Average Traffic Density Research Articles

Effectiveness of chemical road dust suppressants on paved roads of Pimpri Chinchwad

Introduction: Currently, reducing road dust emissions is a serious problem for safe and environment-protective transportation, leading to Particulate Matter (PM) limit violations and high population exposures to dust containing poisonous metals and mineral particles. Nowadays chemical road dust suppressants are being employed more frequently as a viable remedy to reduce roadside dust emissions.
 Materials and methods: Two seasons winter and summer were selected for the study and during which, typically 3 types of roads as per average traffic density and land-use category identified in the city of Pimpri-Chinchwad were sprayed. Dry samples were taken on day one from a 1 m2 area using a portable vacuum cleaner that was run for 8 to 10 min each. Similarly, samples were collected in the same area after 5-10 min after spraying with Calcium Chloride (CaCl ) and Magnesium Chloride (MgCl ). Also, the second set of 2 samples was collected after 24 h of chemical applied.
 Results: Maximum reduction in silt loading occurred i.e., 99.73% on the application of 30 g/m2 of CaCl . Reduction observed in emission factor of PM2.5 was 97.78% and in the case of PM10 it was 97.59%. While increase in average particle size was observed up to 27.88 µm.
 Conclusion: Both the suppressant efficient in suppressing the road dust but the day 1 result shows more dust suppression as compared to the day 2 dust suppression also as per the dust loading CaCl2 is a better dust suppressant than MgCl2.

Pengaruh Karakteristik Lalu Lintas Terhadap Kebisingan Jalan Raya

The availability of transportation facilities in urban areas strongly supports community activities and mobility from one land use to another. The increase in the number of motorized vehicles causes an increase in noise level on the highway.
 The purpose of this study was to analyze the traffic characteristics on Ahmad Yani street Kendari City, noise level, and partial effect of traffic characteristics on the noise level. The research method uses descriptive and parametric statistical approaches.
 The results showed that the average traffic volume on Ahmad Yani street was 1179 pcu/hour, with a composition of motorcycles 59%, light vehicles 37%, and heavy vehicles 4%. The average vehicle speed is 48 km/hour and average traffic density is 51 pcu/km. The highest noise level occurred in the school area of 76.1 dB, while the lowest occurred in the trading area of 70.5 dB. The average noise level on the Ahmad Yani street is 73.4 dB. The effect of vehicle volume on the noise level is 56.7%, vehicle speed on the noise level is 53.4%, and the traffic density on the noise level is 68.7%. This shows that the traffic density variable has the greatest influence on the noise intensity on the highway, followed by vehicle volume variable, and the lowest is vehicle speed variable.

Seizing the Opportunity for Automation—How Traffic Density Determines Truck Drivers' Use of Cruise Control

We analyze how traffic congestion affects the usage of cruise control in real-traffic situations. Usually, a negative relationship is assumed, but empirical evidence in naturalistic settings is rare. We make use of a large sample of truck drivers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> = 562) in Germany. We take advantage of the volatile traffic density imposed by the first implementation of COVID-19-related measures in Germany and analyze truck drivers resulting application of cruise control using random effects models. We match official traffic density information and the share of cruise control usage as recorded by the telematics information of each truck per day. We find that on average traffic density does have the expected effect: more traffic leads to a lower usage of cruise control. However, we find great heterogeneity among drivers and a strong tendency to react nonlinear to changes in traffic density. Additionally, comparing traffic types, heavy-goods vehicle traffic density has a stronger impact on drivers' usage of cruise control than private traffic density. Again, not all drivers react similarly: we investigate eight different reaction patterns to varying types of congestion. Our results show that even in a favorable environment such as low traffic congestion, the use of cruise control cannot be taken for granted: about 25%–34% of the drivers (depending on traffic type) do not respond to changes in traffic density at all. Our results may help to inform models of traffic flow and traffic automation and may serve as a premise for more realistic assumptions about human behavior in traffic.

Research on the Division Method of Signal Control Sub-Region Based on Macroscopic Fundamental Diagram

The macroscopic fundamental diagram (MFD) provides a method to evaluate macro traffic operation through micro traffic parameters, which can be applied to traffic control to prevent traffic congestion transfer and improve road network efficiency. However, due to the large scale of the urban road network as well as the complex temporal and spatial distribution of road congestion, the application of the MFD for signal control first requires the partition of the urban road network. Based on the analysis of MFD partition purposes, a set of MFD partition methods based on graph theory was designed. Firstly, graph theory was used to transform the urban road network; secondly, the minimum spanning tree method was used to divide the urban traffic network map. Moreover, the attribution of the link between connected regions is determined. Our method can solve the problem of ambiguous intersection ownership, and the road sections belonging to the same road in opposite directions are separated. This method has the ability to control the size of the area by limiting the number of intersections; Finally, the evaluation index of regional clustering results was drawn. To achieve the research objective, we collected and processed vehicle information data from the Xuzhou car-hailing platform to obtain traffic density information. Then, we selected an area with sufficient data and a large enough road network. The empirical value range of the regional control value was obtained by comparing the values of multiple groups of measurement data k and evaluation indexes. In this process, it was found that during the period of flat peak and peak transition, while the regional average traffic density changes, the uniformity of traffic density first decreases and then increases. The traffic density uniformity of the signal control area can be improved by controlling the size of the signal control area. We obtained the empirical value range of the regional control value k by comparing the values of multiple groups of measurement data k and evaluation indexes. Then, we compared them with the two kinds of traditional partition algorithms and improved multiple dichotomy algorithms. Our method improves road network balance by 5% over existing methods.

Effects of the manual and electronic toll collection systems on the particulate pollutant levels on highways in Taiwan

Few studies have quantified the effects of particulate air pollution reductions on electronic toll collection (ETC) and manual toll collection (MTC). Our study evaluated the levels of particulate pollutants, including particulate polycyclic aromatic hydrocarbons (p-PAHs), PM1.0, PM2.5, and PM10, from MTC and ETC systems in booths at a manual/electric hybrid toll station on a highway in Taiwan. A two-week sampling campaign was conducted at the toll station on the first highway in the Taipei metropolitan area. Indoor tollbooth monitoring samples were collected for the ETC and MTC lanes to assess the hourly p-PAH, PM1.0, PM2.5, and PM10 concentrations. The hourly average particle concentration was calculated for different types of toll collection based on 100 vehicles per hour. The average traffic density in the truck and bus lanes or car and van lanes with ETC was significantly higher than that with MTC. The mean p-PAH, PM1.0, and PM2.5 concentrations in the car and van lanes with MTC were higher than those for ETC. The increases in PM1.0 and PM2.5 concentrations per 100 vehicles in the bus and truck lanes with MTC were approximately 2 times greater than those for ETC. ETC technologies serve as a solution to traffic delay problems at toll stations in Taiwan and may more homogeneously reduce the levels of p-PAHs, PM1.0, and PM2.5 particulate pollutants from traffic emissions than the MTC along highways.

A methodology for identifying critical links and estimating macroscopic fundamental diagram in large-scale urban networks

The Macroscopic Fundamental Diagram (MFD), which exhibits the relationship between average flow and average density of an urban network, is a promising framework for monitoring and controlling urban traffic networks. Given that monitoring resources (e.g. loop detectors, probe vehicle data, etc.) are limited in real-world networks, acquiring adequate data to estimate an MFD is of crucial importance. This study presents a novel, network-wide approach to identifying critical links and estimating average traffic flow and density. The proposed model estimates the MFD using flow and density measurements from those critical links, which constitute only a small subset of all the links in the network. To find the critical links, we rely on historical probe vehicle data, and propose a model that builds on Principal Component Analysis (PCA), a dimensionality reduction and a feature selection method. Essentially, using PCA, a large number of possibly interrelated variables in a dataset can be reduced to a set of smaller uncorrelated variables, while maintaining as much information as possible in the dataset. The resulting uncorrelated variables, or the principal components, indicate the major patterns or the dominating features of the original dataset. Additionally, PCA enables the (approximate) reconstruction of the full-scale dataset from the selected features (or principal components). In this work, we apply PCA in order to identify the main traffic features from a probe vehicle dataset; then, we find the links that are associated with these features (i.e., critical links); then, we locate loop detectors on those links to collect flow and density data; and finally, we reconstruct the full-scale data, building on the PCA mechanism. This gives us the flow and density of all links, from which we can effectively estimate the MFD.

Optimization based routing model for the dynamic path planning of emergency vehicles

With the increasing number of traffic vehicles and urbanization, the frequencies of traffic accidents are growing exponentially. The research for routing emergency vehicles has gained significant attention in Vehicular Ad hoc Network (VANET) topology. The path planning for an emergency vehicle can avoid congestion and minimize the travel time to prevent accidents. Thus, this work proposes a dynamic path planning scheme for routing emergency vehicles. At first, the VANET is simulated and the dynamic path planning is performed after the arrival of emergency vehicles. The proposed dynamic path planning includes two steps, namely travel-time forecasting, and bi-model routing. The travel time is predicted using the Support Vector Regression model and the bimodal is employed to route the emergency vehicles optimally. The bimodal consists of two steps, such as K-path discovery and optimal path selection process. Initially, the possible K shortest path is detected from which the optimal path is selected by the proposed Exponential Bird Swarm Optimization Algorithm (Exp-BSA). The proposed Exp-BSA algorithm is designed by integrating the Exponentially Weighted Moving Average concept in Bird Swarm Algorithm (BSA). Thus, the proposed Exp-BSA determines the optimal paths based on the devised fitness function, and the selected paths are suggested to emergency vehicles for faster routing. The performance of the proposed dynamic path planning for an emergency vehicle based on Exp-BSA is evaluated using three simulation setups based on the metrics, such as distance, average traffic density, average speed, and travel time. Also, the performance of the proposed method is compared with the existing methods, such as SFLA-KC, K-Path, and BSA. From the results, it is exposed that the proposed Exp-BSA method achieves the overall best performance than the existing methods with a minimal distance of 48.404 m, an average traffic density of 0.015 min, an average speed of 1.392 m/s, travel time of 19.608 s.

Methodology to Evaluate the Quality of Service of Traffic Flow on Intercity Expressway Sections by using Follower Percentage

This study estimated composite headway distributions consisting of follower and non-follower headway elements and used the follower percentage obtained as the estimated parameters of those distributions to evaluate the quality of service (QOS) of traffic flow on Japanese intercity expressways under uncongested conditions. Analysis of pulse data obtained by vehicle detectors at multiple points with differing geometric structures showed that follower percentage is influenced by lane traffic volume, vehicle pair, and lane operation. Use of follower percentage also enabled clear and quantitative comparison and evaluation of the QOS of traffic flow for different lane operation formats, which could not be adequately expressed by such conventional macroscopic indices as average speed and traffic density. This indicates that follower percentage is a suitable performance measure for evaluating the QOS of traffic flow.

Optimization driven cellular automata for traffic flow prediction at signalized intersections

The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed.

Calculation Method of Congestion Metrics based on Mobile Phone Signaling

With the increasingly serious congestion of the highway, it is very important to get the accurate traffic jam by the means of information technology. Under the condition of selecting the speed and traffic density as the congestion measure parameters, in order to effectively improve the accuracy of the results, we propose to eliminate the interference of off-highway vehicle users, adopt the method of the average vehicle mobile phone number and weighted speed calculated average traffic density and road speed, and describes its implementation process in detail. Finally, the results are compared with the data of the fixed detector on the highway, which indicates that the method proposed in this paper can meet the design requirements.

Adaptive offsets for signalized streets

This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy’s impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data – they only require average traffic density readings and no demand forecasts.A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simultaneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street.It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10 to 0%, and the average duration of a trip from 216 to 181 s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23 to 0% and the average trip duration from 2037 to 1143s.

Multi-section cellular automata model of traffic flow

It is more common for drivers to pass through multiple sections to reach destinations instead of single road section. Howerver, most of researches concentrate on improving the effect in an independent section. Based on traditional cellular automata traffic model, a multi-section model is proposed by regarding serverl road sections as a traffic system. In this model, different sections of the road might have different lengths, numbers of lanes or maximal speeds. And vehicles travel from one section to another. The main difficulty lies in dealing with the relationships among the traffic flows of different sections. Besides basic rules in NaSch model, the vehicle inflow rule, crossroad randomization brake rule and crossroad inflow rule is added in this paper to enable vehicles to flow between sections. At the beginning of section, to avoid conflicting at crossroads under open boundary condition, the concept of car pool is introduced when new vehicles enter into sections. Before arriving at the end of section, crossroad randomization brake is used to simulate the influences of crossroads. Speed decreases in probability until lower than a maximal crossroad speed. When leaving the section, vehicles go to the next section with a straight ratio. Also, new vehicles may enter according to traffic condition. Therefore, cellular automata of different sections can be connected in series.Finally, numerical simulation is demonstrated to study the influences of important parameters, including traffic inflow probability, maximal crossroad speed and crossroad randomization brake probability. Compared with traditional models, this model focuses on connecting sections. And improvements of basic models can be implanted easily, thereby increasing the accuracy of the whole model in the future. The experimental result are as follows. 1) According to space-time graphs of different inflow probabilities, there is a new kind of traffic flow called mixed flow. Traffic congestion often starts from crossroads, and spreads to the whole section. And traffic jams in previous section might relieve traffic pressure in latter section. 2) With the increase of traffic inflow probability, crossroads tends to have a greater influence on average speed as well as average traffic density. What is more, the moderate increase of vehicle numbers could cause the road capacity to drop rapidly if it exceeds the threshold value.

Adaptive offsets for signalized streets

Abstract: This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy’s impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data - they only require average traffic density readings and no demand forecasts. A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simulataneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street. It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10% to 0%, and the average duration of a trip from 216s to 181s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23% to 0% and the average trip duration from 2037s to 1143s.

A distributed framework for network-wide traffic monitoring and platoon information aggregation using V2V communications

This study explores an innovative framework for distributed traffic monitoring and information aggregation using vehicle-to-vehicle (V2V) communications alone. We envision the proposed framework as the foundation to an alternative or supplemental traffic operation and management system, which could be particularly helpful under abnormal traffic conditions caused by unforeseen disasters and special events. Each equipped vehicle, through the distributed protocols developed, keeps track of the average traffic density and speed within a certain range, flags itself as micro-discontinuity in traffic if appropriate, and cross-checks its flag status with its immediate up- and down-stream vehicles. The micro-discontinuity flags define vehicle groups with similar traffic states, for initiating and terminating traffic information aggregation. The framework is validated using a microscopic traffic simulation platform VISSIM and its built-in component object model. Vehicle groups are successfully identified and their average speed and density effectively estimated. The impact of market penetration rate (MPR) is also investigated with a new methodology for performance evaluation under multiple traffic scenarios. Our simulation results show that the proposed framework lends itself better to more congested traffic conditions for any given MPR. With 50% MPR, the framework is able to provide information coverage for at least 37.76% of the simulated roadway facility under various traffic scenarios. This indicates that proposed framework could be useful with an MPR as low as 50%. Even with an MRP of 20%, the coverage ratio, under relatively congested traffic, can still reach around 58.82%. The framework is able to provide accurate speed estimation at high spatial resolution for the simulated roadway facility. The maximum relative error is under 10% for relatively congested traffic even with MPR as low as 20%. When there is a wider range of speed distribution (less congested traffic), the worst-case maximum relative error is still under 15% when MPR=20%. The density estimation is more sensitive to MPR, and is more accurate under low demand and high MPR scenarios. As expected, the accuracy of both speed and density estimation increases with MPR for any given traffic scenario.

Refined Statistical Analysis of Evolution Approaches for Wireless Networks

To meet future traffic and data rate demands, operators have the opportunity to select between a variety of network evolution approaches. This paper presents a new method for evaluating such approaches, in a simple yet operator specific way. The novelty of the method lies in that, with limited complexity, it takes into account not only conventional dimensioning parameters like targeted user experience, average traffic demand, user density and cell sizes, but also three dimensional spatial user and traffic distribution, site specific propagation, and deployment strategies. This is enabled through the use of an explicit city model, capturing the main characteristics of the operators environment. Applying the method to two examples of different cities illustrates the influence of the refinement parameters and their impact on the benefits of two evolution approaches: macro densification and deployment of small cells.

Using new satellite based exposure methods to study the association between pregnancy pm2.5 exposure, premature birth and birth weight in Massachusetts

BackgroundAdverse birth outcomes such as low birth weight and premature birth have been previously linked with exposure to ambient air pollution. Most studies relied on a limited number of monitors in the region of interest, which can introduce exposure error or restrict the analysis to persons living near a monitor, which reduces sample size and generalizability and may create selection bias.MethodsWe evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM2.5) levels during pregnancy in Massachusetts for a 9-year period (2000–2008). Building on a novel method we developed for predicting daily PM2.5 at the spatial resolution of a 10x10km grid across New-England, we estimated the average exposure during 30 and 90 days prior to birth as well as the full pregnancy period for each mother. We used linear and logistic mixed models to estimate the association between PM2.5 exposure and birth weight (among full term births) and PM2.5 exposure and preterm birth adjusting for infant sex, maternal age, maternal race, mean income, maternal education level, prenatal care, gestational age, maternal smoking, percent of open space near mothers residence, average traffic density and mothers health.ResultsBirth weight was negatively associated with PM2.5 across all tested periods. For example, a 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy was significantly associated with a decrease of 13.80 g [95% confidence interval (CI) = −21.10, -6.05] in birth weight after controlling for other factors, including traffic exposure. The odds ratio for a premature birth was 1.06 (95% confidence interval (CI) = 1.01–1.13) for each 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy period.ConclusionsThe presented study suggests that exposure to PM2.5 during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.

Distribution of the concentration of heavy metals associated with the sediment particles accumulated on road surfaces

The heavy metal pollution caused by road run‐off water constitutes a problem in urban areas. The metallic load associated with road sediment must be determined in order to study its impact in drainage systems and receiving waters, and to perfect the design of prevention systems. This paper presents data regarding the sediment collected on road surfaces in the city of Torrelavega (northern Spain) during a period of 65 days (132 samples). Two sample types were collected: vacuum‐dried samples and those swept up following vacuuming. The sediment loading (g m−2), particle size distribution (63–2800 µm) and heavy metal concentrations were determined. The data showed that the concentration of heavy metals tends to increase with the reduction in the particle diameter (exponential tendency). The concentrations of Pb, Zn, Cu, Cr, Ni, Cd, Fe, Mn and Co in the size fraction <63 µm were 350, 630, 124, 57, 56, 38, 3231, 374 and 51 mg kg−1, respectively (average traffic density: 3800 vehicles day−1). By increasing the residence time of the sediment, the concentration increases, whereas the ratio of the concentration between the different size fractions decreases. The concentration across the road diminishes when the distance between the roadway and the sampling siteincreases; when the distance increases, the ratio between size fractions for heavy metal concentrations increases. Finally, the main sources of heavy metals are the particles detached by braking (brake pads) and tyre wear (rubber), and are associated with particle sizes <125 µm.

A MACHINE LEARNING FRAMEWORK FOR REAL-TIME TRAFFIC DENSITY DETECTION

Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.

Lead pollution in urban roadside environments of Dar es Salaam city

Lead is among the most toxic elements in nature. It is non-biodegradable and its toxicity does not change with time. Use of leaded gasoline in motor vehicles is known as the major source of lead pollution in cities in the world. Dar es Salaam, the main city of Tanzania, has thousands of cars traveling along its roads. The lead contaminating urban roadside environments of Dar es Salaam was determined. Soil and vegetation samples were collected at various distances from the road edges of the New Bagamoyo, Sam Nujoma and Mandela roads in Dar es Salaam and analyzed for lead using Atomic Absorption Spectrophotometric methods. The results show that the soils and grasses along these major highways of Dar es Salaam were contaminated with lead. The highest lead contents (152.5 ppm in soil and 35.7 ppm in couch grass) were found in soils and vegetation within 1 to 5 meters from the road edge. The background lead levels measured at 150 meters from the road side were less than 1.2 ppm in both soil and vegetation. The lead levels along these roads exhibited strong correlation with the average traffic density (r = 0.912 for soil and r = 0.88 for grass). The levels of lead decreased exponentially with increasing distance away from the road. This suggests the major source of lead contamination of these areas was emissions from automobile traffic on these highways. The high levels of lead at 0 – 10 m from the roadside edge also suggest that soil and vegetation close to the roadside were a potential health hazard to both humans and animals. Tanzania Journal of Science Vol. 32 (2) 2006: pp. 61-68

Are Frequent Asthma Symptoms Among Low-Income Individuals Related to Heavy Traffic Near Homes, Vulnerabilities, or Both?

To investigate whether frequent asthma symptoms (daily/weekly symptoms) can be attributed to traffic-related exposures, poverty, and/or vulnerabilities. Annual average daily vehicle traffic density (TD) within 500 feet of 2001 California Health Interview Survey respondents' residential cross streets was calculated. Logistic regression analyses were performed to evaluate the association between annual average TD and frequent asthma symptoms with income as a confounder and a modifier. After adjustment for age, sex, and race/ethnicity, a 92% increase in frequent asthma symptoms was observed among those in high TD and a 50% increase among those in medium TD compared to those in low TD. Adjustment for poverty did not change these estimates, but individuals in poverty were twice more likely to experience frequent symptoms. Furthermore, delays in care, a poor/fair health status, current/previous smoking, overweight/obese, or unemployment showed independent associations with frequent asthma symptoms. Analyses stratified on income indicated greater estimated traffic effects for asthmatics in poverty, whereas the estimates for asthmatics above the poverty level moved closer toward the null. Traffic-related exposures, poverty and vulnerabilities all increased the risk of frequent asthma symptoms. Those in poverty appeared to be more strongly affected by heavy traffic near their residences.