Safe urban walking environments may improve health by encouraging physical activity, but the relationship between an individual's location and walking pattern and the risk of pedestrian-motor vehicle collision is unknown. We examined associations between individuals' walking bouts and walking risk, measured as mean exposure to the risk of pedestrian-vehicle collision. Walking bouts were ascertained through integrated accelerometry and global positioning system data and from individual travel-diary data obtained from adults in the Travel Assessment and Community Study (King County, Washington) in 2008-2009. Walking patterns were superimposed onto maps of the historical probabilities of pedestrian-vehicle collisions for intersections and midblock segments within Seattle, Washington. Mean risk of pedestrian-vehicle collision in specific walking locations was assessed according to walking exposure (duration, distance, and intensity) and participant demographic characteristics in linear mixed models. Participants typically walked in areas with low pedestrian collision risk when walking for recreation, walking at a faster pace, or taking longer-duration walks. Mean daily walking duration and distance were not associated with collision risk. Males walked in areas with higher collision risk compared with females, while vehicle owners, residents of single-family homes, and parents of young children walked in areas with lower collision risk. These findings may suggest that pedestrians moderate collision risk by using lower-risk routes.

Statement of purpose A safe walking environment may improve public health by facilitating environmental health, physical activity and injury prevention. We studied the walking behaviour of adult pedestrians to examine the relationship between walking and walking route safety. Methods A cross-sectional study of adults in King County, WA in 2007–2008 assessed walking using accelerometry with spatial location via Global Positioning System (GPS) devices and travel diaries. GPS and accelerometer readings were logged every 30 s for up to 7 days. Participants were included if they had at least one walking “bout” in Seattle, WA city boundaries (N = 548). Bouts were spatially matched to Seattle intersection and mid-block locations. The historical probability of a pedestrian-motor vehicle collision during 2007–2013 at each intersection and mid-block was predicted in a random-effects Poisson model which included road characteristics (e.g., traffic volume), built environment features (e.g., sidewalks) and neighbourhood characteristics (e.g., residential density). Multilevel mixed-effects linear regression was used to assess the average safety of walking routes (mean exposure to pedestrian collision risk) and individual walking (accelerometer counts, bout length, and bout time) and demographic characteristics. Results Pedestrians who walked longer had safer walking routes (−2.65% historical probability of pedestrian collision along the route per 10 min of walking, 95% CI −3.54%, −1.76%). Female pedestrians more commonly had safer walking routes relative to males (−1.10%, 95% CI −0.17%, −2.02%), as did people who owned at least one motor vehicle (−3.98%, 95% CI −5.07%, −2.90%). Conclusions These findings may indicate that people who walk more are significantly less likely to walk in areas with a higher risk of pedestrian collisions. Pedestrians who walk more may avoid areas that are perceived or experienced as higher risk to pedestrians. Significance and contributions Our findings suggest that environmental changes to improve safety for walkers may encourage pedestrian activity.

Walking is a popular form of physical activity associated with clear health benefits. Promoting safe walking for pedestrians requires evaluating the risk of pedestrian motor vehicle collisions at specific roadway locations in order to identify where road improvements and other interventions may be needed. The objective of this analysis was to estimate the risk of pedestrian collisions at intersections and mid-blocks in Seattle, WA. The study used 2007-2013 pedestrian motor vehicle collision data from police reports and detailed characteristics of the microenvironment and macroenvironment at intersection and mid-block locations. The primary outcome was the number of pedestrian motor vehicle collisions over time at each location (incident rate ratio [IRR] and 95% confidence interval [95% CI]). Multilevel mixed effects Poisson models accounted for correlation within and between locations and census blocks over time. Analysis accounted for pedestrian and vehicle activity (e.g., residential density and road classification). In the final multivariable model, intersections with 4 segments or 5 or more segments had higher pedestrian collision rates compared to mid-blocks. Non-residential roads had significantly higher rates than residential roads, with principal arterials having the highest collision rate. The pedestrian collision rate was higher by 9% per 10 feet of street width. Locations with traffic signals had twice the collision rate of locations without a signal and those with marked crosswalks also had a higher rate. Locations with a marked crosswalk also had higher risk of collision. Locations with a one-way road or those with signs encouraging motorists to cede the right-of-way to pedestrians had fewer pedestrian collisions. Collision rates were higher in locations that encourage greater pedestrian activity (more bus use, more fast food restaurants, higher employment, residential, and population densities). Locations with higher intersection density had a lower rate of collisions as did those in areas with higher residential property values. The novel spatiotemporal approach used that integrates road/crossing characteristics with surrounding neighborhood characteristics should help city agencies better identify high-risk locations for further study and analysis. Improving roads and making them safer for pedestrians achieves the public health goals of reducing pedestrian collisions and promoting physical activity. (C) 2015 Elsevier Ltd. All rights reserved.

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

Each of us has a complex and reciprocal relationship with our environment. Based on limited knowledge of this interwoven set of influences and consequences, we constantly make choices: where to live, how to go to work, what brands to buy, what to do with our leisure time. These choices evolve into patterns, and these patterns become driving functions of our relationship with the world around us. With increasing ease, devices we carry can sense, process, and transmit data on these patterns for our own use or to share, carefully, with others. In particular, here we will focus on location time series, gathered from GPS-enabled personal mobile devices. From this capacity emerges a new class of hybrid mobile-web applications that, first, enable personal exploration of our own patterns and, second, use the same data to index our life into other available datasets about the world around us. Such applications, revealing the previously unobservable about our own lives, offer an opportunity to employ mobile technology to illuminate the ramifications of our choices on others and the effects of the "microenvironments" we move through on us [1, 10].