. The findings from the survey were linked with other available data. A random-effect negative binomial model was developed to identify the contributing factors and to estimate crash frequency in highway work zones. The results from the model provided insight for better understanding of crashes in work zones. Various factors, including roadway information, traffic volume, work zone–specific features, and police presence, were identified as things that affect crash frequency in work zones. These and other findings will be used to plan police enforcement activities in future Indiana DOT work zones.

Work zones pose mobility issues to the traveling public and safety challenges to travelers and road maintenance workers. These safety and mobility issues may be exacerbated by the presence of heavy vehicles. Connected and automated vehicle (CAV) technologies have been identified as a potential solution for these issues. This paper investigates the operational impacts of connected and automated heavy vehicles (CAHV) on freeway work zone operations on interstate highways. A microsimulation model, calibrated to empirical work zone field data, was used to study the operational impacts of CAHV platoons under various work zone and traffic conditions. It was found that, as the CAHV market penetration rate increases, the average work zone delay and queue length decreases. In addition, as the demand and heavy vehicle percentage increases, so do the benefits of using CAHV technology. For example, it was found that, when all heavy vehicles are classified as CAHV, the average flow rate is approximately 67% higher, and the maximum queue size and average delay decrease by approximately 97%. The methodology used in this paper will help transportation agencies as they design work zones to accommodate heavy vehicles equipped with CAV technologies.

Construction sites or work zones create serious disruptions in the normal flow of traffic, resulting in major inconveniences for the traveling public. Furthermore, these work zones create safety hazards that require special consideration. Current legislation and programs, at both state and national levels, emphasize the need for a better understanding of work zone problems to address work zone safety. This reality—coupled with the temporary closure of more miles of highway every year for rehabilitation and maintenance—makes the analysis of safety at construction sites a serious matter. A summary of a comprehensive study associated with the development of a new practical approach to address highway safety in construction zones is presented. Because empirical models require sample sizes that are not attainable due to the intrinsic scarcity of construction zone accident data, the problem was studied from the point of view of risk analysis. Monte Carlo simulations were used to develop risk factors. These factors are meant to be included in the calculations of additional user costs for work zones, or simply applied as risk measurements, to optimize the length and duration of closures for highway reconstruction and rehabilitation projects. In this way, it will be possible to assess the danger of work zones to the traveling public and minimize adverse effect of work zones on highway safety.

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 Federal Highway Administration (FHWA) reported between 2016 and 2017, fatal crashes in work zones increased by 3%, while fatal crashes outside of work zones decreased by 1.5%. The FHWA also reported that work zones account for approximately 10% of the nation’s overall congestion and 24% of unexpected interstate delays. This paper reports on a study of 23 construction work zones that covered approximately 150 centerline miles of Indiana interstate roadway in the summer of 2019. Approximately 50% of all interstate crashes for the period of May to September 2019 occurred within or in an approach upstream or downstream of one of these work zones. Commercially available vehicle hard-braking event data is used for the study and geofenced to the work zone approaches and limits. This research examined 196,215 hard-braking events over a 2-month period in the summer of 2019 and 3132 crashes over the same 2-month period in 2018 and 2019 for the 23 interstate work zones. The study found there were approximately 1 crash/mile for every 147 hard-braking events in and around a construction site. The R2 was approximately 0.85. The paper concludes by recommending that hard-braking event data be used by agencies to quickly identify emerging work zone locations that show relatively large number of hard-braking events for further evaluation.

State departments of transportation (DOTs) develop traffic control plans (TCPs) as part of construction projects. TCPs communicate required traffic control measures to the construction team and help ensure safe travel through construction work zones and a safe environment for construction workers. TCP quality can vary from project to project as a result of many factors, including the process by which the TCP is designed, reviewed, implemented, and inspected; the features and layout of the TCP and roadway; and the communication of the TCP to the constructor. This paper presents a research study conducted to understand how to improve work zone safety performance by improving the TCP design and implementation processes, identifying the TCP and roadway attributes that made TCP implementation challenging, and identifying factors that affected TCP quality and consistency. A sample of 39 construction projects of varying types and sizes was studied with a mixed methods research approach that included a survey of 12 key personnel on each project, a quantitative evaluation of each project's TCP, analysis of project crash data, and an on-site evaluation of the work zone. TCP quality and consistency and worker and motorist safety were found to be higher on projects for which constructability reviews and design peer reviews had been conducted and on projects on which the contractor super intendent and TCP designer had more years of experience and had undergone specialized TCP training. The TCP features found to increase TCP complexity to the greatest extent were multiple traffic control stages, multilane closures, and dense temporary signage.

Improving construction work zone safety using technology: A systematic review of applicable technologies