International Journal of Sustainable Transportation最新文献

Cycling rates have grown consistently over recent years in cities across the globe. Nevertheless, the lack of policies to promote cycling results in dissatisfaction among cyclists in many cities. In response to this situation, local grassroots initiatives have emerged to pursue more ambitious cycling policies in German cities. However, there is limited knowledge of how grassroots movements influence the cycling policy agenda. Against this background, our article explores the relevance of the grassroots movement “Radentscheid” in four major German cities (Berlin, Frankfurt, Munich, and Hamburg) regarding institutionalizing cycling policymaking. By combining exploratory document analysis, expert interviews, and an analysis of secondary data, we show that the development of cycling over the last two decades in these cities follows three stages: (i) Commitment, when cycling is put on the political agenda; (ii) Imbalanced growth, characterized by a strong increase in cycling but little progress in cycling policies and a decrease in cycling satisfaction; and (iii) Institutional adaptation, when cycling becomes a key issue for local governments due to the pressure from the grassroots movement “Radentscheid”. This paper closes with a discussion of the main results and policy recommendations.

Active school travel has been associated with a wide range of psychological and physiological benefits. However, many parents (particularly those of primary school children) are concerned about their children’s safety due to traffic and urban infrastructure. In this research, we collected information about the geographical school routes, the transportation mode, and the accompaniment status of children of a German primary school. Children and their parents also rated the overall safety of the entire school route. Our findings underline that even primary school children can travel actively to school (about two-thirds in our sample) if the routes to school are short enough and consist of a comparatively safe infrastructure. Children rated their school routes to be significantly safer than their parents did. Furthermore, we found evidence for differences with regard to specific aspects: For example, parents’ (but not children’s) safety perceptions are enhanced by a higher proportion of streets with reduced speed limits. Given that parents who feel the school route is unsafe are less likely to allow their child to travel to school alone, traffic calming measures appear one measure suitable to accommodate their concerns. In contrast to traffic-related issues as the subjectively most prevalent hazard, our survey points toward single-person crashes as a more common case that may be rather underestimated by parents.

Hispanics have a documented higher risk of obesity than other ethnic minorities. Walking can reduce the risk of obesity and promote healthy living. However, walking behavior varies with race/ethnicity, and Hispanics’ walking behavior is less understood. This study compared the walking behaviors of Hispanic and non-Hispanic White residents in Salt Lake County (SLCo), Utah, at the personal and block group levels, based on data from the Utah Household Travel Survey (UHS), which covered 2800 households and 176 Hispanic individuals in SLCo, including their travel trips and socioeconomic status. Trip-level data from the UHS were aggregated into personal and block group levels based on trip ends for multiscale analysis. Our statistical analysis suggested that Hispanics’ walking frequency and density were significantly lower than those of non-Hispanic Whites. According to the personal-level model, education generally contributed to ethnic disparities in walking, but higher education did not increase Hispanics’ walking frequency. The block-group-level model showed that non-Hispanic Whites’ walking behavior was highly sensitive to the built environment. We also found positive impacts of worship accessibility on walking density, which might be unique to non-Hispanic White neighborhoods in SLCo. Built environment factors influenced Hispanics’ walking behavior less than that of non-Hispanic Whites, and the primary determinants included car ownership, driving license, and accessibility of public transit. The research outcomes of this study could provide implications for designing strategies to promote walkability based on ethnic disparities. This also encourages further investigations into the equity of walkable environments for different racial/ethnic populations as an environmental injustice issue.

For the efficient management of bike-sharing systems (BSSs), accurate demand predictions are crucial to address the uneven distribution of bikes at various stations. Recent studies have explored a hierarchical prediction framework using cluster-level models to more accurately estimate demand at the station level. However, in frameworks based on hard clustering, where each station is exclusively assigned to one of several clusters, prediction accuracy tends to be lower for stations at the cluster boundaries. To improve accuracy for such stations, this study proposes a novel soft clustering algorithm for BSSs. The key idea is to allow stations to belong to multiple clusters, calculating the membership degree for each station based on transitions between stations and clusters obtained through hard clustering. This study also investigated the impact of restricting clusters to which individual stations belong based on distance or usage history. Two approaches, distance- and usage-based, were employed to determine the clusters to which each station belongs. Experimental results using Seoul Bike data demonstrate the effectiveness of the proposed method in enhancing traffic prediction accuracy within the hierarchical prediction framework. Notably, excluding clusters with minimal usage for each station using the usage-based approach yielded the best performance.

This study investigates in UK context the relationship between adolescents’ choice of sustainable transport modes (e.g. active transport like walking or cycling and public transport like buses or subways) for their journey to school and maternal non-transport pro-environmental behaviors, such as energy conservation and environmentally friendly purchases, as well as its temporal changes. Data from waves 4 and 10 of the UK Understanding Society survey were separately analyzed using multinomial logistic regression to explore the relationship between frequency of mothers’ non-transport pro-environmental behaviors and adolescents’ sustainable transport to school. Additionally, to understand changes in the strength of this relationship over time, a regression analysis was conducted examining the interaction of mothers’ non-transport pro-environmental behaviors with the survey year. Findings indicate substantial correlations between an array of variables including adolescents’ age, ethnicity, mothers’ occupational and transport behaviors, the number of cars owned by the household, and the nature of residence (urban vs rural), with the adolescents’ active or public transport choice to school, consistently across both waves. As the primary focus of the study, a positive relationship between mothers’ non-transport pro-environmental behaviors and adolescents’ public transport to school is found, although the strength of this relationship declined over time. Importantly, more easily observable mothers’ non-transport pro-environmental behaviors holds a stronger strength of correlation with adolescents’ use of public transport to school, compared to maternal psychological factors like pro-environmental attitudes. Hence, encouraging a range of sustainable behaviors among mothers is crucial to promote adolescents’ public transport to school.

The abrupt switch to work from home during the COVID-19 pandemic has not only altered people’s commutes but also changed their entire work-life balance. While some workers were quick to adapt and maintain or even increase productivity, others experienced a decrease in productivity. Self-assessed productivity changes after switching from traditional in-person work to work from home is studied using a survey of 3,780 workers (including full-time college students). A probabilistic statistical model is used to estimate the probabilities that workers’ self-reported productivity during the pandemic remained the same, decreased, in some ways increased and in other ways decreased, or increased. The model estimation results identify workers who were resilient and adaptable (having a higher probability of increasing their productivity) and those less adaptive workers, who were more likely to experience a decrease in productivity. It was found that race, ethnicity, household income, household size, education, gender, the presence of children in the household, level of life satisfaction, being a student, prior experience with online meetings, and commute distances all play a role in how the workers’ productivity changed. This study provides insights for the development of effective policies to improve equity (by targeting vulnerable populations) and sustainability (by retaining the transportation and environmental benefits of telework) in the post COVID-19 reality.

Cities worldwide are promoting bicycling as a sustainable mode of transportation. However, bicycle theft remains a significant deterrent for potential riders, and also influences the behaviors of existing cyclists. Understanding the impact of theft on bicycling behaviors provides a foundation for developing strategies to address the negative impacts of bicycle theft. Our goal is to characterize if and how bicycle theft changes individual bicycling behavior. We gathered responses from 1821 individuals in a survey focused on bicycle theft in North America. We employed bivariate analysis and binary logistic regression models to explore the relationships between demographic factors, bicycle attributes, and pre-theft behavior to explain post-theft bicycling behavior. The results show that 45% of survey respondents reduced or ceased bicycling post-theft, while 6% increased their bicycling. Additionally, 40% transitioned from bicycling to unsustainable modes of transportation for their post-theft trips. Also, 69% of people eventually replaced their stolen bicycles, of which 46% selected models of equal/higher value. Pre-theft bicycling activity emerged as the most influential factor on ridership behavior after a bicycle theft, with occasional riders experiencing the most negative impact, compared to frequent riders, who remained committed to bicycling. Recovery of the stolen bicycles, e-bicycle usage, number of bicycles owned, and income levels were also predictors of future bicycling patterns. The insights from this research can inform targeted interventions for populations most at risk to reduce the negative impact of bicycle theft, such as secure parking for new and low-income bicyclists.

This article explores the adoption of alternative fuel vehicles (AFVs), leading to decarbonization, in disadvantaged communities (DACs) by applying statistical and explainable artificial intelligence (XAI) techniques to understand the factors associated with AFV adoption in these communities. The study harnesses a unique and comprehensive database of surveys and public databases for the Puget Sound region in the United States. The XAI techniques, specifically the Extreme Gradient Boosting algorithm with Shapely Additive Explanations, provide interpretable and understandable explanations of factors associated with AFV adoption in DACs. The study findings provide an understanding of the social and economic factors and challenges of DACs. The results suggest several key factors, especially a lack of access to charging infrastructure, consumer attitudes, and income, play a substantial role in adopting AFVs. As expected, AFV adoption in DACs (12.96%) is lower than non-DACs (15.30%). More public charging stations strongly correlate with AFV adoption in DACs. Tech-oriented households in DACs are more likely to adopt AFVs compared with non-DACs. The findings also point to the significant effects of home charging facilities while adopting AFVs in DACs. The XAI results emphasize the importance of socio-economic factors in AFV adoption programs and provide insights into decision-making in DACs. This research contributes to the literature on AFV adoption and suggests opportunities for improvements in DACs transitioning to AFVs. The study findings can be used to assess the planning-level impacts of refueling or charging infrastructure in DACs while enabling DACs to benefit from infrastructure investments.

Taking the Yangtze River Delta (YRD) of China as the research area, this paper studies the influence of freight structure adjustment and energy intensity on carbon dioxide (CO2) emission from the transportation industry. Sample data from 1990 to 2019 are selected, and co-integration analysis is performed using three independent variables: energy intensity, turnover ratio of railway to highway (R/H), and turnover ratio of railway to waterway (R/W). Then, an autoregressive distribution lag-error correction model (ARDL-ECM) is established to estimate the long-run and short-run relationships among the variables through unit root test, autoregressive distributed lag (ARDL) boundary test, and Granger test. The results show that in the long run, the growth of energy intensity leads to the long-term growth of CO2 emission in the transportation sector of the YRD, and R/W and R/H have a suppressive effect on CO2 emission. Granger causality indicates that there is a bidirectional causal relationship between energy intensity and CO2 emission. This work can be a reference for government departments to formulate policies related to carbon emissions in the transportation industry.

Low-income populations are disadvantaged in a car-dependent society despite car ownership and could be using walking and cycling to reduce their travel costs. This study explores how low-income households with and without cars living in various geographies disproportionately use walking and cycling to save money in comparison to higher-income households. Data from the 2017 National Household Travel Survey is used to investigate the variation in walking and cycling behavior among three groups of households: (1) carless low-income households, (2) low-income households with cars, and (3) higher-income households. Generalized ordered logistic regression models are estimated to examine how the probability of using active travel to save money varies by household type, location (urban, suburban, rural) and other socioeconomic attributes. We find that low-income households are more likely to walk or cycle to save money on transportation compared to higher-income households. Carless low-income households present a higher probability to use active travel to decrease travel costs in comparison to car-owning low-income households. Our results also indicate that on average, urban residents are more likely to travel actively to reduce expenses compared to suburban and rural residents. The lowest spatial variation is found for carless low-income households, demonstrating their higher disadvantage compared to those with cars. Low-income people of color are more likely to use active travel to save money while being a female, older, or having children are attributes associated with a lower probability to use active travel to reduce travel expenses in low-income households.