Traffic Exposure Measures Research Articles

A copula-based approach for jointly modeling crash severity and number of vehicles involved in express bus crashes on expressways considering temporal stability of data

The consequences of crashes, including injury, loss of lives, and damage to properties, are further worsened when buses plying expressways are involved in the crash. Previous studies have separately analyzed crash severity in terms of monetary cost, injuries and loss of lives, and the size of crashes in terms of the number of vehicles involved. However, as both outcome variables are correlated, it is imperative to perform a combined analysis using an appropriate econometric model to achieve a better model fit. This study contributes to the literature by jointly exploring the factors influencing the severity and size of express bus-involved crashes that occur on expressways and characterizes the dependence between both outcome variables by employing a more plausible copula regression framework. Likelihood ratio tests were also conducted to investigate the temporal stability of the factors that affect both crash severity and size. Based on the goodness-of-fit statistics, the Frank copula model proved superior to the independent ordered probit model. The estimate of the underlying dependence between the outcome variables provided a better comprehension of the correlation between them. Temporal instability was detected for the individual parameters in the models and is attributed to the changing driving behavior due to the heightened road safety campaigns. The results suggest that traffic exposure measures are significantly associated with a higher propensity of observing increased bus crash severity and size. Insights into the factors influencing the size and severity of express bus crashes are discussed, and appropriate engineering, enforcement, and education-related countermeasures are proposed.

Cyclist-vehicle crash modeling with measurement error in traffic exposure

Exposure measures are always among the explanatory variables of any crash model. Regardless of the technique used to model crash, the mean crash frequency will increase with an increase in exposure since more crashes are likely to occur at higher exposure. For cyclist-vehicle crash models, bike and vehicle exposure measures are essential for an accurate and reliable estimate of the cyclist crash risk. However, traffic exposure measures are an example of variables that are measured with error. Generally, measurement error in regression estimates has three effects: 1) produce bias in parameter estimation for statistical models, 2) lead to a loss of explanation power, 3) mask important features of the data. This study proposes a full Bayesian Poisson Lognormal crash models that account for measurement error in traffic exposure measures (i.e., Vehicle Kilometers Travelled and Bike Kilometers Travelled). The underlying approach is to adjust the traffic exposure measures for measurement error to improve the accuracy of the crash model and crash model estimates. The full Bayesian models are developed using data for 134 traffic analysis zones (TAZs) in the city of Vancouver, Canada. The results show that Poisson Lognormal models that account for measurement error have a better fit for the modeled cyclist-vehicle crash data compared to traditional Poisson Lognormal models. The estimates of the Poisson Lognormal model that accounts for measurement error are consistent, with traditional Poisson Lognormal models’ estimates except for the BKT and VKT estimates. Estimates of the BKT and VKT increased after introducing measurement error, which indicates an underestimation (downward bias) to BKT and VKT estimates in case of overlooking measurement error.

A Novel Approach for Identifying, Diagnosing, and Treating Active Transportation Safety Issues

There has been an increasing interest in active transportation because of its many health, environmental, and economical benefits. However, active commuters are subjected to an elevated level of severe crash risk, which can be a deterrent to many road users to shift to active transportation. Therefore, there is a need for developing systematic approaches to improve the safety of active commuters. This paper presents a new approach for identifying, diagnosing, and remedying active transportation safety issues. The approach is demonstrated through a case study of the City of Vancouver’s 134 traffic analysis zones. Comprehensive GIS data related to traffic exposure, socio-economics, land use, built environment, street network, and cyclist and pedestrian networks were used in the analysis. A multivariate full Bayesian spatial mixed crash model (CM) was developed incorporating cyclist and pedestrian crashes as well as motorized and non-motorized traffic exposure measures. The CM was used to identify the top 10% active transportation crash-prone zones (CPZs) and safe zones (SZs) using the novel Mahalanobis distance method. CPZs were found clustered in the Downtown. Sixteen trigger variables were statistically investigated for each CPZ and SZ. Lastly, remedies, related to land use, traffic demand, and traffic supply management, were proposed using the trigger variable analysis and literature consultation.

2445 - Multifaceted Transportation Performance Integrating Mobility, Safety, Air Quality, Physical Activity and Public Health

Background While mobility, safety, and environmental friendliness have been major interests in transportation systems, recently people have started to pay more attention to public health related to transportation. Emphasis has been shifted from mobility and convenience to environmental friendliness and public health. However, traditional performance measures are insufficient in addressing such changes. Therefore, there is a need to provide a multifaceted approach to integrate such performance measures. Method This study proposed an assessment framework that integrates four aspects: mobility or network performance, traffic safety, environmental impact, and physical activity. In order to demonstrate its applicability, the proposed framework was employed and tested in Michigan as a case study. Data in individual categories were collected and analyzed. Delay costs, metabolic equivalent (MET), the number of crashes, and concentration of O3 were used as performance measures representing network performance, physical activity, safety, and environmental impact, respectively. Result Using the multifaceted assessment framework, this study compared performance measures by region (Bay Region, Grand Region, Metro Region, North Region, Southwest Region, and University Region) in Michigan. The result with data in 2015 showed that the Metro region, the most populated area in Michigan, was weaker than other reasons with respect to network performance, traffic safety and physical activity. By producing a multidimensional spider diagram, the strengths and weaknesses of each region's transportation performance results were analyzed and visualized. The results demonstrated its capacity to present multifaceted performance measures and their relative performances among regions. Conclusion This study proposed an integrated approach and presented a case example using the observed data in Michigan. The method was useful researchers understanding transportation performances in a comprehensive manner integrating multifaceted measures. The approach is expected to help decision-makers in developing traffic management strategies by allowing analyses of traffic-related public health strategies based on traffic exposure measures. In order to examine the effects of alternative strategies, performance measures could be derived from activity-based travel demand models or simulations. Simulation-based assessments can serve as a decision supporting tool for evaluating and/or selecting various treatment options for public health strategies prior to actual implementation. The application of activity-based travel demand models makes it possible to quantify various measures in the proposed approach by providing the effects of alternative strategies.

Air pollution and Breast Cancer: A Review.

Breast cancer is the most common cancer diagnosed among US women. Air pollution is a pervasive mixture of chemicals containing carcinogenic compounds and chemicals with endocrine disrupting properties. In the present review, we examine the epidemiologic evidence regarding the association between air pollution measures and breast cancer risk. We identified seventeen studies evaluating the risk of breast cancer associated with air pollution. A higher risk of breast cancer has been associated with nitrogen dioxide (NO2) and nitrogen oxides (NOx) levels, both of which are proxies for traffic exposure. However, there is little evidence supporting a relationship for measures of traffic count or distance to nearest road, or for measures of particulate matter (PM), except potentially for nickel and vanadium, which are components of PM10. Hazardous air toxic levels and sources of indoor air pollution may also contribute to breast cancer risk. There is little existing evidence to support that the relationship between air pollution and breast cancer risk varies by either menopausal status at diagnosis or combined tumor hormone receptor subtype defined by the estrogen receptor (ER) and progesterone receptor (PR). Epidemiologic evidence to date suggests an association between breast cancer risk and NO2 and NOx, markers for traffic-related air pollution; although there was little evidence supporting associations for proxy measures of traffic exposure or for PM. More research is needed to understand the role of specific PM components and whether associations vary by tumor receptor subtype and menopausal status at diagnosis.

Prenatal and childhood traffic-related air pollution exposure and childhood executive function and behavior

BackgroundTraffic-related air pollution exposure may influence brain development and function and thus be related to neurobehavioral problems in children, but little is known about windows of susceptibility. AimsExamine associations of gestational and childhood exposure to traffic-related pollution with executive function and behavior problems in children. MethodsWe studied associations of pre- and postnatal pollution exposures with neurobehavioral outcomes in 1212 children in the Project Viva pre-birth cohort followed to mid-childhood (median age 7.7years). Parents and classroom teachers completed the Behavior Rating Inventory of Executive Function (BRIEF) and the Strengths and Difficulties Questionnaire (SDQ). Using validated spatiotemporal models, we estimated exposure to black carbon (BC) and fine particulate matter (PM2.5) in the third trimester of pregnancy, from birth to 3years, from birth to 6years, and in the year before behavioral ratings. We also measured residential distance to major roadways and near-residence traffic density at birth and in mid-childhood. We estimated associations of BC, PM2.5, and other traffic exposure measures with BRIEF and SDQ scores, adjusted for potential confounders. ResultsHigher childhood BC exposure was associated with higher teacher-rated BRIEF Behavioral Regulation Index (BRI) scores, indicating greater problems: 1.0 points (95% confidence interval (CI): 0.0, 2.1) per interquartile range (IQR) increase in birth-age 6BC, and 1.7 points (95% CI: 0.6, 2.8) for BC in the year prior to behavioral ratings. Mid-childhood residential traffic density was also associated with BRI score (0.6, 95% CI: 0.1, 1.1). Birth-age 3BC was not associated with BRIEF or SDQ scores. Third trimester BC exposure was not associated with teacher-rated BRI scores (−0.2, 95% CI: −1.1, 0.8), and predicted lower scores (fewer problems) on the BRIEF Metacognition Index (−1.2, 95% CI: −2.2, −0.2) and SDQ total difficulties (−0.9, 95% CI: −1.4, −0.4). PM2.5 exposure was associated with teacher-rated BRIEF and SDQ scores in minimally adjusted models but associations attenuated with covariate adjustment. None of the parent-rated outcomes suggested adverse effects of greater pollution exposure at any time point. ConclusionsChildren with higher mid-childhood exposure to BC and greater near-residence traffic density in mid-childhood had greater problems with behavioral regulation as assessed by classroom teachers, but not as assessed by parents. Prenatal and early childhood exposure to traffic-related pollution did not predict greater executive function or behavior problems; third trimester BC was associated with lower scores (representing fewer problems) on measures of metacognition and behavioral problems.

ASSOCIATIONS BETWEEN TRAFFIC AND CARDIOVASCULAR MORTALITY IN A COHORT OF CALIFORNIA WOMEN

Background and Aim: Several studies have reported associations between exposure to traffic and cardiovascular events and mortality. We examined relationships between exposures to traffic and various measures of mortality and morbidity in a cohort of over 65,000 residentially stable female teachers and administrators participating in the California Teachers Study, who were followed from 1996 though 2005. Mean age of the cohort at inception was 54; the baseline active smoking rate was 5%, and participants had similar occupational exposures. Methods: Participants’ geocoded addresses were linked with cross-sectional measures of traffic intensity from the year 2000. We developed three measures of traffic exposure: (1) traffic density (estimated number of vehicle miles traveled within 150 m of a participant’s residence; (2) proximity to a highway within versus beyond 150 m; and (3) vehicle density (number of vehicles registered in each participant’s Census block group, divided by the block group area). Using Cox proportional hazards models, we examined associations between these metrics and several measures of mortality, as well as incident myocardial infarction and stroke. Results: We observed several associations between traffic density and elevated risks of all-cause, cardiopulmonary, and cardiovascular mortality, with statistically significant risk gradients with increasing vehicle density. For example, the hazard ratio for participants in the 90th percentile of traffic density in relation to those below the 50th percentile was 1.20 (95% CI = 1.03, 1.39) for cardiovascular mortality. Associations were not observed between traffic density and incident myocardial infarction or stroke, or for any of the other traffic metrics. Conclusion: This study provides additional evidence that exposure to traffic is associated with mortality from heart disease in a cohort of middle-aged and older women.

Traffic exposure and incident venous thromboembolism in the Atherosclerosis Risk in Communities (ARIC) Study

Two recent case-control studies in Italy reported that long-term exposure to particulate air pollution or living near major traffic roads was associated with an increased risk of deep vein thrombosis (DVT). No prospective evidence exists on the possible association between long-term traffic-related air pollution and incident venous thromboembolism (VTE). To examine the association between long-term traffic exposure and incident VTE in a population-based prospective cohort study. We studied 13,143 middle-aged men and women in the Atherosclerosis Risk in Communities Study without a history of DVT or pulmonary embolism at baseline examination (1987-1989). The Geographical Information System-mapped traffic density and distance to major roads in the four study communities served as measures of traffic exposure. We examined the association between traffic exposure and incident VTE with proportional hazards regression models. A total of 405 subjects developed VTE in 2005. Traffic density was not significantly associated with VTE. Relative to those in the lowest quartile of traffic density, the adjusted hazard ratios across increasing quartiles were 1.18 (95% confidence interval [CI] 0.88-1.57), 0.99 (95% CI 0.74-1.34) and 1.14 (95% CI 0.86-1.51) (P-value for trend across quartiles = 0.64). For residents living within 150 m of major roads, as compared with subjects living further away, the adjusted hazard ratio was 1.16 (95% CI 0.95-1.42, P = 0.14). This first prospective study in the general population does not support an association between air pollution exposure or traffic proximity and risk of DVT. More data may be needed to clarify whether traffic or air pollution influences the risk of VTE.

Socioeconomic position and health status of people who live near busy roads: the Rome Longitudinal Study (RoLS)

BackgroundSubjects living close to high traffic roads (HTR) are more likely to suffer from air-pollution related morbidity and mortality. The issue has large public health consequences but few studies have described the main socio-demographic characteristics of people exposed to traffic.ObjectivesTo characterise a large cohort of residents in Rome according to different measures of traffic exposure, socioeconomic position (SEP), and baseline health status.MethodsResidents of Rome in October 2001 were selected. Individual and area-based SEP indices were available. GIS was used to obtain traffic indicators at residential addresses: distance from HTR (> = 10,000 vehicles/day), length of HTR, average daily traffic count, and traffic density within 150 meters of home. Hospitalisations in the 5-year period before enrolment were used to characterise health status. Logistic and linear regression analyses estimated the association between traffic exposure and socio-demographic characteristics.ResultsWe selected 1,898,898 subjects with complete SEP information and GIS traffic indicators. A total of 320,913 individuals (17%) lived within 50 meters of an HTR, and 14% lived between 50 and 100 meters. These proportions were higher among 75+ year-old subjects. Overall, all traffic indicators were directly associated with SEP, with people living in high or medium SEP areas or with a university degree more likely to be exposed to traffic than people living in low SEP areas or with a low level of education. However, an effect modification by area of residence within the city was seen and the association between traffic and SEP was reversed in the city centre.ConclusionsA large section of the population is exposed to traffic in Rome. Elderly people and those living in areas of high and medium SEP tend to be more exposed. These findings are related to the historical stratification of the population within the city according to age and socioeconomic status.

Residential Exposure to Traffic and Spontaneous Abortion

BackgroundStudies have shown associations between air pollution or traffic exposure and adverse birth outcomes, such as low birth weight. However, very few studies have examined the effect of traffic emissions on spontaneous abortion (SAB).ObjectiveThe goal of this study was to determine whether residential exposure to vehicular traffic was associated with SAB.MethodsPregnant women from a prepaid health plan in California were recruited into a prospective cohort study in 1990–1991. Three measures of traffic exposure were constructed for the 4,979 participants using annual average daily traffic (AADT) counts near each residence and distance from residence to major roads. SAB was examined in relation to the traffic exposure measures using logistic regression adjusting for a number of demographic and lifestyle variables.ResultsOf the traffic measures, maximum annual average traffic within 50 m showed the strongest association with SAB, although it was not statistically significant. The adjusted odds ratio (AOR) for the top 90th percentile (AADT greater than 15,199) versus the bottom 75th percentile (AADT = 0–1,089) was 1.18 [95% confidence interval (CI), 0.87–1.60]. However, subgroup analyses showed statistically significant associations for traffic with SAB among African Americans (AOR = 3.11; 95% CI, 1.26–7.66) and nonsmokers (AOR = 1.47; 95% CI, 1.07–2.04).ConclusionIn this cohort, living within 50 m of a road with AADT of 15,200 or more was significantly associated with SAB among African Americans and nonsmokers. Further research is needed to confirm these results and possibly elucidate the mechanisms responsible for the findings.

Detecting Unsafe Roadways with Spatial Statistics: Point Patterns and Geostatistical Models

A detection scheme that uses classical and spatial statistics has been developed to identify roadways with the most severe safety needs. It is based on the null hypothesis that all roadways have the same crash risk, that is, all have the same nonfatal and fatal crash rates throughout the entire study region. Fatal and nonfatal crash rates, which are assumed to be randomly distributed as Poisson processes, are modeled with a marked homogeneous Poisson process model. Since the traffic exposures are typically unknown at the crash sites, they are predicted with a geostatistical model. Locations, where the null hypothesis is rejected, are safety treatment candidates. P-value risk rankings are used to identify locations with the most severe safety needs. The results from alternative analyses—one using vehicle miles traveled and another using population as measures of traffic exposure—are conducted and compared. The state of New Hampshire is used in a case study. The effects of analyzing areas with small traffic exposures, the so-called small-area estimation problem, and the aim to develop a data-driven detection scheme that meets the requirements as an objective decision-making tool are discussed.

Prospective Analysis of Traffic Exposure as a Risk Factor for Incident Coronary Heart Disease: The Atherosclerosis Risk in Communities (ARIC) Study

BackgroundFor people living close to busy roads, traffic is a major source of air pollution. Few prospective data have been published on the effects of long-term exposure to traffic on the incidence of coronary heart disease (CHD).ObjectivesIn this article, we examined the association between long-term traffic exposure and incidence of fatal and nonfatal CHD in a population-based prospective cohort study.MethodsWe studied 13,309 middle-age men and women in the Atherosclerosis Risk in Communities study, without previous CHD at enrollment, from 1987 to 1989 in four U.S. communities. Geographic information system–mapped traffic density and distance to major roads served as measures of traffic exposure. We examined the association between traffic exposure and incident CHD using proportional hazards regression models, with adjustment for background air pollution and a wide range of individual cardiovascular risk factors.ResultsOver an average of 13 years of follow-up, 976 subjects developed CHD. Relative to those in the lowest quartile of traffic density, the adjusted hazard ratio (HR) in the highest quartile was 1.32 [95% confidence interval (CI), 1.06–1.65; p-value for trend across quartiles = 0.042]. When we treated traffic density as a continuous variable, the adjusted HR per one unit increase of log-transformed density was 1.03 (95% CI, 1.01–1.05; p = 0.006). For residents living within 300 m of major roads compared with those living farther away, the adjusted HR was 1.12 (95% CI, 0.95–1.32; p = 0.189). We found little evidence of effect modification for sex, smoking status, obesity, low-density lipoprotein cholesterol level, hypertension, age, or education.ConclusionHigher long-term exposure to traffic is associated with incidence of CHD, independent of other risk factors. These prospective data support an effect of traffic-related air pollution on the development of CHD in middle-age persons.

How exposure information can enhance our understanding of child traffic “death leagues”

Objectives: To explore whether population-based fatality rates and measures of traffic exposure can be combined to provide a more comprehensive measure of safety. To illustrate how this could be achieved...