Impact of land use, travel behavior, and socio-economic characteristics on carbon emissions in cool-climate cities, USA.

US cities of cool-climate zone such as Chicago and Boston are witnessing a reduction in carbon emissions potentially due to promoting public transportation and alternative energy resources. It is difficult to validate or deny optimal integration between land-use practices and transportation policies in mitigating carbon emissions due to the lack of urban comparative studies among metropolitan areas. Therefore, this research aims to examine the relationship between land use, travel behavior, and socio-economic characteristics related to carbon dioxide emissions at the zip code level. The research tends to investigate the carbon emissions in four metropolitan regions in cool climatic zone 5 compared to the carbon emissions in all US zip codes, to generate benchmarking predictive models. To this end, nine regression models were developed in this research. These include the US data model, zone 5 model, zone 5 cities model, zone 5 metropolitan areas model, zone 5 micropolitan areas model, Boston model, Chicago model, Columbus model, and Detroit model considering 14 independent variables. The nine models were calibrated and evaluated to include the statistically significant variables having the expected logical sign and acceptable values for t-statistic and multicollinearity. The adjusted R2 values vary between 0.62 and 0.91, where Boston, Chicago, Columbus, and Detroit models are statistically better than other models. The results indicate that the policies that can be adopted to reduce carbon emissions vary among the models.

Development of response surface method prediction model for traffic-related roadside noise levels based on traffic characteristics.

Recently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics' ranges to reduce the expected noise levels. The results indicate that the rate of Leq increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that Leq has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R2 = 95.87% and R2 adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.