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Travel demand management measures/policies are important to sustain positive changes among individuals' travel behaviour. An integrated agent-based microsimulation platform provides a rich framework for examining such interventions to assess their impacts using indicators about demand as well as supply side. This paper presents an approach where individual schedules, derived from a lighter version of an activity-based model, are fed into a Multi-Agent Transport Simulation (MATSIM) framework. Simulations are performed for two European cities i.e. Hasselt (Belgium) and Bologna (Italy). After calibrating the modelling framework against aggregate traffic counts for the base case, the impacts of a few traffic management policies (restricting car access, increase in bus frequency) are examined. The results indicate that restricting car access is more effective in terms of reducing traffic from the network and also shifting car drivers/passengers to other modes of travel. The enhancement of bus infrastructure in relation to increase in frequency caused shifting of bicyclist towards public transport, which is an undesirable result of the policy if the objective is to improve sustainability and environment. In future research, the framework will be enhanced to integrate emission and air dispersion models to ascertain effects on air quality as a result of such interventions. [ABSTRACT FROM AUTHOR]

Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual's mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention. [ABSTRACT FROM AUTHOR]

Motivated by the increasing interest on passive control solutions to lower pollutant concentrations in cities, this paper introduces a novel methodology to demonstrate the potential of photocatalytic coatings in abating air pollution in real-world urban environments. The methodology introduced in this paper is based on an original application of Computational Fluid Dynamic (CFD) modelling to simulate the effect of photocatalytic coatings in real yet simplified urban setting. The numerical approach is validated against observations gathered during an ad-hoc designed intensive experimental campaign performed in a real urban area in the city of Bologna, Italy (44.5075 N, 11.3514E), under semi-controlled conditions. Comparison of the model output with observations show a concentration reduction in the range 10–20%. After validation and choice of the proper model set-up, numerical simulations are analyzed by focusing on the mechanisms enhancing the flow circulation within the canyon, an effect that may increase the effect of coatings within street canyons. Results show that application of photocatalytic coatings can give pollutant reductions up to 50% in a confined region close to the walls. A parametrization for the pollutant reduction within the street canyon is suggested to summarize these results, providing a characterization of the photocatalytic coatings performances as a function of the geometric characteristic of the street canyon. • Photocatalytic coatings are promising solutions to reduce NOx in urban environment. • The CFD can quantify the NOx photocatalytic absorption close to the building walls. • A reaction velocity can be determined to model the NO photocatalytic absorption. • Few experiments in the literature measure the effect of photocatalytic coatings. • Street canyon environment fosters the absorption of NOx as air is confined inside it. [ABSTRACT FROM AUTHOR]

Vegetation in the urban environment has various impacts on microclimate. While optimal strategies for investigating these impacts are the subject of ongoing research, most approach rely on Computational Fluid Dynamics (CFD) simulations. We evaluate mean wind and turbulence fields simulated using the fast-running Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System, a simplified CFD tool that resolves buildings and vegetation. We use QUIC to investigate the role of deciduous trees in modifying the airflow of a real neighbourhood in Bologna, Italy by running large ensembles of simulations per case study, obtained by varying the input wind direction. This approach can minimise intrinsic model uncertainty as well as uncertainties associated with real environments. Model validation is performed using measurements from an experimental field campaign focused on a vegetated urban street canyon in Bologna. Ensemble simulation results show good agreement with the experimental data for various conditions (i.e., simulation ensembles overlap experimental variability in most cases). The role of trees is investigated by comparing simulations with and without trees. Trees are found to reduce airflow by constraining local circulation and reducing turbulence intensity. Finally, the combined effect of building morphology and vegetation is investigated by adopting a formalism to represent the presence of vegetation using area-fraction coefficients. An area-fraction coefficient threshold of 0.225 has been identified that separates flow behaviours and is present in cases with and without vegetation. Below this threshold, a constant wind and turbulence regime exists, while above the threshold, winds and turbulence vary with area-fraction coefficient. • Ensemble simulation methodology to evaluate and validate mean and turbulence fields. • Probability distribution-based methodology to evaluate the impact of vegetation. • Morphological analysis using area-fraction distributions that include vegetation. • Vegetation constrains and reduces the intensity of the local circulation. • Two flow regimes are identified are identified as function of morphology density. [ABSTRACT FROM AUTHOR]

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30′ N, 11°21′ E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution. [ABSTRACT FROM AUTHOR]