•Type 2 diabetes is a leading contributor to the global burden of public health.•Residential traffic density increases type2 diabetes prevalence and medication use.•NO2, PM10, PM2.5, and O3 ...significantly associated with antidiabetic medication use.
Type 2 diabetes has raised great public health concerns due to its association with the increased risk of several adverse health outcomes. We aimed to investigate the association of criteria air pollutants and traffic density with the prevalence of type 2 diabetes and antidiabetic medication use in California.
A cross-sectional study was conducted using 2005 California Health Interview Survey (CHIS) data, linked with criteria air pollutant exposure measures, including government-monitored ozone (O3), particulate matter (PM10, PM2.5) and nitrogen dioxide (NO2), residential traffic density, and land use regression modeled NOX, NO, and NO2 estimates for Los Angeles County only based on the respondents’ geocoded residential addresses. Weighted logistic regression models were used to examine the influences of 36-month average exposures to each air pollutant and traffic density on diabetes prevalence and medication use.
Among 31,483 CHIS 2005 state-wide respondents, 6.7% of adults reported having been diagnosed with type 2 diabetes. We observed type 2 diabetes prevalence was positively associated with exposures to O3, PM10, and PM2.5, and with NO, NO2, and NOx only in Los Angeles County. For each 10ppb increase in O3 or 10ug/m3 increase in PM10 or PM2.5, the odds of taking any medication increased by 40%, 56%, and 50%; taking pills increased by 33%, 31%, and 41%; taking insulin increased by 43%, 53%, and 46%; and taking both insulin and pills increased by 70%, 60%, and 88%, respectively. When traffic density within 750 feet of a respondent's home increased by one interquartile, 7% increase in odds of using any medication and taking pills was also observed.
This study adds to evidence indicating greater air pollution exposure is associated with increased diabetes prevalence. It also provides new evidence demonstrating a strong association between pollutant exposure and antidiabetic medication use in adult Californians.
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•GF-2 time-series images were collected to study traffic density changes throughout Wuhan lockdown.•A method combing morphology filter and deep learning was proposed for vehicle detection.•Traffic ...density of Wuhan dropped with the percentage higher than 80% during city lockdown.•Reduction of Traffic density on main roads is higher than 90% caused by lockdown.•After lockdown lift, traffic density returned to norm with a slight increase of 1% on main roads.
In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow. Therefore, in this paper, we collected time-series high-resolution remote sensing images with the resolution of 1 m acquired before, during and after Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and counted for the statistics of traffic density to reflect the changes of human transmissions in the whole period of Wuhan lockdown. Open Street Map was used to obtain observation road surfaces, and a vehicle detection method combing morphology filter and deep learning was utilized to extract vehicles with the accuracy of 62.56%. According to the experimental results, the traffic density of Wuhan dropped with the percentage higher than 80%, and even higher than 90% on main roads during city lockdown; after lockdown lift, the traffic density recovered to the normal rate. Traffic density distributions also show the obvious reduction and increase throughout the whole study area. The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.
We propose a combined particle-based density prediction model consisting of three components: trajectory prediction for existing particles, entering particle prediction, and iterative sampling. At ...initialization, the combined model takes in a set of trajectories for trajectory prediction and a sequence of observation vectors for entering particle prediction. Then, the iterative sampling module generates the density prediction for the next time instance. It will also sample a pool of particles and pass on their trajectories to the next trajectory prediction model for future density prediction.
Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach ...incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure – especially near major roads in the U.S.
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•An advanced national-level traffic model based on point-level traffic volume data•Traffic volume estimations and meteorological parameters for RLINE input•NO2 exposure assessment incorporated RLINE into an advanced spatiotemporal model•RLINE boosted model performance, especially for roadside sites
Owing to their physical and chemical properties, particles generated by the abrasion of tyre tread against road surfaces, or tyre wear particles, are recognised as microplastics. Recent desk-based ...studies suggest tyre wear to be a major contributor of microplastic emissions to the environment. This study aimed to quantify tyre wear in roadside drains and the natural environment near to a major road intersection. Tyre particles were identified by visual identification and a subsample confirmed as tyre wear by GC-MS using N-cyclohexyl-2-benzothiazolamine (NCBA) as a marker. The abundance of tyre wear within roadside drains was greater in areas associated with increased braking and accelerating than that with high traffic densities (
p
= < 0.05). Tyre particle abundance in the natural environment ranged from 0.6 ± 0.33 to 65 ± 7.36 in 5 mL of material, with some evidence of decline with distance from the road. This study offers preliminary data regarding the generation and abundance of this under-researched microplastic.
Traffic density estimation is a very important component of an automated traffic monitoring system. Traffic density estimation can be used in a number of traffic applications – from congestion ...identification to macroscopic traffic control in urban environment. In this paper, we implemented Single Shot Detection (SSD) and MobileNet-SSD to estimate traffic density. SSD is capable of handling different shape, size and view angle of the objects. MobileNet-SSD is a cross-trained model from SSD to MobileNet architecture, which is faster than SSD. In this study, we show a key application area for the SSD and MobileNet-SSD framework. The advantages and shortcomings of the SSD and MobileNet-SSD framework were analyzed using fifty-nine individual traffic cameras. In addition, we compared the two algorithms with manually estimated density. The SSD framework shows significant potential in the field of traffic density estimation. SSD achieved 92.97% average detection accuracy in our experiment. On the other hand, the MobileNet-SSD achieved 79.30% average detection accuracy.
•Real time traffic density estimation by traffic cameras and deep learning algorithms.•Car counting using SSD and MobileNet-SSD.•Quantitative analysis of the algorithms based on number of training data vs accuracy.•Performance and speed comparison of SSD and MobileNet-SSD.
Air traffic density prediction is crucial to aviation safety and air traffic management (ATM). Understanding the complex spatial–temporal varying traffic patterns and the inter-dependencies of air ...traffic networks is especially demanding. To address these challenges, a novel knowledge-based deep learning framework, Bayesian ensemble graph attention network (BEGAN), is proposed to leverage the interactions of traffic density at different airspace grids and predict stochastic traffic density near the terminal. The core concept of BEGAN is to divide the large airspace into small overlapped gridded sections for individual training, and then assemble posterior distributions of density forecasts for large-scale prediction. The ensemble method improves the scalability of handling heterogeneous air traffic flow patterns. Algorithm-wise, a weighted directed graph interleaves the connection between different gridded airspaces. A parallel graph attention network (P-GAT) block followed by the long short-term memory (LSTM) network is designed to learn the spatial–temporal dependencies of the graph. Notably, P-GAT can incorporate existing domain knowledge into data-driven learning, such as historical trajectory paths, future flight plans, and airspace restrictions. The inclusion of existing knowledge and rules proves beneficial for air traffic density prediction in heavily regulated and controlled airspace. We evaluate BEGAN with real-world data collected from Hartsfield-Jackson Atlanta International Airport (KATL) and Orlando International Airport (KMCO). Extensive experiments show that the proposed framework can make accurate and robust predictions, outperforming nine baseline models. Based on the proposed study, limitations and future research directions are given.
•Proposes dynamic graph-based deep learning for air traffic density prediction.•Retrieves and processes real-world data for multi-layer gridded problem formulation.•Bayesian approximation embedded in deep learning for uncertainty quantification.•Performance evaluated at operational airports with ablation and robustness studies.
Dynamic traffic prediction is an important section of the urban intelligent transportation system. Although there have been many studies in this area, it is still a challenge for the urban road ...network considering the complexity of urban traffic and the lack of high-quality traffic data. Electronic Registration Identification of Vehicles (ERI) is an emerging traffic information acquisition technology based on Radio Frequency Identification (RFID). It can identify each vehicle accurately and generate high-quality traffic data. We employ ERI data to realize the dynamic prediction of traffic density and travel time for the urban road network. First of all, we study the temporal characteristics model of traffic through the Markov chain. Secondly, combining the Expectation–Maximization algorithm and logistic regression classifier, we classify the training data into different traffic scenes and build the spatial characteristics model for each traffic scene. The model parameters are obtained by the particle swarm optimization algorithm. Then, the trained temporal and spatial models are combined to conduct dynamic traffic prediction. Finally, the real data of Chongqing is utilized to verify the proposed method. The experimental results show that the proposed method has a good prediction accuracy and is suitable for all kinds of roads in the road network. Besides, the constructed model has good interpretability for real traffic.
•This paper studies the prediction of traffic density and travel time.•We use novel traffic data, Electronic Registration Identification data of vehicles.•The temporal and spatial characteristics of urban traffic are considered.•The interpretability of the prediction model is highlighted.
•This paper proposes a novel adaptive-R extended Kalman filter (AREKF) combined with a model-based data imputation technique to estimate traffic density even when data is missing, due to for example ...sensor failure.•We show analytically that the AREKF is able to accurately estimate the density even when the noise covariance matrices are not accurately known; as a result, it is no longer necessary to accurately know the noise covariance matrices which are often unknown and varying with time.•The estimated density from the AREKF with data imputation is fed into a real-time ramp metering control algorithm to control vehicle flow on a merging road, which is highly susceptible to traffic congestion; the results show that the AREKF can accurately estimate the traffic density, and the ramp metering control algorithm yields a significant improvement to the traffic flow and thus, alleviates congestion.•The computational cost of our proposed method with normal data, missing data, and missing data imputation is very low, which makes it suitable for real-time implementation.
Traffic density is a crucial indicator of traffic congestion, but measuring it directly is often infeasible and hence, it is usually estimated based on other measurements. However, a challenge in measuring traffic parameters is the high probability of sensor failure, which results in missing measurement or missing data. To overcome this difficulty, in this paper, we propose a novel adaptive-R extended Kalman filter (AREKF) combined with a model-based data imputation technique to estimate traffic density. We show analytically that the AREKF is able to accurately estimate the density even when the noise covariance matrices are not accurately known. Microscopic traffic simulations demonstrated the efficacy of the AREKF, where the estimated density is fed into a real-time ramp metering control algorithm to control vehicle flow on a merging road, which is highly susceptible to traffic congestion. The results show that the proposed AREKF with data imputation is able to accurately estimate the traffic density even when data is missing, and the ramp-metering controller significantly improves the traffic flow and thus, alleviates congestion.
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