Work Zone Research Articles

Improved Temporary Traffic Control Guidance for Mobile Operations on Two-Lane Roadways

The objective of this research was to develop guidance for improving safety at mobile operations on two-lane roadways. One of the most significant hazards to workers performing mobile operations is unpredictable driver behavior around the operation (including poor passing decisions). Researchers observed 1,209 passing maneuvers at 17 different mobile operations in Texas and Minnesota, U.S., to better understand driver behavior around mobile operations. An analysis of the passing maneuver data showed that 449 passing maneuvers were made by drivers who were not first in the queue of vehicles behind the mobile operation. These passing maneuvers were considered undesirable because they indicate some degree of driver impatience. Researchers analyzed the amount of time each driver spent in the queue and found that nearly 50% of drivers who waited in the queue for 4 min or longer attempted an undesirable passing maneuver. This increased to nearly 85% when wait times exceed 6 min. As a result of the field studies, the researchers recommended that transportation agencies strive to avoid creating situations where drivers in queue must follow a mobile operation for several minutes without opportunities to pass. When these delays cannot be avoided, the work crew may need to pull over and allow vehicles to pass. If there are no suitable pullover locations, a stationary work zone with positive traffic control methods (e.g., flaggers, portable signals) should be considered.

Using Deep Neural Networks and Big Data to Predict Microscopic Travel Time in Work Zones

Traffic management in work zones is a challenging task that requires a balance between creating a safe environment for workers and minimizing traffic delays. Accurately predicting travel times through work zones is essential for dynamic traffic management and reducing congestion. However, complex interrupted traffic patterns in work zones (also known as construction zones) make this challenging compared with regular traffic congestion and free-flow conditions. To address this complexity, the paper develops a data-driven deep feed-forward artificial neural network to forecast under-construction travel times in work zones using an integrated data set of almost half a million observations. The variables considered in the neural network include the work zone characteristics, road design features, weather information, and traffic flow information. The raw data set comprised approximately 15 million travel time observations collected at 674 work zones. After cleaning and preprocessing the data and applying feature engineering techniques, the raw data set was reduced to 81 work zones spread along a 700 km corridor between the western borders of Alberta and Vancouver, British Columbia. The data were split into training and test data using a 90:10 ratio. Training data were used to develop a 27-input neural network with four hidden layers. After validating the test data, the neural network achieved a root mean squared error of 0.150 min and an R-squared score of 0.945, indicating a high accuracy level in estimating the under-construction travel time.

Optimization of Sustainable Production Processes in C45 Steel Machining Using a Confocal Chromatic Sensor

Metal machining production faces a myriad of demands encompassing ecology, automation, product control, and cost reduction. Within this framework, an exploration into employing a direct inspection of the machined area within the work zone of a given machine through a confocal chromatic sensor was undertaken. In the turning process, parameters including cutting speed (A), feed (B), depth of cut (C), workpiece length from clamping (D), and cutting edge radius (E) were designated as input variables. Roundness deviation (Rd) and tool face wear (KM) parameters were identified as output factors for assessing process performance. The experimental phase adhered to the Taguchi Orthogonal Array L27. Confirmatory tests revealed that optimizing process parameters according to the Taguchi method could enhance the turning performance of C45 steel. ANOVA results underscored the significant impact of cutting speed (A), feed (B), depth of cut (C), and workpiece length from clamping (D) on turning performance concerning Rd and KM. Furthermore, initial regression models were formulated to forecast roundness variation and tool face wear. The proposed parameters were found to not only influence the machined surface but also affect confocal sensor measurements. Consequently, we advocate for the adoption of these optimal cutting conditions in product production to bolster turning performance when machining C45 steel.

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Uncovering Critical Causes of Highway Work Zone Accidents Using Unsupervised Machine Learning and Social Network Analysis

Uncovering Critical Causes of Highway Work Zone Accidents Using Unsupervised Machine Learning and Social Network Analysis

EXPERIMENTAL STUDY OF THE INTENSITY OF COAL DUST REMOVAL

Problem statement. Industrial sites where coal storages are located are intensive sources of dust pollution of the environment. There is an important problem of assessing the intensity of dust removal into the atmospheric air from polluted areas. Knowledge of the intensity of dust removal into the atmospheric air makes it possible to scientifically assess the impact of contaminated sites on the pollution of the environment and work zones at industrial sites. The solution to this problem can be obtained experimentally. The purpose of the article. An experimental study of the value of the air flow velocity at which the detachment of dust particles from the surface with coal begins and their removal into the air and the determination of the intensity of the emission of coal dust from the contaminated surface. Methodology. The intensity of removal of coal dust from the contaminated area was studied experimentally in laboratory conditions. The research was conducted on coal samples from DTEK “Pavlohradvuhillya”, grade “ДГ. During the research, the velocity of the air flow at which the process of movement of dust particles along the emission source began and the velocity of “detachment” of dust particles and their removal from the emission source were determined. At the second stage of experimental research, the intensity of removal of coal dust from the polluted area was determined. Scientific novelty. The values of the air velocity at which the removal of coal dust particles from the contaminated area begins were determined experimentally. The regularity of the intensity of the removal of coal dust depending on the velocity of the air flow over the contaminated area was obtained. Practical significance. The obtained experimental data make it possible to determine under which weather conditions there is a risk of dust formation and the removal of dust into the atmosphere. The empirical dependence obtained by processing experimental data can be used for a scientifically based assessment of the level of pollution of working areas at industrial sites where there are coal storage facilities. Conclusions. The value of the velocity of the air flow at which the movement of dust particles on the contaminated surface begins, as well as the value of the velocity of the air flow at which the removal of dust particles into the air begins, was determined experimentally. The resulting empirical model can be used to estimate environmental damage due to dust pollution of atmospheric air.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes

Objective Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. Method This study employed random parameters logit models, accommodating potential variations in parameter estimates’ means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. Results This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories – geometry and non-geometry factors. Conclusion These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Ultrafine industrial aerosol as a risk factor for the health of smelting shop workers at a machine-building enterprise

Aim. To evaluate the amount and physicochemical properties of ultrafine industrial aerosol (UIA) in the work zone air (WZA) of smelting shop workers at a machine-building enterprise during various technological processes (melting, welding, and metal machining). Materials and methods. The physical properties of UIA particles were evaluated with NanoScan 3910 scanning spectrometer: the number of particles (number/cm3), particle surface volume (nm3/cm3), particle surface area (nm2/cm2), and nanoparticle mass concentration (μg/cm3) in the WZA of a furnace operator (n = 416), a welder (n = 315), a cutter (n = 286), a grinder (n = 78), and workers of the control group (n = 315). The chemical composition of the air samples was determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using an Optima 2100 DV device. Results. It has been found that the highest concentration of UIA nanoparticles was recorded during metal melting at the workplaces (WP) of furnace operators (4.28 × 104 to 2.41 × 105 particles/cm3) and welders (3.01 × 104 to 3.34 × 105 particles/cm3). During mechanical metal processing, a much smaller number of nanoparticles was produced (for grinders, the number varied from 9.81 × 104 to 1.44 × 105 particles/cm3; for cutters, it varied from 2.71 × 104 to 1.94 × 105 particles/cm3). Indicators of surface area, surface volume and mass concentration at the WPs of furnace operators, welders, grinders and cutters exceeded the corresponding indicators of the control group for almost all sizes of suspended particles with statistically significant differences (p ≤ 0.05). It has been estimated that such metals as Al, Cu, Mg, Mo, Fe, and Ni were present in the workers’ WZA but their content did not exceed the current maximum permissible concentrations. Conclusions. The presence of suspended particles of UIA with a maximum concentration in the range from 20 nm to 70 nm has been confirmed in the WZA during melting, welding of metals and machining. Indicators of concentration, surface area, surface volume, and mass concentration at the workplaces of melting shop workers at the machine-building enterprise significantly exceeded the corresponding indicators in the control group without dust formation processes, showing statistically significant differences (p ≤ 0.05). The evidence of metals in the WZA has suggested their presence in the form of nanoparticles, which are more active and dangerous, thus increasing the risk of their adverse effects on the workers. Ультрадисперсний

Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest

The escalating congestion impacts of short-term traffic disruptions, such as double parking or short-duration work zones, are gaining increased attention. This study introduces an enhanced isolation forest-based unsupervised anomaly detection algorithm to detect short-term traffic disruptions in urban areas. To enable the detection in real-time, a sliding window approach is introduced to allow the streaming data to be read and processed according to the window size. An adaptive threshold that automatically adjusts itself to overcome the limitations of the miss rate on local anomalies is shown to further enhance the model predictions. The proposed algorithm is empirically validated on four study sites in Manhattan, New York City. The experimental results demonstrate that the proposed unsupervised algorithm can effectively detect different types of traffic anomalies including accidents, work zones/road closures, traffic jams, double parking events and police activities. On all sites, the average detection rate is 81.1% for traffic jams and 89.6% for police activities, respectively. For three out of the four sites, the detection rate ranges from 71.4% to 100% for accidents, work zones and double parking. An optimizer using high-pass filter is also presented to further improve off-line detection. The primary advantages of this proposed computationally-efficient method are that its only required data input is travel time information and that it does not need labeled data for training, which make it highly deployable for real-time operational applications and can be easily adopted by other cities.

Dynamic Risk Assessment and Control Framework for Work Zone and Its First Implementation under Simulation Environment

The number of road maintenance operations in China is increasing. Work zones can interfere with everyday driving and bring significant safety hazards to the throughput vehicles and construction personnel. However, there is currently a lack of effective methods to conduct a dynamic risk assessment of work zones and provide control guidance when necessary. Therefore, this study proposes a dynamic risk assessment and control framework for the work zones. The framework adopts a closed-loop control and dynamic monitoring architecture. A massive amount of microscopic data on vehicles’ behavior is obtained based on advanced data collection technologies (including multicamera multiobject tracking, naturalistic driving, and microscopic traffic simulation). Traffic conflicts between single vehicle and two- or multivehicle are detected using vehicle behavior analyzing technology and the surrogate safety assessment model. A comprehensive assessment index, i.e., UTECN (unit total equivalent conflict number), is established that can consider the severity and possibility of accidents caused by conflicts based on the vehicle collision energy theory, probability theory, and risk management theory. A risk assessment standard is established based on the level of safety service. The framework is first implemented in a simulation environment, and its feasibility and effectiveness are verified by taking a work zone of Shanghai Waihuan Expressway S20 as a case study. The result shows that the framework has good practicality. Under a moderate traffic level, the risk of the work zone can be assessed within 20 minutes. It can be quickly and effectively controlled at an acceptable level after several iterations, which is of great significance for ensuring the safety of life and property of throughput vehicles and construction personnel within the work zone.

Integrated optimization of dedicated engine and energy management strategy for the plug-in hybrid commercial vehicle at high altitude

To improve fuel efficiency and reduce pollutant emissions of plug-in hybrid commercial vehicles (PHCVs) at high altitude, an integrated optimization of dedicated engine of PHCV (DEP) and energy management strategy (EMS) is performed. Firstly, the physical model of DEP is established using experimental data. Its empirical model is developed through machine learning with samples generated by the physical model. Subsequently, a hybrid model-based successive approximation method is proposed to optimize various combinations of control parameters including fuel injection timing, rail pressure, VGT opening and EGR rate, aiming at reducing brake specific fuel consumption (BSFC) and NOx within the major working zone of DEP. This optimized DEP is then integrated into the PHCV model to collectively optimize EMS at this challenging altitude. Results demonstrate that this method considerably reduces optimization time without compromising model accuracy. Notably, at 4000 m altitude and under the CHTC-C driving cycle, overall BSFC reduces with a minimum of 194.27 g/(kW·h), most brake specific NOx values remain under 7.5 g/(kW·h), and optimal working range broadens. With integrated optimization, the DEP predominantly works in low-fuel consumption and low-emission zone. Consequently, the fuel economy of PHCV improves by 30.03 %, while NOx and CO2 emissions decrease by 35.88 % and 18.05 %, respectively.

Changing Regularity of the Interaction Effects of Multi-Scale Factors on Drivers’ Merging Behaviors in the Highway Work Zone

This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.

Framework for assessing bridges construction impact on work zone traffic using BrIM

This research presents a framework for visualizing bridge construction impact on work zone traffic using Bridge Information Modelling (BrIM) and Google Maps API. The framework consists of four modules. The first module contains the construction data of the bridge by reporting the construction's daily progress. The second module is designated to model traffic data of the work zone using Google Maps API, traffic Survey counting, and Google Earth Images. The third module performs a traffic simulation for the work zone, and calculating the user cost of different stages. The fourth module visualizes the bridge's construction using Bridge Information Modelling (BrIM) and virtual reality in the Twinmotion engine to demonstrate the construction stages and the corresponding traffic states. An actual case study of El-Nahas Bridge in Cairo city-Egypt is presented to demonstrate the main features of the developed framework and its practical aspects. The case study results reveal that the impact of the construction stages varies on work zone traffic throughout the project period. The impact greatly influences the traffic status at the beginning of the project associated with high user costs. Then, the impact declines in later stages depending on the type of construction activities in each stage.

Mixed-method usability investigation of ARROWS: augmented reality for roadway work zone safety

This article explores the usability and user experience challenges of ARROWS, a novel augmented reality (AR) and wearable technology (WT) safety system for roadway work zones, an area with limited existing usability research. We utilized a mixed-method approach with two complementary experiments in indoor and outdoor settings, using the Wizard of Oz methodology and a high-fidelity prototype. We focused on identifying usability challenges, factors contributing to user experience and the distinct needs of highway workers, documenting results using the system usability scale (SUS), the rating scale mental effort (RSME) and a trust score. Participants rated the usability of ARROWS above average in both settings, while making a reasonable level of mental effort. The findings also indicate a significant correlation between perceived trust and usability, highlighting the importance of trust in user experience.

Impact Mitigation of Tunnels Maintenance Works on Highway Performance

Major maintenance interventions on highways (e.g., for the safety improvement of tunnels and viaducts) involve the presence of work zones for extended periods of time and along wide sections of the infrastructure. These interventions lead to supply changes, especially a reduction in capacity, causing a decrease in the performance of the transportation system, which users experience with increased traffic congestion and economic losses in terms of productivity. This paper aims to demonstrate that proper traffic management and work zone planning allow for a significant reduction of such impacts. In relation to traffic management, possible solutions consist of diverting traffic flows onto alternative routes (if possible, using primary roads) or keeping them in the remaining lanes along the work zone. In terms of work zone planning, leverage can be applied, on the one hand, through the design of the work zone layout and maintenance scheme and, on the other, through the duration and scheduling of the interventions. The methodology proposed in this paper is based on traffic simulation models in order to assess different maintenance scenarios under varying demand levels (i.e., during the rush hour and off-peak hours, weekdays and weekends, summer and winter months) and vehicular compositions (i.e., percentage of heavy vehicles), with the goal of identifying the scenario that minimizes the impacts on users. Modelling evidence on a real case study of tunnel maintenance interventions on an Italian highway is reported, discussing the policy implications on the basis of quantitative indicators, such as level of service, duration of delays, length of queues, and economic impacts.

PID‐Based Freeway Work Zone Merge Control with Traffic State Prediction under Mixed Traffic Flow of Connected Automated Vehicles and Manual Vehicles

During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short‐term prediction model combined with a proportional‐integral‐derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real‐time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller’s decision to initiate a “Merge” or “No Merge” guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real‐world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas.

Evaluation of the Impact of Driver Behavior on Back of Queues Events in Work Zones Using the SHRP2 Naturalistic Driving Study Data

Evaluation of the Impact of Driver Behavior on Back of Queues Events in Work Zones Using the SHRP2 Naturalistic Driving Study Data

Towards Safer Highway Work Zones: Insights from Deep Learning Analysis of Thermal Footage

Thermal imaging, when coupled with deep-learning-based analysis, can significantly improve highway work zone management and safety, though data scarcity in this field has been a historical challenge. We collected over 440 hours of thermal footage from highway sites in Massachusetts and New Hampshire and created a vehicle segmentation dataset featuring over 14,000 vehicle instances. We implement effective training strategies for deep learning models and develop algorithms to analyze vehicle trajectories and merging behaviors. Various visualization techniques are illustrated to better understand the extensive analytics data generated for each site, which can lead to development of data-driven strategies to improve Traffic management and safety. GitHub repository: https://github.com/z00bean/SmartWorkZoneControl.

A Holistic Approach to Exploring the Root Factors of Work Zone Accidents

A Holistic Approach to Exploring the Root Factors of Work Zone Accidents

Development of Empirical Equation to Estimate Corridor Speed of Vehicles in Metro Construction Work Zone

Abstract Construction of elevated metro line leads to a reduction in the average speed of vehicles due to congestions, diversions, and road incidents. This reduction in speed depends on recurring (e.g., congestion in rush hours) and non-recurring (e.g., road accidents) delay factors. Non-recurring factors involve incidents such as accidental bus stops, pedestrian crossings, or clearing debris from road, etc., some of which may occur at very short notice. This research paper presents a case study of Pune Metro Rail Construction (Corridor II) in India for developing an empirical equation to estimate corridor speed of vehicles in metro construction work zone. Observations were recorded in spatial and temporal zones using floating car method through video recordings and traffic count using manual method. Based on the traffic behavior pattern, five non-recurring delay factors (NRDFs) were defined, namely vehicles in lateral entry, slow moving vehicles, vehicles in the wrong direction, on-street parked vehicles, pedestrian interference and their correlation with speed of vehicles was tested. It was concluded that these factors have a strong negative correlation with the speed of the test vehicle. To estimate corridor speed with given NRDFs, nonlinear equation was formulated using empirical evidence and validated using larger simulated data.