Retrofitting the suburbs to increase walking: evidence from a land-use-travel study

Drive less but exposed more? Exploring social injustice in vehicular air pollution exposure

Despite growing understanding of racial and class injustice in vehicular air pollution exposure, less is known about the relationship between people's exposure to vehicular air pollution and their contribution to it. Taking Los Angeles as a case study, this study examines the injustice in vehicular PM_2.5 exposure by developing an indicator that measures local populations' vehicular PM_2.5 exposure adjusted by their vehicle trip distances. This study applies random forest regression models to assess how travel behavior, demographic, and socioeconomic characteristics affect this indicator. The results indicate that census tracts of the periphery whose residents drive longer distances are exposed to less vehicular PM_2.5 pollution than tracts in the city center whose residents drive shorter distances. Ethnic minority and low-income tracts emit little vehicular PM_2.5 and are particularly exposed to it, while White and high-income tracts generate more vehicular PM_2.5 pollution but are less exposed.

Benchmarking City Layouts—A Methodological Approach and an Accessibility Comparison between a Real City and the Garden City

This article presents a comparative accessibility study between a real city and its redraft as a Garden City. The benchmarking methodology involves defining and evaluating a location-based accessibility indicator in a GIS environment for the city of Coimbra, Portugal, and for the same city laid out as a Garden City, with the same number of inhabitants, jobs, and similar number of urban facilities. The results are derived as maps and weighted average distances per inhabitant to the facilities and jobs, and show that, for the Garden City, average distances drop to around 500 m for urban facilities and 1500 m for the combination of facilities and jobs, making much of the city accessible by walking and practically the whole of it accessible by cycling, with positive impact on transport sustainability and accessibility equity. The methodology can be extended to other benchmarking indicators and city layouts, and the quantitative results it yields make a valuable contribution to the debate on the ideal layout of cities. Moreover, it gives directions on how to improve real cities to address current and future sustainability concerns.

Is Built Environment Associated with Travel Mode Choice in Developing Cities? Evidence from Hanoi

This paper examines the association between the built environment (BE) and travel behavior in Hanoi, Vietnam. A multinomial logit model is used to analyze individuals’ choice of travel mode from a dataset collected via a questionnaire-based household travel survey in 2016 and the geospatial data of BE variables; the dataset contains 762 responses from local residents in ten districts of the Hanoi Metropolitan Area about their daily travel episodes. It also examines a spatial aggregation effect by comparing model performances among four buffering distances and ward-zones. The results showed that (1) a higher population density around an individual’s home is associated with more bus use and less motorbike and car use; (2) mixed land use around the home, average tax revenue near the home, and bus frequency at the workplace have positive relationships with bus ridership; (3) senior people, students, or unskilled laborers tend to use the bus; (4) the spatial aggregation bias significantly affects the estimation results; and (5) new immigrants tend to choose to reside in areas designed for automobile users. Finally, there are several policy implications for transit-oriented development (TOD) in Hanoi, including: (1) parking regulations and/or control strategies should be jointly incorporated into the Hanoi’s TOD policy; (2) Hanoi’s TOD policy should be carefully designed in terms of its scope of development site and type; and (3) a polycentric structure strategy only may not be sufficient for increasing public transit ridership.

Methods for accounting for neighbourhood self-selection in physical activity and dietary behaviour research: a systematic review

BACKGROUND

Self-selection into residential neighbourhoods is a widely acknowledged, but under-studied problem in research investigating neighbourhood influences on physical activity and diet. Failure to handle neighbourhood self-selection can lead to biased estimates of the association between the neighbourhood environment and behaviour. This means that effects could be over- or under-estimated, both of which have implications for public health policies related to neighbourhood (re)design. Therefore, it is important that methods to deal with neighbourhood self-selection are identified and reviewed. The aim of this review was to assess how neighbourhood self-selection is conceived and accounted for in the literature.

METHODS

Articles from a systematic search undertaken in 2017 were included if they examined associations between neighbourhood environment exposures and adult physical activity or dietary behaviour. Exposures could include any objective measurement of the built (e.g., supermarkets), natural (e.g., parks) or social (e.g., crime) environment. Articles had to explicitly state that a given method was used to account for neighbourhood self-selection. The systematic review was registered with the PROSPERO International Prospective Register of Systematic Reviews (number CRD42018083593) and was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

RESULTS

Of 31 eligible articles, almost all considered physical activity (30/31); few examined diet (2/31). Methods used to address neighbourhood self-selection varied. Most studies (23/31) accounted for items relating to participants' neighbourhood preferences or reasons for moving to the neighbourhood using multi-variable adjustment in regression models (20/23) or propensity scores (3/23). Of 11 longitudinal studies, three controlled for neighbourhood self-selection as an unmeasured confounder using fixed effects regression.

CONCLUSIONS

Most studies accounted for neighbourhood self-selection by adjusting for measured attributes of neighbourhood preference. However, commonly the impact of adjustment could not be assessed. Future studies using adjustment should provide estimates of associations with and without adjustment for self-selection; consider temporality in the measurement of self-selection variables relative to the timing of the environmental exposure and outcome behaviours; and consider the theoretical plausibility of presumed pathways in cross-sectional research where causal direction is impossible to establish.

Smartphone App Usage Prediction Using Points of Interest

In this paper we present the first population-level, city-scale analysis of application usage on smartphones. Using deep packet inspection at the network operator level, we obtained a geo-tagged dataset with more than 6 million unique devices that launched more than 10,000 unique applications across the city of Shanghai over one week. We develop a technique that leverages transfer learning to predict which applications are most popular and estimate the whole usage distribution based on the Point of Interest (POI) information of that particular location. We demonstrate that our technique has an 83.0% hitrate in successfully identifying the top five popular applications, and a 0.15 RMSE when estimating usage with just 10% sampled sparse data. It outperforms by about 25.7% over the existing state-of-the-art approaches. Our findings pave the way for predicting which apps are relevant to a user given their current location, and which applications are popular where. The implications of our findings are broad: it enables a range of systems to benefit from such timely predictions, including operating systems, network operators, appstores, advertisers, and service providers.

The cost-effectiveness of installing sidewalks to increase levels of transport-walking and health

OBJECTIVE

This study investigated the cost-effectiveness of installing sidewalks to increase levels of transport-walking.

METHODS

Secondary analysis using logistic regression established the association of sidewalks with transport-walking using two transport-walking thresholds of 150 and 60 min/week using Western Australian data (n=1394) from 1995 to 2000. Minimum, moderate and maximum interventions were defined, associated respectively with one sidewalk, at least one sidewalk and sidewalks on both sides of the street. Costs, average and incremental cost-effectiveness ratios were calculated for each intervention and expressed as 'the cost per person who walks for transport for more than 150 min/week (60 min/week) after the installation of new sidewalks'. A sensitivity analysis examined the robustness of the incremental cost-effectiveness ratios to varying model inputs. Costs are in 2012 Australian dollars.

RESULTS

A positive relationship was found between the presence of sidewalks and transport-walking for both transport-walking thresholds of 150 and 60 min/week. The minimum intervention was found to be the most cost-effective at $2330/person and $674/person for the 150 and 60 min/week transport-walking thresholds respectively. Increasing the proportion of people transport-walking and increasing population density by 50% improved the cost-effectiveness of installing side-walks to $346/person.

CONCLUSIONS

To increase levels of transport-walking, retrofitting streets with one sidewalk is most cost-effective.

Disparities in exposure to automobile and truck traffic and vehicle emissions near the Los Angeles-Long Beach port complex

OBJECTIVES

We assessed how traffic and mobile-source air pollution impacts are distributed across racial/ethnic and socioeconomically diverse groups in port-adjacent communities in southern Los Angeles County, which may experience divergent levels of exposure to port-related heavy-duty diesel truck traffic because of existing residential and land use patterns.

METHODS

We used spatial regression techniques to assess the association of neighborhood racial/ethnic and socioeconomic composition with residential parcel-level traffic and vehicle-related fine particulate matter exposure after accounting for built environment and land use factors.

RESULTS

After controlling for factors associated with traffic generation, we found that a higher percentage of nearby Black and Asian/Pacific Islander residents was associated with higher exposure, a higher percentage of Hispanic residents was associated with higher traffic exposure but lower vehicle particulate matter exposure, and areas with lower socioeconomic status experienced lower exposure.

CONCLUSIONS

Disparities in traffic and vehicle particulate matter exposure are nuanced depending on the exposure metric used, the distribution of the traffic and emissions, and pollutant dispersal patterns. Future comparative research is needed to assess potential disparities in other transportation and goods movement corridors.