Predicting Real-Time Roadside CO and $\hbox{NO}_{2}$ Concentrations Using Neural Networks

Zito, Pietro; Chen, Haibo; Bell, MC

doi:10.1109/tits.2008.928259

Cited by 31 publications

(19 citation statements)

References 16 publications

(9 reference statements)

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“…Morning and afternoon periods are regarded as peak traffic periods while midday is identified as the offpeak period. The actual pollution level varies remarkably at fine-time scales around intersections (Zito et al, 2008), and thus PM 2.5 (mg/m 3 ) and CO (ppm (parts per million)) were measured at minute level in this study. Two sets of portable monitors were used to detect both pollutants at roadside and setbacks.…”

Section: Data Collectionmentioning

confidence: 99%

“…Consequently, there are still some challenges to reach agreements on how these correlated factors affect street-scale pollutant variation in a statistical sense. Moreover, as the traffic-related emission intensity and local meteorology are variable, air pollutants around an intersection can significantly vary at minute scale (Zito et al, 2008;Galatioto and Zito, 2009;Soulhac et al, 2009;Tiwary et al, 2011;He and Lu, 2012). Studies further found that short-term exposures (e.g., minutes) to high pollutant levels are even more serious compared to long-term general exposures (e.g., annual, monthly, daily) (Brook et al, 2002;Grivas and Chaloulakou, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

Wang

et al. 2017

Front. Earth Sci.

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The minute-scale variations of fine particulate matter (PM 2.5 ) and carbon monoxide (CO) concentrations near a road intersection in Shanghai, China were investigated to identify the influencing factors at three traffic periods. Measurement results demonstrate a synchronous variation of pollutant concentrations at the roadside and setbacks, and the average concentration of PM 2.5 at the roadside is 7% (44% for CO) higher than that of setbacks within 500 m of the intersection. The pollution level at traffic peak periods is found to be higher than that of off-peak periods, and the morning peak period is found to be the most polluted due to a large amount of diesel vehicles and unfavorable dispersion conditions. Partial least square regressions were constructed for influencing factors and setback pollutant concentrations, and results indicate that meteorological factors are the most significant, followed by setback distance from the intersection and traffic factors. CO is found to be sensitive to distance from the traffic source and vehicle type, and highly dependent on local traffic conditions, whereas PM 2.5 originates more from other sources and background levels. These findings demonstrate the importance of localized factors in understanding spatiotemporal patterns of air pollution at intersections, and support decision makers in roadside pollution management and control.

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Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

Wang

et al. 2017

Front. Earth Sci.

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“…Different neural network topologies are also considered. The performance of three different neural network topologies (MLP, RBF and Modular-ANN) for predicting roadside concentrations of CO and N O 2 in Melton Mowbray, UK, are compared in [19]. The transferability of these models is then considered by applying them to data from another site in Leicester, UK, concluding that whilst performance is degraded, it is still able to provide a useful prediction capability.…”

Section: Air Quality Forecastingmentioning

confidence: 99%

Infering Air Quality from Traffic Data Using Transferable Neural Network Models

Molina‐Cabello

Passow

Domínguez

et al. 2019

Advances in Computational Intelligence

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This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.

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“…Prediction of real-time roadside CO and NO 2 concentrations using multilayer perceptrons, modular and RBF networks is the subject of [8]. All networks are trained on traffic and meteorological data available for a road junction in the UK, then tested on another intersection with similar traffic patterns and weather.…”

Section: Offboard Applicationsmentioning

confidence: 99%

Overview of CI research in automotive ITS

Prokhorov¹

2011

2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings

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Illustrative examples of applications of the CI methods in ITS are overviewed. They include off-board and on-board applications. On-board object recognition is further discussed from the standpoint of making a more reliable system for ITS applications. I. INTRODUCTIONComputational intelligence (CI) traditionally includes neural networks (NN), fuzzy logic systems (FS) and evolutionary computations (EC) as three main areas distinguishing CI in terms of employed methods from classical AI. The difference between CI and AI is blurred because both the IEEE Computational Intelligence Society and the Association for the Advancement of Artificial Intelligence have sometimes been employing essentially the same methods to attack same challenges.Instead of categorizing by methods, the division by application is accepted which is more useful for applied research. In this paper ITS applications of CI are separated in two large categories:1) On-board: inward (driver or vehicle) focused and outward (environment) focused, 2) Off-board: infrastructure based including communications and Vehicle-Infrastructure Integration (VII).The on-board applications are exemplified in [1], [2], which include but are not limited to driver behavior assessment (drowsiness, intentions, etc.), object recognition, vehicle health monitoring and intelligent control.The infrastructure applications are exemplified in [3], [4], [5], which include but are not limited to remote counting of road objects, recognizing license plates and other vehicle properties.In this paper traditional CI applications in ITS still in the research phase are illustrated. The state of art in object recognition is then summarized. It is also discussed what combining multiple sensors, or sensor fusion, could bring to the table and what perhaps is not so important for the ITS area. This paper is organized as follows. The next section briefly overviews illustrative off-board applications of CI in ITS and system architectures. Section III briefly overviews on-board applications. Section IV discusses in more details object recognition applications and their specifics for ITS, as well as overviews some experimental results, followed by Conclusion.

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Predicting Real-Time Roadside CO and $\hbox{NO}_{2}$ Concentrations Using Neural Networks

Cited by 31 publications

References 16 publications

Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

Infering Air Quality from Traffic Data Using Transferable Neural Network Models

Overview of CI research in automotive ITS

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