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Mixed findings have been reported in previous research regarding the impact of built environment on travel behavior-i.e., statistically and practically significant effects found in a number of empirical studies and insignificant correlations shown in many other studies. It is not clear why the estimated impact is stronger or weaker in certain urban areas and how effective a proposed land use change/policy will be in changing certain travel behavior. This knowledge gap has made it difficult for decision makers to evaluate land use plans and policies according to their impact on vehicle miles traveled (VMT), and consequently, their impact on congestion mitigation, energy conservation, and pollution and greenhouse gas emission reduction.This research has several objectives: (1) re-examine the effects of built-environment factors on travel behavior, in particular, VMT in five US metropolitan areas grouped into four case study areas; (2) develop consistent models in all case study areas with the same model specification and datasets to enable direct comparisons; (3) identify factors such as existing land use characteristics and land use policy decision-making processes that may explain the different impacts of built environment on VMT in different urban areas; and (4) provide a prototype tool for government agencies and decision makers to estimate the impact of proposed land use changes on VMT.The four case study areas include Seattle, WA; Richmond-Petersburg and Norfolk-Virginia Beach, VA; Baltimore, MD; and Washington, DC. Our empirical analysis employs Bayesian multilevel modeling method with various person-level socioeconomic and demographic variables, and five built-environment factors including residential density, employment density, entropy (measuring level of mixed-use development), average block size (measuring transit/walking friendliness), and distance to city center (measuring decentralization and level of infill development).Our findings show that promoting compact, mixed-use, small-block, and infill developments can be effective in reducing VMT per person in all four case study areas. However, the effectiveness of land use plans and policies encouraging these types of land development is different both across case study areas and within the same case study area. We have identified several factors that potentially influence the connection between built environment shifts and VMT changes including urban area size, existing built environment characteristics, transit service coverage and quality, and land use decision-making processes.

The UK government introduced strict measures (including asking people to work from home and a lockdown) to slow the spread of COVID-19 by limiting people’s movement. This led to substantial reductions in traffic, making roads much safer for cyclists. This provides a unique opportunity to study the role played by safe cycling infrastructure. Many UK cities have provided cycling infrastructure to improve safety and encourage cycling. However, access to safe cycling infrastructure varies across neighbourhoods, potentially contributing to inequality. Since roads became safer due to the unprecedented reduction in traffic during the lockdown, safe cycling infrastructure may not play a significant role during this period. On the other hand, safe cycling lanes are often connected to amenities, potentially attracting cyclists even if they confer no additional safety benefit. That is, connectivity might matter more than safety. In this study, we utilised crowdsourced cycling data and regression models to examine the extent to which cycling intensity for non-commuting purposes changes with different types of cycling infrastructure in the city of Glasgow, Scotland, UK. In addition, we selected some areas with large increases in cycling intensity and examined the surrounding environments using Google Street View. Our results showed that non-commuting cycling activities increased significantly after the government interventions on both typical roads and safe cycling lanes while much higher increases were observed on safe cycling lanes than on other roads. A further analysis showed that there were large increases in cycling volumes on both typical roads and safe cycling lanes with good amenities and connectivity, highlighting the importance of these factors when building new cycling infrastructure. Since safe cycling lanes are not equally accessible to people, providing temporary cycling lanes during the pandemic considering these conditions could encourage people to cycle more, and thereby improve their health.

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The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013-2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data.

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