João Teixeira

The Potential of Metering Roundabouts: Influence in Transportation Externalities

Roundabouts are increasingly being used on busy arterial streets for traffic calming purposes. However, if one roundabout leg is near a distribution hub, for example, parking areas of shopping centers, the entry traffic volumes will be particularly high in peak hours. This paper investigated a partial-metering-based strategy to reduce traffic-related costs in a corridor. Specifically, the resulting traffic performance, energy, environmental, and exposure impacts associated with access roundabouts were studied in an urban commercial area, namely: (a) to characterize corridor operations in terms of link-specific travel time, fuel consumption, carbon dioxide and nitrogen oxides emissions, and noise costs; (b) to propose an optimization model to minimize these outputs; and (c) to demonstrate the model applicability under different traffic demand and directional splits combinations. Traffic, noise, and vehicle dynamics data were collected from a corridor with roundabouts and signalized intersections near a commercial area of Guimarães, Portugal. Microscopic traffic and emission modeling platforms were used to model traffic operations and estimate pollutant emissions, respectively. Traffic noise was estimated with a semi-dynamical model. Link-based cost functions were developed based on the integrated modeling structure. Lastly, a sequential quadratic programming-type approach was applied to find optimal timing settings. The benefit of the partial-metering system, in terms of costs, could be up to 13% with observed traffic volumes. The efficiency of the proposed system increased as entering traffic at the metered approaches increased (~7% less costs). The findings enable quantification of metering benefits near shopping areas.

Influence of different complexity levels of road traffic models on air quality modelling at street scale

Urban mobility accounts for 38 and 19% of nitrogen oxide (NOx) and particulate matter (PM) emissions at European urban areas, respectively. Despite of all the technological development around automobile industry, urban areas are still facing problems related to exposure to high levels of air pollutants. Increasing the accuracy of both emissions and air quality modelling from road traffic is a key-issue for the management of air pollution in road transport sector. This study assessed the influence of using different road traffic emission models on the accuracy of air quality modelling with street-level resolution, having as a case study an urban area located on the centre region of Portugal. Two emission models, with different complexity levels regarding the ability to characterise the traffic dynamics were analysed, namely, transport emission model for line sources (TREM) and vehicle-specific power (VSP), based on data obtained in an experimental campaign. To perform the air quality simulations, the pollutant dispersion in the atmosphere under variable wind conditions (VADIS) model was used and two pollutants were analysed: NOx and PM10. The results showed that the magnitude of PM10 and NOx concentrations were result of a conjoint influence of traffic dynamics and meteorological conditions. Comparison between measured and modelled data showed that the VADIS model could track the evolution of NOx levels, for both emission models considered, displaying a high correlation (> 0.8) between traffic-related NOx emissions and NOx concentrations. For PM10, VADIS model is more sensitive to the differences in the emissions calculation; however, it was observed that the traffic-related PM10 emissions accounts 1.3–8.4% to the PM10 concentration levels at the study area.

Statistical and Semi-Dynamical Road Traffic Noise Models Comparison with Field Measurements

The need for road traffic noise monitoring is growing in urban areas due to the growth of vehicles number and to the consequent increase of risk for human health. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels at any distance, knowing certain parameters needed as input of the RTNM. In this paper, the main statistical RTNMs are presented, together with the implementation of two innovative and advanced models: the EU suggested model (CNOSSOS-EU) and a research model presented by Quartieri et al. (2010). These models will be compared with noise measurements performed in different sites and with different traffic conditions, in order to avoid bias from geometry or other features of the area under study. The main conclusion is that the application of innovative models and the inclusion of dynamical information about traffic flow, will lead to better results with respect to statistical models.

Exploring crowdsourcing information to predict traffic-related impacts

Due to the increased public awareness on global climate change and other environmental problems, advanced strategies and tools are being developed and used to reduce the environmental impact of transport. The main objective of this paper is to explore the potential of using crowdsourcing information as an alternative or complementary source data to predict traffic-related impacts. Three main road connections to two important commercial areas in the city of Aveiro in Portugal, are examined. Driving patterns over different periods were collected using a probe vehicle equipped with a GNSS data logger and traffic volumes were counted during different days. The emissions estimation was based on the concept of Vehicle Specific Power (VSP), which has the capability to predict emissions during a trip through second-by-second vehicle dynamics data. Various tests were conducted in order to explore the potential correlations between these data sets and the information of the peak periods of a certain place that are provided by Google Maps. The findings of the study prove the potential of crowdsourcing information and shows that ICT technologies can be used to estimate emissions and traffic-related impacts.