Roadway Type Research Articles

Land use regression model to predict nitrogen dioxide in the greater Philadelphia area

Because of greater concern of nitrogen dioxide (NO2) pollution for urban residents, monitoring efforts are typically focused in urban areas. However, large populations in suburban areas near urban centers may also experience elevated NO2 levels. This study focuses on the application of a land use regression (LUR) model to predict NO2 pollution in the suburban and urban areas around the city of Philadelphia, Pennsylvania, USA. Air pollutant measurements used for the LUR was obtained from Ogawa™ passive samplers, which were deployed at several sites (n = 15) throughout the Greater Philadelphia area. Passive samplers were deployed for two weeks each during the fall, winter, and spring months between 2021 and 2022. Predictor data included various spatial buffers of land use, landcover, and traffic counts, as well as distances to various roadway types. The best LUR model based on regression statistics (F-statistic with 4 and 10 degrees of freedom = 5.111, p < 0.05) showed that mean NO2 concentrations increase within a 1000m buffer of annual average daily traffic. The model shows a patchwork of NO2 concentrations throughout the study area that could indicate regional and local emissions. The results highlight spatial variations of elevated concentrations influenced by a mix of land use, including roads, residential, and industrial areas.

Study on overburden rock structure characteristics and surrounding rock control technology of island working face

Based on the background of ZF2822 island working face in Xiagou Coal Mine, the characteristics of overburden fracture structure and surrounding rock control technology of island working face are explored by means of theoretical analysis, numerical simulation and field measurement. The results show that the ten morphological structures of the main roof fracture can be divided into four typical symmetrical structures and six asymmetric structures. According to the stress calculation formula of the key block B to the coal pillar, the stress characteristics of the 10 structures are classified and the risk classification is carried out. Four typical structures (a), (b), (c) and (d) were constructed to simulate the stress and strain characteristics of surrounding rock. The overlying strata subsidence displacement values of (a), (b), (c) and (d) at the roadway are 1.75 m, 1.5 m, 0.8 m and 0.6 m respectively, and the maximum stresses are 26.6 MPa, 19.5 MPa, 15.5 MPa and 10.0 MPa respectively, (a) > (b) > (c) > (d). Therefore, the surrounding rock control technology of gob-side roadway in island working face is put forward, the fracture structure of basic roof is determined and the reasonable applicable type of gob-side roadway is selected. The results after pressure relief show that the average daily microseismic energy of ZF2822 working face after pressure relief is 0.87 × 104 J, which is about 42.38% lower than that of April. The average stress is 1.13 MPa lower than that of the mining before the pressure relief area, and the decrease is 22.42%. It shows that the pressure relief has played a very good effect and provides a reference for the same type of mine.

Uncovering pedestrian midblock crash severity patterns using association rules mining

The current study investigated the contributing factors and temporal variation in pedestrian crashes at midblock, with a particular focus on the severity levels: fatal/severe, moderate injury, and minor/no injury. It used association rules mining to uncover patterns between crash-contributing factors. By generating, evaluating, and visualising association rules for each severity level within each cluster, significant findings were discovered. Significant associations are observed between fatal crashes on weekdays and factors such as alcohol or drug impairment and nighttime. Similarly, factors including one-way roadway type, summer, and 25 MPH posted speed limit have a strong association with moderate injury crashes during weekdays. On weekends, nighttime crashes with non-motorised vehicles have the strongest association with fatal/severe injury crashes. Moreover, the generated rules for nighttime pedestrian fatal/severe injury crashes highlighted physical or drug-impaired pedestrians as the predominant attribute. The findings can enhance pedestrian safety at midblocks through targeted interventions.

Tracking of microplastics distribution patterns and their characterisation in deposited road dust from Dhaka city, Bangladesh

The widespread presence of microplastics (MPs) in road dust has considerable concern regarding their potential risks to ecosystems and human health. Despite the massive production of plastic, the erudition of MPs distribution patterns in various sizes of deposited road dust is still limited around the globe. Thus, the aim of this research is to provide an unambiguous picture of MPs distributional pattern, identification, classification, quantification, and features from road dust in various zones and types of roadways in Dhaka, Bangladesh. This study examined MPs in road dust samples with particle sizes ranging from 300 to 150 μm (Group-A), 149-75 μm (Group-B), and <75 μm (Group-C). This work extracted MPs from road dust using 30 % H2O2, 1.6 g/cm3 ZnCl2, and 0.45 μm filter paper. A fluorescent microscope (Motic B410E, Germany), Motic Pictures, and 3.0 ML software were utilized to identify MPs visually. Additionally, FTIR and SEM were utilized to determine the chemical composition of MPs. Group-A Road dust samples had a significantly higher concentration of MPs (38945 items/kg) compared to Group-B and <75 μm Group-C dust (16720 and 5945 items/kg, respectively). The distribution hierarchy for total MPs on average by location and type of road is as follows: paved road (355 items/5 g) > unpaved road (325 items/5 g) > soil samples (294 items/5 g), roadside dust samples (284 items/5 g), and mid-road (283 items/5 g). By taking into account all sizes of road dust samples, the MPs were classified as fiber (70.26 %), fragment (26.12 %), beads (0.66 %), films (1.32 %), and foams (1.58 %). It was found that adults inhaled MPs an average of 1612 items/day, while they ingested an average of 880 items/day. Children had an inhalation rate of 1232 items/day and an ingestion rate of 10267 items/day, which was 4–17 times greater than in other countries. This study identified 22 MPs polymer types, and SEM results show that MPs surfaces are being weathered into nanoplastics, creating a more hazardous environment.

Examining Factors Contributing to Motorcycle Collisions with Left-Turning Vehicles at Urban Intersection Locations

Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note – in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists’ susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.

When Do Users Prefer Voice Control Systems in Vehicles? A Survey of Chinese Drivers

Voice control systems (VCSs) are becoming increasingly common in modern vehicles, but concerns are still expressed by drivers about adopting them in smart cockpits. Previous research on privacy and recognition accuracy may not fully address users’ needs with regard to VCSs. In addition, studies on the safety impacts of different interaction modalities may not reveal drivers’ preference for interaction modalities, given that driver choice may not be solely based on interaction safety. Thus, to better understand and optimize VCSs design, an online survey was conducted in China to investigate drivers’ choices between VCSs and manual interaction. In total, we analyzed 168 drivers’ preferred interaction modality (i.e., either VCSs or manual interaction) in different scenarios defined by passenger presence (i.e., present versus absent), traffic complexity (i.e., low versus high traffic density), roadway type (i.e., urban road versus highway), and task characteristics (i.e., six in-vehicle interaction tasks). Compared with manual interactions, drivers preferred VCSs when driving alone, driving on highways, and driving in complex traffic. Further, drivers preferred VCSs when in-vehicle tasks were more distracting and time-demanding (e.g., compiling a text message). The preference for interaction modalities could also be affected by the characteristics of drivers: those with lower driving frequencies and higher VCSs familiarity and those who perceived higher VCSs usability tended to choose VCSs as an interaction modality for in-vehicle interaction tasks. These findings offer insights for adaptive interface design and future optimization of VCSs.

Autonomous prediction of rock deformation in fault zones of coal roadways using supervised machine learning

Coal roadway fault zones typically rely on multiple re-excavations and repeated reinforcements for control, posing challenges in achieving precise control of the initial support strength of the roadway. Therefore, predicting the deformation of the surrounding rock during the stability phase of fault zone excavation is a prerequisite for the precise control of the initial support strength. This study employs supervised learning algorithms to extract 14 sets of features as input variables for deformation prediction, focusing on the displacement of the roof and ribs in coal roadway fault zones. By constructing a dataset using field data, the reliability of the four different algorithms for predicting the displacement of the roof and rib in fault zones of roadways is validated. This predicts the deformation in the fault zone surrounding the rocks and ultimately categorises the failure types of the fault zone roadways into four classes. The results indicate that the gradient boosting decision tree method exhibits the highest reliability in predicting the roof and rib displacements, with the maximum prediction and absolute mean errors of the surrounding rock deformation controlled within 30.3 mm. This provides a guiding path for the precise control of the surrounding rock in the early excavation stage of fault-zone roadways.

Mine wind speed sensor location selection based on decision tree theory

Abstract In order to realize the scientific layout of underground roadway sensors, based on the information entropy theory, the decision tree algorithm is applied to the location selection of the underground wind speed sensor. Taking the air volume traversing the entire network branch as the goal, the node air volume balance and the characteristic relationship between air volume and wind pressure as the constraints, the conventional layout data of the mine wind speed sensor is selected as the training sample, and the reasonable decision node is selected by measuring the uncertainty of the characteristic attribute. The research shows that the smaller the information entropy of the layout elements, the greater the weight in determining the sensor location. The classification conditions obtained by the wind speed sensor are, in descending, order: airflow disturbance, roadway support, distance from the inlet (return) and outlet, and roadway type. Algorithms are applied to effectively combine downhole sensor siting with historical data containing unambiguous results.

Examination of the Factors Influencing the Electric Vehicle Accident Size in Norway (2020–2021)

With the great increase of electric vehicles (EVs) in the past decade, EV-involved traffic accidents have also been increasing quickly in many countries, bringing many new traffic safety challenges. Norway has the largest EV penetration rate in the world. Using the EV accident data from Norway in 2020 and 2021, this study aims to investigate the features of EV safety comprehensively. Firstly, a descriptive analysis is conducted. It has been found that rear-end collisions are the major collision type of EVs, and EVs are very likely to collide with pedestrians/cyclists. In addition, in terms of roadway type, EV accidents mainly occur on medium- and low-speed roads; in terms of environment, they mainly occur in good visibility conditions and dry road surface conditions. Then, a regression analysis is conducted to identify the key factors affecting the accident size, which is the number of traffic units involved in an accident and taken as the accident severity surrogate here. Since EV accidents are divided into four categories in order of accident size, the ordered logit model is adopted. It divides a multi-categorical dependent variable into multiple binary data points in order and calculates the probability of the dependent variable falling into each category with the logit model, respectively. The estimation results indicate that time of day, speed limit, and presence of medians have statistically significant impacts on the EV accident size. Finally, some countermeasures to prevent EV accidents are proposed based on the research results.

Roadway Weather Challenges Illuminate Real‐World Driving Biomarkers of Dementia Risk

AbstractBackgroundCognitive decline is a leading early predictor of dementia risk and may be evident in real‐world (RW) digital data even before patients ever present to clinic. This study examined the feasibility of using video and digital sensor profiles of real world (RW) driving in adverse weather as a marker of dementia risk.Method70 older drivers (mean age = 75.6 years) without dementia participated in a RW driving study for 2, 3‐month periods, one year apart (Wang, et al. 2021) . Self‐reported demographics and neuropsychological tests relevant to dementia and driving were collected at study start. Drivers were classed as cognitively neurotypical (N = 33) or impaired (N = 44) based on normed neuropsychological scores. Sensors installed in each driver’s vehicle collected video, speed, and GPS data across 405,104 miles driven. GIS data categorized roadway type (residential, commercial, interstate). A computer vision model (99% accuracy) we developed classed roads as clear or poor (rain, snow). Bayesian mixed‐effect logistic regressions assessed driver speed limit compliance (vehicle speed‐posted speed limit) across driver class, roadway, and weather.ResultDrivers were 8.01 times more likely to speed on interstates (95% CI: 7.61‐8.61) than residential roads (15‐25 mph). Impaired drivers were 15% more likely to drive slower than neurotypical drivers on clear roadways (95% C: 1.0413‐1.2829). This pattern reversed in poor weather with impaired drivers driving faster than neurotypical (b = 0.74, 95% CI: 0.57‐0.94)ConclusionDigital biomarkers from RW data show promise to screen for early dementia risk, advancing research and innovative clinical trials to prevent dementia. Driving challenges, like poor weather, discriminate cognitive decline before dementia. Impaired drivers showed self‐restriction by driving slower on clear roads and reduced self‐restriction by driving faster in poor weather despite risk. Big data strategies, like computer vision, complement driver performance data to identify RW environmental risk, paving the road for the car as a medical diagnostic device.

Network-Scale Analysis of Sea-Level Rise Impact on Flexible Pavements

This study investigates the potential damage to flexible pavements caused by rising groundwater tables resulting from sea-level rise. A case study was conducted in Miami-Dade County, Southeast Florida, a low-lying area at high risk of inundation and rising groundwater table due to sea-level rise. Flexible pavement specifications are differentiated using functional classification, and the reduced service life for various roadway types due to rising groundwater tables is predicted. The study utilized regional groundwater table maps for future sea-level rise scenarios to identify the saturated unbound layers for each roadway. An improved multilayer linear elastic model incorporating an unsaturated modulus resilient module, capable to handle saturated subgrade to base layer, is employed to quantify pavement response for each classified road at a network scale. The results indicate that the groundwater table response due to sea-level rise will extend further inland, impacting coastal infrastructure and inland areas. This study contributes to a network-scale deterministic pavement model tailored specifically for assessing the impact of sea-level rise on pavement performance. Given the increasing threats posed by sea-level rise, flooding, and infrastructure vulnerability, a comprehensive tool is provided for planners, pavement engineers, and policymakers.

A comparison of patterns and contributing factors of ADAS and ADS involved crashes

Crashes involving Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been increasing in recent years. Understanding the characteristics of these crashes can guide the optimization of driving automation systems and the policies improving the safety of mixed traffic. However, due to the limited available data, the crashes of ADS- and ADAS-controlled vehicles are still under-investigated. Thus, utilizing the latest National Highway Traffic Safety Administration crash reports, our study explores the patterns and contributing factors of ADAS- and ADS-involved crashes. The sequences of events leading to crashes were extracted from the reports and then categorized into five clusters. Next, for incomplete records, a non-parametric imputation method was applied based on Random Forest. Finally, logistic regression models were built to explore the factors associated with the crashes. The results show that the automation level, speed limit, and vehicle speed are predictors of crash patterns. At the same time, the crash pattern, combined with incident time, roadway type, roadway surface, and vehicle model year are associated with crash outcomes (i.e. contact area and injury severity). The results indicate that further improvement of the ADS/ADAS control algorithms and driver education may be needed to improve the safety of mixed traffic.

Analysing the Energy Consumption of Urban Electric Vehicles for Real Driving Scenarios

Driving efficiently with an electric vehicle is a key requirement to reduce energy consumption. This article aims to develop a unique two-layer driving pattern recognition approach for the classification of roadway type along with traffic situation for a particular route. The main idea is to use this information to accurately predict the driving pattern and hence analyse the energy consumption of an electric vehicle driving along a specific route under real driving scenarios. The main novelty of the contribution to develop a classification technique based on representative drive cycles, which characterise a roadway type and traffic condition on the basis of certain features. The simulation results indicate a classification accuracy of 85.2–97.7% in driving pattern recognition resulting in prediction of energy consumption under real traffic and road conditions.

A Novel Way of Optimizing Headlight Distributions Based on Real-Life Traffic and Eye-Tracking Data Part 2: Analysis of Real-World Traffic Environments Data in Germany

In order to find optimized headlight distributions based on real traffic data, a three-step approach has been chosen. Since the complete investigations are too extensive to fit into one single publication, this paper is the second of three papers. Over the course of these papers, a novel way to optimize automotive light distributions based on real-life traffic and eye-tracking data is presented. Over all three papers, 119 test subjects participated in the studies, with over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances and other objects in traffic. In the first paper, an ideal headlight distribution for straight roads with no other road users was identified. The second paper aims to collect the data required to modify this idealized headlight distribution for use on real roads. The first step is to find the extent to which real roads differ from an ideal, straight road. To do this, the German traffic space was analyzed. A new test vehicle recorded video and GPS data over a selected route. The video data were then evaluated by a machine learning algorithm. Object recognition software was used to find different traffic participants and road signs. Camera calibrations were used to find the exact angles of these objects. Using publicly available road data combined with the recorded GPS data, the video data were split into different road categories, and traffic object distributions were calculated for urban roads, country roads and motorways. The resulting analyses provided representative distributions of vehicles and highway signs along different types of roadways and roadway geometries. The GPS data were also used to find the curvature distributions along the selected route. These data were then used to optimize segment sizes for an adaptive driving beam. Overall, increasing the number of segments above 100 did not have appreciable benefits. These data will also be used in the third paper, where along the same route, the gaze distribution of drivers was recorded and analyzed.

Understanding overdriving: Human factors considerations in heavy vehicle headlight visibility and stopping distance

Nighttime road fatalities are a significant safety concern in the U.S., especially those involving commercial vehicles. Often an explanation offered involves overdriving headlights, or driving at a speed that does not allow for safe response within the range of the headlights’ visibility. The scientific literature was reviewed to assess the real-world applicability of guidance commonly provided in Commercial Driver’s License (CDL) manuals that advises heavy vehicle drivers to drive at a speed that allows them to be able to stop within the range of the vehicle’s headlights. We conclude that this guidance provided by CDL does not account for the variety of roadway types, driver expectations, and travel speeds, nor a scientific understanding about visual perception and hazard avoidance. It is recommended that further work be conducted to examine real-world application of commercial vehicle driver capabilities and variable roadway types to better inform guidance and recommendations.

The Impact of Environmental Features on Drivers’ Situation Awareness Using Real-World Driving Scenarios

Advanced driver assistance systems (ADAS) need to account for the driver’s awareness of the environment to be effectively used. This study examines the impact of environmental features (eg, visual complexity, object density, roadway type, lighting) on drivers’ situation awareness (SA). This is achieved using a controlled study with 40 participants. Using a split-plot design, the participants were shown 30 out of 75 real-world driving scenarios displayed in a driving simulator environment. Participants’ responses to Situational Awareness Global Assessment Technique (SAGAT) queries on the type and coordinates of objects in the scene were used to calculate SA scores. A hurdle model was developed to estimate participants’ SA scores. The key findings highlight visual complexity as a significant predictor of SA scores. This predictor was easy to compute and able to capture the complexity of objects that impact road safety as well as the visual clutter in the background. The model showed that drivers were able to identify at least one object of interest in complex environments with high visual complexity and with many objects. A higher proportion of vulnerable road users was associated with a greater likelihood of a non-zero SA score, but the SA score was lower compared to environments with higher proportions of cars. The findings of this study provide insights into the environmental factors to be considered for SA predictive models.

Injury severity of single-vehicle large-truck crashes: accounting for heterogeneity

This research examines the injury severity of single-vehicle large-truck crashes in Florida while exploring the role of heterogeneity. A random parameter ordered logit (RPOL) model was applied to 27,505 single-vehicle large-truck crashes from 2007 to 2016 in Florida, and the contributing factors were identified. Random parameters and interaction effects were introduced to the model to determine the heterogeneity and its potential sources. The results suggested that driving speed of 76–120 mph and defective tires were the most influential factors in crash injury severity, increasing the probability of severe crashes. Regarding truckers’ attributes, asleep or fatigued conditions and driving under the influence were correlated with a higher possibility of severe crashes. Interestingly, the results showed that truckers from outside the state of Florida were less likely to cause severe single-vehicle large-truck crashes compared to their Floridian counterparts. Y-intersections were also found as a high-risk location for single-vehicle large-truck crashes, leading to more severe outcomes. Regarding heterogeneity, the results indicated that the impacts of driving speed (26–50 mph) and light condition (dark – not lighted) significantly varied among the observations, and these variations could be attributed to driver action, vision obstruction, driver distraction, roadway type and roadway alignment.

Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). This paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.

Exploring association of contributing factors to pedestrian fatal and severe injury crashes under dark-no-streetlight condition

The pedestrian crash pattern in the dark-no-streetlight condition is a noteworthy ongoing traffic safety concern. The persistently high percentage of pedestrian fatalities at night in the U.S., as well as in the state of Louisiana, necessitates new research to supplement existing studies. This study utilized 10 years (2010–2019) of pedestrian fatal and severe injury crashes in Louisiana that occurred in the dark without streetlights to identify the associated crash patterns. The methodology is based on Multiple Correspondence Analysis (MCA), an exploratory approach used to discover the association of multiple categorical variables from a crash dataset. The findings suggest that driver characteristics (age, gender, and physical condition), pedestrian action, pedestrian alcohol impairment, and physical settings (posted speed limit, location, and roadway type) have a substantial impact on pedestrian collisions at night without streetlights. Moreover, the obtained combination clouds of MCA reveal associations such as elderly pedestrian (>64 years) alcohol impairment resulting in fatalities, crashes in an open country location with a high posted speed limit, crashes involving pedestrians in dark clothing on high-speed (50–55 mph) roadways, alcohol-impaired driver involvement in crashes on two-way roads without physical separation, severe injury crashes at intersections, male pedestrian crashes on midblock locations during weekends, and young (15–24 years) female driver's involvement in crashes while pedestrians were walking against the traffic. Based on the findings, this research also suggests safety recommendations that can assist highway safety practitioners in determining appropriate countermeasures to reduce pedestrian crashes in the dark-no-streetlight condition.

Numerical study on fire behavior and temperature distribution in a blind roadway with different sealing situations.

Blind roadways have only one portal which connects with other types of mine roadways. Sealing the fire area in a blind roadway is an effective method of disaster relief in a mine. To understand the effect of sealing ratio and sealing distance on fire behavior, Fire Dynamics Simulator (FDS 6.6) was used to study blind roadway fires with different fire scenarios. Results indicate that the smoke flow velocity increases significantly with the increase of sealing distance. The fire in the blind roadway is ventilation-controlled. When the sealing ratio reaches 80%, the fire self-extinguishes completely. Otherwise, the fire experiences an extinguishing-reburning cycle periodically. Besides, an empirical model is proposed to predict the downstream temperature distribution beneath the ceiling in the region from fire source to sealing position. The predictions by the proposed model comply well with the simulation and experimental results from our and others' studies. This study provides new insights into the sealing strategies in blind roadway fires, and the outcomes of the current study are of guiding significance for the fire rescue in the blind roadways or similar structures.