Kriging method application and traffic behavior profiles from local radar network database: A proposal to support traffic solutions and air pollution control strategies

The Economic Effects of Electromobility in Sustainable Urban Public Transport

“…Therefore, it is necessary to develop integrated and sustainable urban mobility policies. Transportation management strategies aimed at reduction of air pollution, according to Pinto et al [41], may contribute to building sustainable cities in the future.…”

The Effect of COVID-19 Pandemic on Emitted PM2.5 in Urban Road Networks: Using Loop Data and Kriging Method for Passenger Cars in the Central Part of the City of Lodz

“…Guttikunda et al (2019) reported that transportation is the major source of pollution in the Bengaluru region. Pinto et al (2020) modeled dispersion of the pollutions produced by the traffic flows and stated that air pollution is associated with vehicular movement in Belo Horizonte city in Brazil. It should be noted that although these studies depicted the connection between air pollution and vehicular movement, analyzing pollution emission of the traffic is problematic because of some reasons.…”

“…Basically, traffic data can be acquired by origin-destination survey, traffic modeling software, and traffic data prediction. The latter one combined with smart data (e.g., loop sensors and real-time location-based data) is an alternative and low-cost approach to the survey and commercial software application and can provide satisfactory inputs for vehicular emission analysis (Fu et al, 2017;Pinto et al, 2020). Spatiotemporal flow prediction can be implemented by the utilization of spatial analysis tools, namely, Kriging interpolations (Selby and Kockelman, 2011;Yang et al, 2018), Thiessen polygons (Gómez et al, 2018), geographical regressions (Song et al, 2018), spatial autoregressive models (Sun et al, 2018), and neural networks (Fu et al, 2017).…”

“…Spatiotemporal flow prediction can be implemented by the utilization of spatial analysis tools, namely, Kriging interpolations (Selby and Kockelman, 2011;Yang et al, 2018), Thiessen polygons (Gómez et al, 2018), geographical regressions (Song et al, 2018), spatial autoregressive models (Sun et al, 2018), and neural networks (Fu et al, 2017). Considering the potentials of the traffic prediction and spatial analysis tools for the vehicular emission analysis, recently, Pinto et al (2020) applied spatial analysis interpolation in a Euclidian space together with an air pollution model to analyze emissions and air pollution reduction strategies. However, it should be noted that the predictive spatial models are sensitive to the distance of entities scattered in the environment, and for variables such as traffic flow, network-based distances can be calculated as well as the Euclidian ones.…”

“…Since spatial analysis techniques provide useful, efficient, and low-cost tools for the estimation of flows in the space, this study aims at analyzing the effect of lockdowns imposed on the city of Lodz, the most congested urbanized area in Poland (TomTom, 2021), by computing flow of each link in its street network. In addition, due to the potential and availability of the smart data in the city of Lodz, the evaluation can be carried out at the hourly temporal resolution by testing both Euclidian and network-based spaces that have not been studied in the previous air pollution analysis research (e.g., Pinto et al (2020)).…”