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Over the past decade, cities have witnessed a surge in micromobility services that offer flexible mobility options for citizens on an as-needed basis, such as for covering the first/last mile connection of their trips. Although these services have known benefits, including reduced CO2 emissions and less public space required for parking, there is still insufficient understanding of their common dynamics and usage, which can support decision-making in the quest for allocating new mobility infrastructure, like mobility hubs. In this paper, we propose a methodology to identify potential mobility hub locations based on the common associated spatial factors with the ridership of different micromobility services (station-based bike-sharing, dockless moped-style scooter-sharing and scooter-sharing services) in Madrid, Spain. We identify the common associated spatial factors with micromobility usage (e.g. bike stations' density, commercial land use and cycling infrastructure) and train linear models to explore which dependent variables represents better a "common ridership" of multiple micromobility services while fitting better that data. Subsequently, we test our models in a different area to identify potential hotspots for suggested locations. Findings show that considering micromobility ridership altogether using principal component analysis provides better ridership estimations in the test areas. The methodology has the potential to be replicable in other cities and guide decision-making processes for searching potential mobility hub locations. [ABSTRACT FROM AUTHOR]

Since urban traffic is a major source of CO 2 and NO x emissions, cities play a key role averting climate change and combating air pollution. Most researchers agree on the need of designing comprehensive mitigation strategies instead of applying isolated measures. Nevertheless, it is important to understand the specific impact and scope of each measure to look for the most effective synergies among them. In 2004, the Madrid City Council launched a plan to re-design its inner ring-road to move traffic out of the city centre. For safety reasons the planned speed limit for the full-renovated South-West section was finally reduced from 90 km/h to 70 km/h. Besides contributing to traffic safety, this strategy could also be seen as positive to the environment due to the associated reduced fuel consumption and lower emissions. However, lower speed limits have lower rates of community acceptance due to its impact on average travel times at the individual level. This paper conducts an ex-post evaluation of this speed reduction strategy to explore its environmental and traffic performance impacts. The results support the thesis that, in this velocity range, lower speed limits present important opportunities for reducing GHG and air pollution in the section affected by the measure, without substantially altering traffic performance. The implementation of the new speed limit policy produces a 14.4% and 16.4% reduction in CO 2 and NO x emissions respectively, while global travel time remains virtually constant and the saturation rate decreases slightly. Besides, this cost-effective measure reveals great potential to reduce air pollution in highly populated urban areas located next to urban highways. This work provides local policy makers and city managers with useful insights regarding potential co-benefits of traffic optimization and speed reduction management to reduce mobile source emissions in urban environments. [ABSTRACT FROM AUTHOR]

Amidst the growing interest in enhancing the academic understanding of the relationships between e-shopping and transport, a key element remains underexplored – the impact of e-shopping on spatial accessibility to in-store retail. The paper studies variations in multimodal accessibility to in-store retail between e-shopper groups and the associated spatial effects. The research is based on a face-to-face questionnaire, administered in the city of Alcalá de Henares (Madrid Metropolitan Area, Spain), which provides data on socio-economic characteristics, e-shopping habits, and travel time preferences to reach in-store retail. Clustering techniques serve to identify three e-shopper groups: occasional e-shoppers with a car, infrequent e-shoppers with a car, and frequent e-shoppers without a car. A comparison of e-shopper distance-decay functions to reach in-store retail is made, revealing significant differences between the three e-shopper groups for car and public transport for any time interval. However, for walking such differences are limited to time intervals between 10 and 40 min. Distance-decay functions are processed through a gravity-based model, identifying five categories of multimodal accessibility places that provide information on how in-store retail locations may be affected by e-shopping habits. The paper closes with concluding remarks on policy-making and a few pathways for future research. [ABSTRACT FROM AUTHOR]