Margarida C. Coelho

Generating Emissions Information for Route Selection: Experimental Monitoring and Routes Characterization

Infrastructure and traffic management technologies can have substantial impact on fuel use and emissions. This article explores a way to generate information about emissions and other route characteristics for drivers faced with a choice of routes. Global positioning system (GPS)-equipped vehicles were used to traverse various paths between origins and destinations to collect second-by-second trajectory data required for microscale emission analysis. A methodology based on the vehicle specific power (VSP) concept was used to estimate the emissions impact. On-board video footage recorded route features, traffic incidents, and congestion levels. Two different vehicles and drivers traversed several urban and intercity routes to enable the consideration of the influence of driver behavior and vehicle dynamics. It was found that the choice of a route can substantially affect emission rates of the analysed pollutants and that smoother driving styles can also result in considerable emissions reduction. A trade-off between reducing CO2/fuel consumption and local pollutants has been identified. Specifically, faster intercity routes are more desirable in terms of fuel use and CO2 emissions. However, these same routes yielded carbon monoxide, nitrous oxides, and hydrocarbons emission increases of up to 150%. These findings have implications for future investment and policy decisions regarding eco routing strategies.

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.

A Numerical Tool for Estimating Pollutant Emissions and Vehicles Performance in Traffic Interruptions on Urban Corridors

ABSTRACT The principal objective of this research was to develop and apply a traffic and emission decision support (TEDS) tool to urban highway corridors. This model simulates traffic while predicting time elapsed, energy consumed, and pollutants emitted to the atmosphere by vehicles on the corridor. Because emissions from internal combustion engines are very high during speed-change cycles, congested stop-and-go traffic can be very detrimental to local levels of pollution. Thus, the research was mainly focused on the existence and quantification of effects for local singularities (or traffic interruptions), such as pay tolls, roundabouts, and traffic signals within the corridor. This approach yielded a numerical predictive model, based on experimental measurements and concepts of traffic flow theory, which explains the interaction between the system operational variables of each traffic interruption and the environmental and traffic performance variables. In particular, the focus was on carbon monoxide, carbon dioxide, nitric oxide, and hydrocarbons emissions and their relation to queue length and stops. The proposed traffic and emission estimation models provide an overall pollution estimate for a traffic interruption under any control configuration and traffic demand pattern.

Turboroundabouts: Multicriterion Assessment of Intersection Capacity, Safety, and Emissions

A “turboroundabout” is a variation of the conventional multilane roundabout in which spiral road markings and raised lane dividers force drivers to follow a specific path according to their intended destination. This geometry eliminates weaving and cut-in conflicts by guiding drivers continuously from entry to exit. Turboroundabouts were conceived with the main aim of improving safety, but their practical benefits are relatively unknown. Likewise, the few existing studies on turboroundabouts do not allow definitive conclusions to be drawn about the delay and emissions performance characteristics of turboroundabouts; further research is needed. This research focused on the use of appropriate modeling methodologies to understand the effects of turboroundabouts on capacity, safety, and emissions in comparison with the effects of conventional single-lane and double-lane roundabouts. The results indicate that turboroundabouts have capacity levels comparable to those of two-lane roundabouts but are less robust concerning the directional split of the entry traffic; turboroundabouts lead to fewer traffic conflicts, but the traffic conflicts that do occur are more severe. The results also show that the implementation of turboroundabouts provides no advantages for emissions when the main concerns are carbon dioxide and oxides of nitrogen.

Emissions Estimation at Multilane Roundabouts: Effects of Movement and Approach Lane

This paper reports on research that explored how multilane roundabouts located on urban corridors have affected traffic performance and pollutant emissions generated from vehicles. The research also compared the emissions of vehicles moving through the roundabouts as they used either the left or right entry lanes. The methodology can be generalized to measure the emissions of any multilane roundabout. The paper identified a representative speed profile for each speed trajectory type, no stop, one stop, and multiple stops, from field data collected at four multilane roundabouts in Aveiro, Portugal. The vehicle-specific power emissions methodology was employed to estimate the second-by-second emissions generated from a vehicle during different acceleration–deceleration cycles. Congestion-specific vehicle speed profiles for two-lane roundabout approaches were used to develop regression models to predict the percentage of vehicles that would experience different speed trajectory types in the roundabout. The analysis tested hypotheses about how differences in the following characteristics have affected the amount of emissions generated from vehicles in each lane: (a) the speed profiles in each lane (left versus right), (b) the conflicting flows for the left and right lanes, (c) the lane flow, and (d) the overall congestion levels. Under low congestion levels, vehicles in the right lane emitted more pollutant because they had on average higher speed and sharper acceleration and deceleration rates. For high congestion levels, given equal flow rates for the left and right lanes, vehicles in the left lane produced more emissions because vehicles in the left lane experienced longer stop-and-go cycles and had different speed profiles than did vehicles in the right lane.

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.

Driving around turbo-roundabouts vs. conventional roundabouts: Are there advantages regarding pollutant emissions?

ABSTRACT This article addresses the impact of turbo-roundabouts located in urban areas on pollutant emissions using field measurements of vehicle activity data and road congestion levels. The research also compares the emissions of vehicles moving along a turbo-roundabout and a conventional multilane roundabout. Based on field measurements taken at turbo-roundabouts without curb dividers located in Grado, Spain, and multilane roundabouts in Aveiro, Portugal, three representative speed profiles for each speed trajectory were identified: no stop (I), stop once (II), and multiple stops (III). This study also develops discrete models for turbo-roundabouts and multilane roundabouts in which the relative occurrence of those speed profiles is expressed as a function of the entry and conflicting traffic flows. The vehicle specific power (VSP) methodology is then employed to estimate second-by-second pollutant emissions. This study tests the hypotheses that emissions are impacted by the differences in (1) the characteristics of speed profiles in each movement, (2) the volumes of entry and conflicting flows, (3) the overall saturation level, and (4) the transportation facility considered (turbo-roundabout / multilane roundabout). Considering the selected case studies and traffic demands, vehicles at turbo-roundabouts generated more emissions (15–22%, depending on the pollutant) than multilane conventional roundabouts, especially under medium and high congestion levels. These findings suggest that there are no advantages in implementing turbo-roundabouts from an environmental point of view, regardless of the traffic congestion levels.

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.