Overseeing road safety progress at regular intervals has and will continue to be advocated as the most promising means to achieve continuous safety improvement. Thus, a scientific approach that can be capable of doing so is disparately required. This study aims to propose a brand-new and efficient methodology for overseeing overall road safety progress at the regional level. To this end, CV-PROMETHEE Ⅱ-JSS, which seamlessly incorporates Coefficient of Variation (CV), Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), and Joint Singular value decomposition and Semi-discrete decomposition (JSS) in an integrative manner, is developed. Specifically, this is designed to combine the retrospective examination and benchmarking analysis in a comprehensive and systematic framework. Based on the proposed methodology, the road safety development of the European Union (EU) Member States is examined over the past decade (2010–2020), whilst simultaneously benchmarking safety performance looking forward to the next decade (2020–2030). As a result, a detailed picture of changes in road safety for each country is quantitatively depicted, providing policymakers with deeper insights into how progress was achieved. The appropriate benchmarks are also scientifically identified for each laggard member to use as a meaningful reference, which largely avoids the need for reinventing the wheel and trial and error approaches. This study provides the EU27 + 3 countries with a practical paradigm to perform both diagnostics and treatment to improve overall road safety levels in an effective way; supporting the government officials and policymakers in the charting of future strategic directions and intervention priorities, and helping them define ways to accelerate action on proven strategies and policies for better lives. Moreover, this study enriches the existing Multi-Criteria Decision-Making (MCDM) mechanism by introducing the CV-PROMETHEE Ⅱ-JSS, and implies its feasibility and effectiveness in future MCDM cases involving safety-related issues.

Road traffic crashes have been a leading cause of death in Southeast Asian countries, which greatly harms the development of countries and affects the livelihood of countless families in this region. In this context, a regular review of road safety attainment is needed to understand why road crashes happen and to better guide the ongoing policymaking and implementation of effective countermeasures as well as next-level strategies. This study introduces an easy-to-use, effective, and systematic methodology for multi-criteria decision-making, CRiteria Importance Through Inter-criteria Correlation (CRITIC) - ELimination and Et Choice Translating REality (ELECTRE) - Fuzzy C-Means (FCM) (CRITIC-ELECTRE-FCM). Its purpose is to appraise the road safety attainment of 11 countries in Southeast Asia. Accompanied by the robustness of analyses with other widely used methods, these countries are ranked and grouped into several levels regarding their road safety attainment over the past decade (2009–2018). The findings provide government officials, policymakers, and any stakeholders of these countries with meaningful information (e.g., what has been done well and what has not) and instructive guidance for future action. Overall, the proposed appraisal system serves as an efficacious policymaking support for countries in the region to review road safety attainment, develop future strategies and policies, and implement safety management.

Housing reform in socialist China has incurred considerable restructuring and transformation of urban space and society. Yet its specific socio-spatial outcomes have not been fully investigated from the perspective of housing type at the meso- and micro-levels. This study attempts to fill the gap by examining the nature and magnitude of the consequences of housing reform and the corresponding effects on mobility. Specifically, based on census data and a mobility survey, this paper combines statistical breakdowns and structural equation modeling to capture the socio-spatial differentiation of urban structure resulting from housing reform and its influences on individual vehicle kilometers traveled (VKT) and transportation walking. The results reveal that: (1) different types of housing tend to feature internally homogeneous populations in terms of socio-economic composition and socio-psychological condition, with pronounced social stratification; (2) residents in different types of housing display dramatically different travel styles, with substantial mobility inequities; (3) social differentiation appears to have spatial determinants; in particular spatial segregation contributes to increasing social exclusion; (4) the effects of spatial and social characteristics on mobility are led by housing type; and (5) individual mobility patterns are shaped by the joint influences of spatial and social dimensions of housing differentiation. The findings contribute to further understanding of socio-spatial differentiation in countries with a transitional housing market, suggesting that the design of land-use policies should recognize their social effects and that urban mobility planning practices should deliver sustainability that serves a diverse population, including in particular disadvantaged groups in public and replacement housing. This study serves as a mirror to observe the urban transition compared to other political economies and adds additional richness and diversity to the theoretical debates on the issue of socio-spatial differentiation and empirical evidence on residential and mobility inequities across global contexts.

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

Due to spatial heterogeneity worldwide, results from studies examining the effect of residential self-selection on travel behavior vary substantially. As a result of housing reform, the unique housing allocation system in China is a prime example of a context where the self-selection effect may conflict with international knowledge. Using a sample of 3836 residents, whom are living in Transit-Oriented Development (TOD) and non-TOD neighborhoods in Shanghai, this study untangles the effects that the built environment and residential self-selection have on travel behavior, in the context of diversified housing types in urban China. Specifically, this paper employs propensity score matching (PSM) to quantitate the relative importance of the built environment itself, verses residential self-selection, in influencing travel behavior for each of the housing types. The results show that the residential self-selection effect in the four types of housing (work-unit, commodity, public, and replacement) accounts for 15.2%, 30.7%, 18.5%, and 5.9% of the total impact on vehicle kilometers traveled (VKT), respectively. These findings expand the international database of point estimates in the relative contribution of self-selection toward the impact on travel behavior across global contexts, providing a comprehensive framework for similar studies on self-selection in other parts of the world.

Reducing CH4 emissions is a major global challenge, owing to the world-wide rise in emissions and concentration of CH4 in the atmosphere, especially in the past decade. China has been the greatest contributor to global anthropogenic CH4 emissions for a long time, but current understanding towards its growing emissions is insufficient. This paper aims to link China's CH4 emissions during 2005–2012 to their socioeconomic determinants by combining input-output models with structural decomposition analysis from both the consumption and income perspectives. Results show that changes in household consumption and income were the leading drivers of the CH4 growth in China, while changes in efficiency remained the strongest factor offsetting CH4 emissions. After 2007, with the global financial crisis and economic stimulus plans, embodied emissions from exports plunged but those from capital formation increased rapidly. The enabled emissions in employee compensation increased steadily over time, whereas emissions induced from firms' net surplus decreased gradually, reflecting the reform on income distribution. In addition, at the sectoral level, consumption and capital formation respectively were the greatest drivers of embodied CH4 emission changes from agriculture and manufacturing, while employee compensation largely determined the enabled emission changes across all industrial sectors. The growth of CH4 emissions in China was profoundly affected by the macroeconomic situation and the changes of economic structure. Examining economic drivers of anthropogenic CH4emissions can help formulate comprehensive mitigation policies and actions associated with economic production, supply and consumption.