Traffic-related Variables Research Articles

A correlation study of road dust pollutants, tire wear particles, air quality, and traffic conditions in the Seoul (South Korea)

The interdependence of traffic dynamics and air quality on the concentrations of tire wear particles (TWP), heavy metals, and carbon black (CB) in road dust collected from 15 locations in Seoul, South Korea, was assessed. Pearson correlation, principal component analysis, and network analysis were employed to evaluate the correlations among traffic volume, vehicle speed, air quality parameters (PM10, PM2.5, NOx, and CO), and the concentrations of TWP (5934 to 16,253 mg/kg, average 9581 ± 4086 mg/kg), CB (371–22,287 mg/kg, average 4291 ± 6096 mg/kg), and heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, and Pb) in road dust. The enrichment factors for heavy metals and the pollution index of TWP and CB were also calculated to evaluate the contribution of vehicle-derived particulate substances to road dust contamination. It was found that Cluster B, characterized by traffic-related variables such as traffic volume, vehicle speed, and heavy metals (Zn, Cd, and Pb), was significantly correlated with these pollutants, with correlation coefficients reaching up to 0.933. TWP was identified as a significant mediator in the increase of Zn, Pb, and Cd concentrations linked to traffic activities, contributing to pollution levels that were 2–16 times higher than the geochemical background. The presence of TWP and CB in road dust was identified as an indicator of contamination from traffic-related activities, highlighting the importance of Zn, Pb, and Cd as emerging pollutants that require targeted management strategies.

Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk

Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m−3, a decrease of approximately 70% from its 2000 value of 0.44 ng m−3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m−3, which is the BaP concentration associated with an excess lifetime cancer risk of 10−5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.

Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 μm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 μm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100–150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Freeway crashes represent a significant and persistent threat to road safety, resulting in both loss of life and extensive property damage. Effectively addressing this critical issue requires a comprehensive understanding of the factors contributing to these incidents and the ability to accurately predict crash severity under different traffic conditions. This study aims to improve the accuracy of crash classification by incorporating key traffic-related variables such as braking, weather conditions, and speed. To validate the effectiveness of proposed model, we utilize real-world crash data from Flint, Michigan. To achieve the objective, we employ an innovative Boosting Ensemble Learning approach, leveraging five advanced ensemble learning models: Gradient Boosting, Cat Boost, XGBoost, LightGBM, and SGD. Through the application of hyperparameter optimization techniques, we further enhance the performance of these models, improving their predictive capabilities. Our evaluation results demonstrated the effectiveness of our approach, with Gradient Boosting algorithms achieving an accuracy rate of up to 96% in crash classification. This research provides valuable insights into the potential of using Boosting Ensemble Learning as a tool for accurately and efficiently classifying freeway crashes across a spectrum of traffic conditions. Additionally, it sheds light on the nuanced variations in crash mechanisms observed when employing diverse ensemble learning models. The findings of this study underscore the significance of hyperparameter optimization as a critical factor in elevating the predictive precision of freeway crashes.

Spatial modeling and source identification of PAHs in soils and roadside dusts from Hangzhou, a new first-tier megcity of China

Soil (n = 100) and roadside dust (n = 97) samples across a new first-tier megacity (Hangzhou, East China) were collected to systematically survey the spatial patterns and sources of 16 priority Polycyclic aromatic hydrocarbons (PAHs). The total PAH (Σ16PAH) concentrations in the surface soils exhibited an average of 329 ± 168 ng/g with a range of 67.7–2950 ng/g. 45 % of soils suffered from the weak - heavy PAH contaminations. By contrast, the average concentration of Σ16PAHs in roadside dusts was approximately three times higher than that found in the soils. Within both soil and dust samples, PAHs with a high molecular weight (HMW, 4–6 rings) were the dominant homologs. Spatial lag models revealed that industrial and traffic-related variables significantly influenced the spatial distribution of PAHs in dusts. Furthermore, a positive matrix factorization model indicated that Σ16PAHs in soils and dusts mainly derived from coal-related production/combustion and automobile exhaust emission (65.0 % and 77.9 %, respectively). The isotopic composition analysis conducted on heavily polluted samples further demonstrated that HMW PAHs in these soils and dusts predominantly originated from coal processing and liquid fossil fuel combustion. Overall, the findings of this study suggest that the intensive urban construction and human activities strongly stimulated PAH accumulations in soils and dusts during the rapid urbanization of Hangzhou.

Analysis of student pedestrian gap acceptance behaviour in suburban areas

The safety of student pedestrians on suburban roads is an issue that is mainly overlooked. Numerous studies have been carried out on pedestrian crossing safety in developing countries. However, a study to identify factors that affect the gap acceptance behaviour of student pedestrians on suburban roads is still lacking. Consequently, it is crucial to understand how factors influence pedestrians’ decisions. This motivated an investigation into student pedestrian crossing behaviour at a crosswalk near a university campus located on suburban roads in Babol city, Iran. A video recording of the case study was conducted to provide a database for the analysis. Following data extraction, a logistic regression method was used to examine gap acceptance behaviour based on influential variables. Results indicated that waiting for time, the mean speed of the flow, headway between vehicles, available gap size and the type and speed of the approaching vehicle were significant. In addition, the gender and age of pedestrians did not significantly affect gap acceptance behaviour. Generally, traffic-related variables were observed as significantly influencing gap selection behaviour.

Seat belt use among rural non-drivers: the role of demographic and traffic-related variables.

The rate of seat belt use in rural societies is less than in urban societies. The present study aimed to determine the effect of demographic and traffic-related variables on seat belt use among rural non-drivers based on the theory of planned behavior (TPB). This study was conducted among 450 non-drivers in the rural areas of Hashtroud district in Iran. For collection of data, a questionnaire containing questions about demographic characteristics and general information on traffic-related behaviors of non-drivers, and questions on seat belt use based on constructs of the TPB was used. The lowest seat belt use rate was for non-drivers that sit in the rear seat of a car on rural roads (22.4 % never, 14.4 % always). Also, the rate of seat belt use among parents of participants on rural roads was lower than on city roads. Adherence to traffic rules and having training about seat belt use had significant effects on the construct of TPB, including attitude, subjective norms, perceived behavioral control, behavioral intention, and behavior of seat belt use. With increasing age, subjective norms about seat belt use have improved. The attitude toward seat belt use among females was better than males. The result indicated that most of rural non-drivers did not adhere to traffic rules. Adherence to the traffic rules and having training on seat belt use had a significant impact on seat belt use behavior. Training seat belt use especially by parents could be effective in improving seat belt use.

Effects of traffic load amplitude sequence on the cracking performance of asphalt pavement with a semi-rigid base

ABSTRACT The propagation of cracks in in-service asphalt pavements is closely related to the complicated traffic loading patterns over time. However, typical traffic-related variables capture only the overall traffic level without being able to account for the load-time history. Therefore, this study aims to investigate the effects of traffic load sequence on the cracking performance of asphalt pavement from both field and laboratory perspectives. A load amplitude sequence (LAS) index was developed to characterize the traffic loading sequence in the field. Two machine learning (ML) algorithms, namely artificial neural network (ANN) and random forest regression (RFR), were applied to correlate the LAS index with field pavement cracking performance. The two-block semi-circular bending (SCB) test was developed to characterize the non-linear fatigue damage accumulation of asphalt mixtures. It was found that heavier traffic loads in early stages are detrimental to the long-term pavement cracking performance. The LAS index plays a crucial role in the prediction and development of pavement cracks. The laboratory test results reveal that a loading sequence starting with a higher stress may shorten the fatigue life and vice versa. The outcomes of this study may contribute to a better understanding of the traffic loading characterization of in-service asphalt pavements.

Characteristics and determinants of personal exposure to typical air pollutants: A pilot study in Beijing and Baoding, China

Personal exposure to fine particulate matter (PM2.5), nitrogen oxides (NOx, NO2 and NO), ozone (O3) and sulfur dioxide (SO2) was repeatedly measured among fourteen office workers in Beijing and Baoding, China in summer, autumn and winter of 2019. Time–activity patterns were simultaneously recorded. Determinants of personal air pollution exposure were investigated for each pollutant via a linear mixed effect model. The personal concentrations of PM2.5, NO2, NO and O3 were higher in autumn and winter than those in summer. A decreasing trend was found in the personal PM2.5 level for a typical indoor population in Beijing, indicating that particulate pollution was effectively controlled in Beijing and its surrounding area. The personal levels of PM2.5, NO2, and O3 were weakly correlated with those monitored at ambient stations and were lower than the respective ambient levels except for PM2.5 in summer and NO2 in winter. This pilot study showed that the indoor air environment, ambient pollution, traffic-related variables and temperature were significant exposure sources for office workers. Our study highlighted the significance of controlling traffic emissions and improving the workplace air quality to protect the health of office workers. More importantly, we demonstrated the feasibility of model development for personal air pollution exposure prediction.

Evaluation of geostatistical and multiple regression models for assessment of spatial characteristics of carbon monoxide concentration in a data-limited environment

Efforts to manage air pollution problems caused by motorized transport in developing countries have frequently failed due to a lack of air quality data, both quantitatively and qualitatively. Air quality data from sparse monitoring stations is typically insufficient for spatial modelling, which is required for understanding and managing air quality conditions. This study aimed at assessing the empirical Bayesian kriging regression prediction (EBKRP) model's ability to predict the spatial concentration of CO in the study area using data from moderately sparse monitoring stations. Multiple Linear Regression (MLR) and Analysis of Variance (ANOVA) statistics were used to model the relationship between meteorological and traffic-related variables and CO concentration, as well as the difference in CO concentrations between the different traffic scenarios (morning, afternoon and evening traffic periods). Data on CO concentration and CO predictor variables were collected simultaneously, every day, at 2 m and 5 m above ground level (agl), during each traffic scenario, at 21 monitoring stations from January to March 2022. The data was aggregated based on height agl and traffic scenarios. ArcGIS Pro 3.0 was used to implement the EBKRP model and MLR statistics, and SPSS was used to run the ANOVA. To assess the accuracy of the EBKRP model, various error metrics (mean error, mean standard error, root mean square error, and Pearson correlation R) were calculated. The results show that EBKRP performed optimally, with the root mean square error (RMSE) of 0.51 ppm and 0.23 ppm recorded for the predicted CO surfaces at the 2m and 5m agl, respectively. CO concentration were found to have a significant relationship with traffic volume, relative humidity, temperature, and wind speed, with statistical coefficients of 0.4, 0.15, −0.16, and −2.45; and 0.02, 0.21, −0.39, and −3.23 at 2m and 5m agl, respectively. The CO concentration in the study area differed significantly (p < 0.05) between the traffic periods, and the CO-predictor variables explained approximately 93% and 91% of the CO concentration at 2 m and 5 m, respectively. CO concentrations were higher at 2 m than at 5 m and increased from west to east. Overall, concentrations of CO in the study area were found to be within regulatory permissible limits. The current study lays the groundwork for using the EBKRP model in a data-limited context as well as understanding the meteorological and traffic-related factors that influence CO concentration in Uyo urban.

Biological monitoring and personal exposure to traffic-related air pollutants of elementary school-age children living in a metropolitan area

An ever-growing burden of scientific evidence links air pollution to different aspects of human health even at very low concentrations; the impact increases for those living in urban environments, especially the youngest and the elderly. This study investigated the exposure to air pollution of urban school children of Milan, Italy, by personal and biological monitoring, in the frame of the MAPS-MI project.A total of 128 primary school children (7–11 years) were involved in a two-season monitoring campaign during spring 2018 and winter 2019. Personal exposure to airborne VOCs and eBC, and biological monitoring of urinary benzene (BEN-U) and methyl-tert-butyl ether (MTBE-U) were performed. Time-activity patterns, environmental tobacco smoke (ETS), spatial, and meteorological information were evaluated as determinants in mixed effects regression analysis.Children personal exposure was mostly quantifiable with median (5th–95th percentile) levels 1.9 (0.8–7.5) μg/m3 for eBC, and 1.1 (<0.6–3.4) and 0.8 (0.3–1.8) μg/m3 for benzene and MTBE, respectively; with values 2–3-fold higher in winter than in spring. In urine, median (5th–95th) BEN-U and MTBE-U levels were 44.9 (25.7–98.6) and 11.5 (5.0–35.5) ng/L, respectively. Mixed effect regression models explained from 72 to 93 % of the total variability for air pollutants, and from 58 to 61 % for biomarkers. Major contributors of personal exposure were season, wind speed, mobility- or traffic-related variables; biomarkers were mostly predicted by airborne exposure and ETS.Our results suggest that traffic-mitigation actions, together with parents' educational interventions on ETS and commuting mode, should be undertaken to lower children exposure to air pollution.

Continuous estimations of daily PM2.5 chemical components from temporally sparse monitoring data using a machine learning approach

Accurate estimates of the concentrations of fine particulate matter (PM2.5) components at high spatial and temporal resolution is essential for assessing their impact on human health. In this study, we developed prediction models of daily concentrations of major PM2.5 components (sulfate, nitrate, ammonium, elemental carbon, and organic carbon) in the Kansai region, Japan, from 2010 to 2017 using the random forest algorithm. The objective is to establish a modeling approach for obtaining accurate daily estimates of PM2.5 component concentrations using temporally sparse monitoring data covering only 15% of the study period. We used gaseous and particulate pollutant concentrations, chemical transport model outputs, simulated meteorological parameters, and conventional land use and traffic-related variables to produce daily estimations at 1 km × 1 km resolution. We evaluated our models via spatial and temporal cross-validation and obtained R2 values of 0.59–0.86 for individual components. The model reproduced the day-to-day variations well, with Pearson’s correlation coefficients of 0.75–0.88 between estimates and independent data collected at continuous monitors. We estimated the daily concentrations of PM2.5 components from 2010 to 2017 at a 1 km × 1 km resolution. The annual trends of the components were obtained by temporally and spatially aggregating the daily estimations. Our modeling approach enabled accurate estimates of the daily PM2.5 component levels using temporally sparse monitoring data. The estimated concentrations will be further utilized in a birth cohort study to assess the potential health impacts of PM2.5 components.

Impacts of traffic-related particulate matter pollution on semen quality: A retrospective cohort study relying on the random forest model in a megacity of South China

BackgroundEmerging evidence shows the detrimental impacts of particulate matter (PM) on poor semen quality. High-resolution estimates of PM concentrations are conducive to evaluating accurate associations between traffic-related PM exposure and semen quality. MethodsIn this study, we firstly developed a random forest model incorporating meteorological factors, land-use information, traffic-related variables, and other spatiotemporal predictors to estimate daily traffic-related PM concentrations, including PM2.5, PM10, and PM1. Then we enrolled 1310 semen donors corresponding to 4912 semen samples during the study period from January 1, 2019, and December 31, 2019 in Guangzhou city, China. Linear mixed models were employed to associate individual exposures to traffic-related PM during the entire (0–90 lag days) and key periods (0–37 and 34–77 lag days) with semen quality parameters, including sperm concentration, sperm count, progressive motility and total motility. ResultsThe results showed that decreased sperm concentration was associated with PM10 exposures (β: -0.21, 95 % CI: −0.35, −0.07), sperm count was inversely related to both PM2.5 (β: -0.19, 95 % CI: −0.35, −0.02) and PM10 (β: -0.19, 95 % CI: −0.33, −0.05) during the 0–90 days lag exposure window. Besides, PM2.5 and PM10 might diminish sperm concentration by mainly affecting the late phase of sperm development (0–37 lag days). Stratified analyses suggested that PBF and drinking seemed to modify the associations between PM exposure and sperm motility. We did not observe any significant associations of PM1 exposures with semen parameters. ConclusionOur results indicate that exposure to traffic-related PM2.5 and PM10 pollution throughout spermatogenesis may adversely affect semen quality, especially sperm concentration and count. The findings provided more evidence for the negative associations between traffic-related PM exposure and semen quality, highlighting the necessity to reduce ambient air pollution through environmental policy.

Application of Extremely Randomised Trees for exploring influential factors on variant crash severity data

Crash severity models play a crucial role in evaluating the influencing factors in the severity of traffic crashes. In this study, Extremely Randomised Tree (ERT) is used as a machine learning technique to analyse the severity of crashes. The crash data in the province of Khorasan Razavi, Iran, for a period of 5 years from 2013 to 2017, is used for crash severity model development. The dataset includes traffic-related variables, vehicle specifications, vehicle movement, land use characteristics, temporal characteristics, and environmental variables. In this paper, Feature Importance Analysis (FIA), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots are utilised to analyse and interpret the results. According to the results, the involvement of vulnerable road users such as motorcyclists and pedestrians alongside traffic-related variables are among the most significant variables in crash severity. Results show that the presence of motorcycles can increase the probability of injury crashes by around 30% and almost double the probability of fatal crashes. Analysing the interaction of PDPs shows that driving speeds above 60 km/h in residential areas raises the probability of injury crashes by about 10%. In addition, at speeds higher than 70 km/h, the presence of pedestrians approximately increases the probability of fatal crashes by 6%.

Quiet Route Planning for Pedestrians in Traffic Noise Polluted Environments

Noise pollution is one of the main stressors in urban environments, having negative impacts on people’s quality of life and health. For some groups of citizens, such as school children, patients, and elders, there is a need to support them in finding pedestrian routes in noise polluted areas of cities. In this paper, we focus on the estimation of traffic noise, and present an approach to provide quiet routing services, taking into account the estimated noise levels of roads. By combining Volunteered Geographic Information, official socio-economic data, and open-access GPS trajectory data, we develop a set of traffic related variables, and apply machine learning methods to perform traffic volume estimations. Given the estimated traffic information, an existing traffic noise model is then employed to derive the noise polluted areas. For generation of quiet routes, a new routing algorithm is proposed based on the Dijkstra algorithm. It minimizes the exposure of pedestrians to traffic noise pollution while taking into account the route distance constraint. We apply our quiet routing approach to the city of Heidelberg (Germany). The application results demonstrate the efficacy of our algorithms in the generation of quiet routes customized to pedestrian preferences.

Land-Use Regression of Long-Term Transportation Data on Metabolic Syndrome Risk Factors in Low-Income Communities

Traffic-related air pollution has been associated with adverse cardiovascular health effects in near-road residents. Transportation parameters are important surrogate variables to determine spatial variation of air pollution and consequential health outcomes. We used land-use regression models to explore associations between cardiovascular (metabolic syndrome [MetS]) health outcomes collected from a sample of low-income participants ( N = 4,959) and transportation parameters within a defined impact zone of a participant’s residence. We hypothesize cardiovascular risk factors are associated with spatially distributed transportation parameters and land-use data. MetS risk factors (waist circumference, blood pressure, triglycerides, HDL-cholesterol, and glucose) were obtained from 4,945 participants between 2014 and 2020 across the city of El Paso, Texas. Traffic-related and land-use variables were acquired from the El Paso MPO and the U.S. Census Bureau within two impact zones of 500 m and 1,000 m radius, centered at each participant resident’s home latitude and longitude coordinates using GIS. The increase in street length within 500 m radius was found to associate with increases in BMI, waist circumference, triglycerides, and glucose ( p &lt; 0.05). Glucose showed positive relationships with inverse distance to the nearest international ports of entry ( p = 0.02). Also, as the total length of the street increases, the likelihood of a high waist ( p &lt; 0.01), high triglycerides ( p = 0.03), and low HDL-cholesterol ( p &lt; 0.01) also increased. Based on a multivariable regression model, a probability surface map was prepared to show the spatial distribution of likelihood for acquiring MetS in El Paso, TX.

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. These techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

Who overtakes more? Explanatory analysis of the characteristics of drivers from low/middle and high-income countries on passing frequency

The passing manoeuvre requires a driver to make decisions and take actions which are dependent on his/her behavioural characteristics and driving ability. However, previous works on passing rate models have exclusively considered geometric and traffic-related variables. This study aims at bridging this gap by investigating the influence of driver profile (i.e., age, gender, nationality - Italian or Iranian - aggressive driving scores, driving exposure) on passing frequency. A driving simulation experiment involving 54 drivers (36 Italians, 18 Iranians) was conducted along a 6.67 km segment of a two-lane rural highway with passing manoeuvres permitted along 25% of its length. Controlled factors included traffic flow and speed in the oncoming direction, and speed in the driver direction, with a total of 27 scenarios assigned to drivers based on a 33 confounded factorial design. A Poisson regression model was used to investigate the significance of independent variables. Age and gender and their interaction term were significant, thus the effects of age and gender on the number of passing manoeuvres are mutually interdependent. Furthermore, drivers who drive less often completed fewer overtaking manoeuvres. Sensitivity analyses were carried out to understand the magnitude of change in passing frequency attributable to a variation in the explanatory variables. The findings suggest that driver characteristics have a significant effect on passing frequency and should be considered when conducting a performance and safety evaluation of two-lane roads.

Exploration of the relationship among roadway characteristics, operating speed, and crashes for city streets using path analysis

Estimating the speed-crash relationship has long been a focus area of interest in roadway safety analysis. Because of many confounding factors that may influence both speeds and crashes, the relationship cannot be appropriately established without considering the corresponding roadway contexts and accounting for their effects on speeds and crashes. This paper investigates the speed-crash relationship for city streets by jointly modeling speed, roadway characteristics, and crashes using a path analysis approach that has been recently introduced into safety analysis while incorporating a wide range of roadway and traffic related variables and additional speed measures. The results from the coherent path analysis identified multiple speed measures of interest that have a statistically significant association with crashes as well as having intuitive and useful interpretation. The results also supported a positive relationship between speed variability and crash occurrence (i.e., larger spread/variability in operational speed is associated with more crashes).

Multiple environmental exposures in early-life and allergy-related outcomes in childhood

IntroductionEarly onset and high prevalence of allergic diseases result in high individual and socio-economic burdens. Several studies provide evidence for possible effects of environmental factors on allergic diseases, but these are mainly single-exposure studies. The exposome provides a novel holistic approach by simultaneously studying a large set of exposures. The aim of the study was to evaluate the association between a broad range of prenatal and childhood environmental exposures and allergy-related outcomes in children. Material and MethodsAnalyses of associations between 90 prenatal and 107 childhood exposures and allergy-related outcomes (last 12 months: rhinitis and itchy rash; ever: doctor-diagnosed eczema and food allergy) in 6–11 years old children (n = 1270) from the European Human Early-Life Exposome cohort were performed. Initially, we used an exposome-wide association study (ExWAS) considering the exposures independently, followed by a deletion-substitution-addition selection (DSA) algorithm considering all exposures simultaneously. All the exposure variables selected in the DSA were included in a final multi-exposure model using binomial general linear model (GLM). ResultsIn ExWAS, no exposures were associated with the outcomes after correction for multiple comparison. In multi-exposure models for prenatal exposures, lower distance of residence to nearest road and higher di-iso-nonyl phthalate level were associated with increased risk of rhinitis, and particulate matter absorbance (PMabs) was associated with a decreased risk. Furthermore, traffic density on nearest road was associated with increased risk of itchy rash and diethyl phthalate with a reduced risk. DSA selected no associations of childhood exposures, or between prenatal exposures and eczema or food allergy. DiscussionThis first comprehensive and systematic analysis of many environmental exposures suggests that prenatal exposure to traffic-related variables, PMabs and phthalates are associated with rhinitis and itchy rash.