Tânia Fontes

Assessment of potential improvements on regional air quality modelling related with implementation of a detailed methodology for traffic emission estimation

The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power - VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM10, NOx and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM10, CO and O3. For NO2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM10, CO and O3.

Can artificial neural networks be used to predict the origin of ozone episodes

Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere-Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model - the Multilayer Perceptron - is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, (7)Be activity and meteorological conditions were used. With this model, 2-7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65-0.92). Precision and F1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures.

Empirical assessment of route choice impact on emissions over different road types, traffic demands, and driving scenarios

ABSTRACT Eco-routing has been shown as a promising strategy to reduce emissions. However, during peak periods, with limited additional capacity, the eco-friendliness of various routes may change. We have explored this issue empirically by covering about 13,300 km, in three different areas, using GPS-equipped vehicles to record second-by-second vehicle dynamics. This study has confirmed the importance of the eco-routing concept given that the selection of eco-friendly routes can lead to significant emissions savings. Furthermore, these savings are expected to be practically unchanged during the peak period. However, some potential negative externalities may arise from purely dedicated eco-friendly navigation systems.

Traffic restriction policies in an urban avenue: A methodological overview for a trade-off analysis of traffic and emission impacts using microsimulation

ABSTRACT Urban traffic emissions have been increasing in recent years. To reverse that trend, restrictive traffic measures can be implemented to complement national policies. We have proposed a methodology to assess the impact of three restrictive traffic measures in an urban arterial by using a microsimulation model of traffic and emissions integrated platform. The analysis is extended to some alternative roads and to the overall network area. Traffic restriction measures provided average reductions of 45%, 47%, 35%, and 47% for CO2, CO, NOX, and HC, respectively, due to traffic being diverted to other roads. Nevertheless, increases of 91%, 99%, 55%, and 121% in CO2, CO, NOX, and HC, respectively, can be expected on alternative roads.

Integrated computational methods for traffic emissions route assessment

This paper focuses on the integration of multiple computational tools towards the objective of assessing emission impacts of different routes. Data from real life GPS tracks was integrated with traffic emission modelling for multiple pollutants (NOx, HC, CO and PM10) to investigate different routing strategies. The main conclusion is that different pollutants dictate different best routes. Hence, strategies for assigning relative weights to pollutants are devised in order to be able to select the best environment-friendly route.

Application of Artificial Neural Networks to Predict the Impact of Traffic Emissions on Human Health

Artificial Neural Networks (ANN) have been essentially used as regression models to predict the concentration of one or more pollutants usually requiring information collected from air quality stations. In this work we consider a Multilayer Perceptron (MLP) with one hidden layer as a classifier of the impact of air quality on human health, using only traffic and meteorological data as inputs. Our data was obtained from a specific urban area and constitutes a 2-class problem: above or below the legal limits of specific pollutant concentrations. The results show that an MLP with 40 to 50 hidden neurons and trained with the cross-entropy cost function, is able to achieve a mean error around 11%, meaning that air quality impacts can be predicted with good accuracy using only traffic and meteorological data. The use of an ANN without air quality inputs constitutes a significant achievement because governments may therefore minimize the use of such expensive stations.

Assessment of Corridors with Different Types of Intersections: Environmental and Traffic Performance Analysis

Recently, roundabouts in a series have been installed along corridors to enhance road safety. However, the benefits of this traffic-calming technique on traffic performance and pollutant emissions compared with other forms of intersections, such as traffic lights and stop-controlled solutions, are not properly known. This study used a microscopic approach to evaluate the effects of a corridor with four roundabouts on traffic performance and emissions, in comparison with traffic lights and stop-controlled solutions. Average travel time and number of vehicle stops were used as measures of traffic performance; carbon dioxide, monoxide carbon, nitrogen oxides, hydrocarbons, and particulate matter were used to quantify emissions. The traffic and emissions performance of each solution was evaluated on three levels: (a) arterial, (b) intersection, and (c) morning peak versus evening peak periods. It was found that, regardless of the time period, traffic lights in corridors at the arterial level produced higher total emissions (> 6%), while stop-controlled intersections produced lower emissions (≈12%) compared with roundabouts, mainly because of unbalanced traffic flows between main and minor roads. The results for traffic performance showed advantages in implementing roundabouts when the main concern was the number of vehicle stops. At the intersection level, an emissions improvement (between 2% and 14%) was observed at traffic lights on four-leg intersections.

Multicriteria Assessment of Crosswalk Location in Urban Roundabout Corridors

Midblock pedestrian crossing areas between closely spaced roundabouts can affect traffic operations and may result in a trade-off between capacity, environment, and safety benefits. Even though research has been conducted on the impacts of traffic performance on pedestrian crosswalks located at isolated roundabouts, few studies have focused on how pedestrian crosswalks between closely adjacent roundabouts affect traffic operations. A microsimulation approach was used to examine the integrated effect of a pedestrian crosswalk on traffic delay, carbon dioxide emissions, and relative speed between vehicles and pedestrians at different locations between closely spaced two-lane roundabouts. The main purpose of the study was to develop a simulation platform of traffic (VISSIM), emissions (vehicle-specific power), and safety (surrogate safety assessment model) to optimize such variables. The fast nondominated sorting genetic algorithm NSGA-II was mobilized to identify an optimized set of pedestrian crosswalk locations for the roundabout exit section along the midblock segment. One acceptable solution that provided a good balance between traffic performance, emissions, and pedestrian safety benefits was locating the crosswalks at 15, 20, and 30 m from the exit section. Even at low pedestrian demand, crosswalk effectiveness (as determined by capacity and environment) gradually decreased near the circulatory ring delimitation (<10 m). Findings suggest that crosswalks in the midblock segment (55 to 60 m from the exit section) also must be considered, especially under high traffic demand.

Integrated indicator to evaluate vehicle performance across: safety, fuel efficiency and green domains

In general, car manufacturers face trade-offs between safety, efficiency and environmental performance when choosing between mass, length, engine power, and fuel efficiency. Moreover, the information available to the consumers makes difficult to assess all these components at once, especially when aiming to compare vehicles across different categories and/or to compare vehicles in the same category but across different model years. The main objective of this research was to develop an integrated tool able to assess vehicles performance simultaneously for safety and environmental domains, leading to the research output of a Safety, Fuel Efficiency and Green Emissions (SEG) indicator able to evaluate and rank vehicles performance across those three domains. For this purpose, crash data was gathered in Porto (Portugal) for the period 2006-2010 (N=1374). The crash database was analyzed and crash severity prediction models were developed using advanced logistic regression models. Following, the methodology for the SEG indicator was established combining the vehicles safety and the environmental evaluation into an integrated analysis. The obtained results for the SEG indicator do not show any trade-off between vehicles safety, fuel consumption and emissions. The best performance was achieved for newer gasoline passenger vehicles (<5year) with a smaller engine size (<1400cm(3)). According to the SEG indicator, a vehicle with these characteristics can be recommended for a safety-conscious profile user, as well as for a user more interested in fuel economy and/or in green performance. On the other hand, for larger engine size vehicles (>2000cm(3)) the combined score for safety user profile was in average more satisfactory than for vehicles in the smaller engine size group (<1400cm(3)), which suggests that in general, larger vehicles may offer extra protection. The achieved results demonstrate that the developed SEG integrated methodology can be a helpful tool for consumers to evaluate their vehicle selection through different domains (safety, fuel efficiency and green emissions). Furthermore, SEG indicator allows the comparison of vehicles across different categories and vehicle model years. Hence, this research is intended to support the decision-making process for transportation policy, safety and sustainable mobility, providing insights not only to policy makers, but also for general public guidance.

Prediction of Journey Destination in Urban Public Transport

In the last decade, public transportation providers have focused on improving infrastructure efficiency as well as providing travellers with relevant information. Ubiquitous environments have enabled traveller information systems to collect detailed transport data and provide information. In this context, journey prediction becomes a pivotal component to anticipate and deliver relevant information to travellers. Thus, in this work, to achieve this goal, three steps were defined: (i) firstly, data from smart cards were collected from the public transport network in Porto, Portugal; (ii) secondly, four different traveller groups were defined, considering their travel patterns; (iii) finally, decision trees (J48), Naive Bayes (NB), and the Top-K algorithm (Top-K) were applied. The results show that the methods perform similarly overall, but are better suited for certain scenarios. Journey prediction varies according to several factors, including the level of past data, day of the week and mobility spatiotemporal patterns.