An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

13 p.The global electric vehicle (EV) market has been experiencing an impressive growth in recent times. Understanding consumer preferences on this cleaner, more eco-friendly mobility option could help guide public policy toward accelerating EV adoption and sustainable transportation systems. Previous studies suggest the strong influence of individual and external factors on EV adoption decisions. In this study, we apply machine learning techniques on EV stated preference survey data to predict EV adoption using attitudinal factors, ridesourcing factors (e.g., frequency of Uber/Lyft rides), as well as underlying sociodemographic and vehicle factors. To overcome machine learning models’ low interpretability, we adopt the innovative Local Interpretable Model-Agnostic Explanations (LIME) method to elaborate each factor’s contribution to the predicting outcomes. Besides what was found in previous EV preference literature, we find that the frequent usage of ridesourcing, knowledge about EVs, and awareness of environmental protection are important factors in explaining high willingness of adopting EVs

Boosting the eco-friendly sharing economy: The effect of gasoline prices on bikeshare ridership in three U.S. metropolises

Transportation has become the largest CO2 emitter in the United States in recent years with low gasoline prices standing out from many contributors. As demand side changes are called for reducing car use, the fast-growing sharing economy shows great potential to shift travel demand away from single-occupancy vehicles. Although previous inter-disciplinary research on shared mobility has explored its multitudes of benefits, it is yet to be investigated how the uptake of this eco-friendly sharing scheme is affected by gasoline prices. In this study, we examine the impact of gasoline prices on the use of bikeshare programs in three U.S. metropolises: New York City, Boston, and Chicago. Using bikeshare trip data, we estimate the impact of citywide gasoline prices on both bikeshare trip duration and trip frequency in a generalized linear regression setting. The results suggest that gasoline prices significantly affect bikeshare trip frequency and duration, with a noticeable surge in short trips. Doubling gasoline prices could help save an average of 1933 gallons of gasoline per day in the three cities, approximately 0.04% of the U.S. daily per capita gasoline consumption. Our findings indicate that fuel pricing could be an effective policy tool to support technology driven eco-friendly sharing mobility and boost sustainable transportation

The dynamic evolution of multipoint interplanetary coronal mass ejections observed with BepiColombo, Tianwen-1, and MAVEN

We present two multipoint interplanetary coronal mass ejections (ICMEs) detected by the Tianwen-1 and Mars Atmosphere and Volatile Evolution spacecraft at Mars and the BepiColombo (0.56 au ∼0.67 au) upstream of Mars from 2021 December 5 to 31. This is the first time that BepiColombo is used as an upstream solar wind monitor ahead of Mars and that Tianwen-1 is used to investigate the magnetic field characteristics of ICMEs at Mars. The Heliospheric Upwind Extrapolation time model was used to connect the multiple in situ observations and the coronagraph observations from STEREO/SECCHI and SOHO/LASCO. The first fast coronal mass ejection event (∼761.2 km s−1), which erupted on December 4, impacted Mars centrally and grazed BepiColombo by its western flank. The ambient slow solar wind decelerated the west flank of the ICME, implying that the ICME event was significantly distorted by the solar wind structure. The second slow ICME event (∼390.7 km s−1) underwent an acceleration from its eruption to a distance within 0.69 au and then traveled with the constant velocity of the ambient solar wind. These findings highlight the importance of background solar wind in determining the interplanetary evolution and global morphology of ICMEs up to Mars distance. Observations from multiple locations are invaluable for space weather studies at Mars and merit more exploration in the future

Planning towards an equitable sharing economy: On housing, on transportation, on policymaking

The sharing economy has experienced phenomenal growth in the past decade. Its two most popular sectors, short-term rental (STR) and shared mobility, have significantly transformed people’s travel behavior and disrupted the urban housing/transportation markets. On the other hand, planning and policy efforts lag behind the growth of the sharing economy due to its novelty and its market-based business model. In this dissertation, I use three empirical studies to demonstrate one of those planning and policymaking challenges from the equity perspective. In the first study, I investigate the impact of STR on single-family housing prices in Washington DC using a data-driven, hedonic analytical framework. Not only do I find a significant price inflation as a result of increasing STR activities, but I also identify the spatially uneven impacts that can adversely affect housing affordability in some minority-populated neighborhoods in the city. In the second study, I focus on the built and social environment factors to explain the spatial distribution of e-scooter sharing trips on Washington DC’s streets. Using real-time, trip trajectory level data, I am able to examine not only the built environment factors for a trip’s origin and destination neighborhoods, but also the street design factors for a trip’s traversing paths. Moreover, I apply a machine-learning based clustering analysis to segment trips by their temporal patterns, built environment, and social environment attributes. With both data-intensive analyses, I identify potential equity issues and opportunities associated with the emerging e-scooter sharing in DC. In the third study, I expand my analysis on STR and shared micromobility in a cross-city, cross-section exploration. I find similar tourist-oriented spatial patterns for three types of activities, including STR, station-based bike-sharing, and dockless bike/e-scooter sharing. Additionally, I find a significant lag in their uses in socially disadvantaged neighborhoods in eight cities, as well as identifying a potential connection between active STR business and gentrification in communities of high social vulnerability. The policy heterogeneities within the eight cities provide different angles to understand the feasible and effective planning practices and policy approaches to address the equity concerns on the rising sharing economy

Numerical Optimization Analysis of Floating Ring Seal Performance Based on Surface Texture

Much research and practical experience have shown that the utilization of textures has an enhancing effect on the performance of dynamic seals and the dynamic pressure lubrication of gas bearings. In order to optimize the performance of floating ring seals, this study systematically analyzes the effects of different texture shapes and their parameters. The Reynolds equation of the gas is solved by the successive over-relaxation (SOR) iteration method. The pressure and thickness distributions of the seal gas film are solved to derive the floating force, end leakage, friction, and the ratio of buoyancy to leakage within the seal. The effects of various texture shapes, including square, 2:1 rectangle, triangle, hexagon, and circle, as well as their parameters, such as texture depth, angle, and area share, on the sealing performance are discussed. Results show that the texture can increase the air film buoyancy and reduce friction, but it also increases the leakage by a small amount. Square textures and rectangular textures are relatively effective. The deeper the depth of the texture within a certain range, the better the overall performance of the floating ring seal. As the texture area percentage increases, leakage tends to increase and friction tends to decrease. A fractal roughness model is developed, the effect of surface roughness on sealing performance is briefly discussed, and finally the effect of surface texture with roughness is analyzed. Some texture parameters that can significantly optimize the sealing performance are obtained. Rectangular textures with certain parameters enhance the buoyancy of the air film by 81.2%, which is the most significant enhancement effect. This rectangular texture reduces friction by 25.8% but increases leakage by 79.5%. The triangular textures increase buoyancy by 28.02% and leakage increases by only 10.08% when the rotation speed is 15,000 r/min. The results show that texture with appropriate roughness significantly optimizes the performance of the floating ring seal

Srovnání metod měření plynných emisí S02 a NOx

Import 20/04/2006Prezenční výpůjčkaVŠB - Technická univerzita Ostrava. Fakulta metalurgie a materiálového inženýrství. Katedra (616) ochrany životního prostředí v průmysl