ABSTRACT
Reducing emissions from the transport sector is one of the crucial countermeasures for climate action. This study focuses on the optimization and emission analysis regarding the impacts of left-turn lanes on the emissions of mixed traffic flow (CO, HC, and NOx) with both heavy-duty vehicles (HDV) and light-duty vehicles (LDV) at urban intersections, combining high-resolution field emission data and simulation tools. Based on high-precision field emission data collected by Portable OBEAS-3000, this study first develops instantaneous emission models for HDV and LDV under various operating conditions. Then, a tailored model is formulated to determine the optimal left-lane length for mixed traffic. Afterward, we empirically validate the model and analyze the effect of the left-turn lane (before and after optimization) on the emissions at the intersections using the established emission models and VISSIM simulations. The proposed method can reduce CO, HC, and NOx emissions crossing intersections by around 30% compared to the original scenario. The proposed method significantly reduces average traffic delays after optimization by 16.67% (North), 21.09% (South), 14.61% (West), and 2.68% (East) in different entrance directions. The maximum queue lengths decrease by 79.42%, 39.09%, and 37.02% in different directions. Even though HDVs account for only a minor traffic volume, they contribute the most to CO, HC, and NOx emissions at the intersection. The optimality of the proposed method is validated through an enumeration process. Overall, the method provides useful guidance and design methods for traffic designers to alleviate traffic congestion and emissions at urban intersections by strengthening left-turn lanes and improving traffic efficiency.
ABSTRACT
Cities worldwide adopted lockdown policies in response to the outbreak of coronavirus disease 2019 (COVID-19), significantly influencing people's travel behavior. In particular, micro-mobility, an emerging mode of urban transport, is profoundly shaped by this crisis. However, there is limited research devoted to understanding the rapidly evolving trip patterns of micro-mobility in response to COVID-19. To fill this gap, we analyze the changes in micro-mobility usage before and during the lockdown period exploiting high-resolution micro-mobility trip data collected in Zurich, Switzerland. Specifically, docked bike, docked e-bike, and dockless e-bike are evaluated and compared from the perspective of space, time and semantics. First, the spatial and temporal analysis results uncover that the number of trips decreased remarkably during the lockdown period. The striking difference between the normal and lockdown period is the decline in the peak hours of workdays. Second, the origin-destination flows are used to construct spatially embedded networks. The results suggest that the origin-destination pairs remain similar during the lockdown period, while the numbers of trips between each origin-destination pair is reduced due to COVID-19 pandemic. Finally, the semantic analysis is conducted to uncover the changes in trip purpose. It is revealed that the proportions of Home, Park, and Grocery activities increase, while the proportions of Leisure and Shopping activities decrease during the lockdown period. The above results can help planners and policymakers better make evidence-based policies regarding micro-mobility in the post-pandemic society.
