Neighborhood Environment and Objectively Measured Sedentary Behavior Among Older Adults: A Cross-Sectional Study

Background: We examined the relationships between objectively assessed neighborhood environment and the patterns of sedentary behavior among older adults.Methods: A total of 126 community-dwelling older adults (aged 65 years or above) were recruited. Data on neighborhood environmental attributes (resident density, street intersection density, sidewalk availability, accessible destinations, and accessible public transportation), accelerometer-assessed total time and patterns of sedentary behavior (number and duration of bouts), and sociodemographic characteristics were collected. Multiple linear regression models were developed.Results: After adjustment for potential confounders, greater sidewalk availability was negatively related to the number of sedentary bouts (β = −0.185; 95% CI: −0.362, 0.015; p = 0.034) and sedentary bout duration (β = −0.180; 95% CI: −0.354, −0.011; p = 0.037).Conclusions: This study revealed that a favorable neighborhood environment characterized by sidewalk availability is negatively associated with sedentary behavior patterns in Taiwanese older adults. These findings are critical to inform environmental policy initiatives to prevent sedentary lifestyle in older adults.

Multimodal interaction-aware motion prediction for autonomous street crossing

For mobile robots navigating on sidewalks, the ability to safely cross street intersections is essential. Most existing approaches rely on the recognition of the traffic light signal to make an informed crossing decision. Although these approaches have been crucial enablers for urban navigation, the capabilities of robots employing such approaches are still limited to navigating only on streets that contain signalized intersections. In this article, we address this challenge and propose a multimodal convolutional neural network framework to predict the safety of a street intersection for crossing. Our architecture consists of two subnetworks: an interaction-aware trajectory estimation stream ( interaction-aware temporal convolutional neural network (IA-TCNN)), that predicts the future states of all observed traffic participants in the scene; and a traffic light recognition stream AtteNet. Our IA-TCNN utilizes dilated causal convolutions to model the behavior of all the observable dynamic agents in the scene without explicitly assigning priorities to the interactions among them, whereas AtteNet utilizes squeeze-excitation blocks to learn a content-aware mechanism for selecting the relevant features from the data, thereby improving the noise robustness. Learned representations from the traffic light recognition stream are fused with the estimated trajectories from the motion prediction stream to learn the crossing decision. Incorporating the uncertainty information from both modules enables our architecture to learn a likelihood function that is robust to noise and mispredictions from either subnetworks. Simultaneously, by learning to estimate motion trajectories of the surrounding traffic participants and incorporating knowledge of the traffic light signal, our network learns a robust crossing procedure that is invariant to the type of street intersection. Furthermore, we extend our previously introduced Freiburg Street Crossing dataset with sequences captured at multiple intersections of varying types, demonstrating complex interactions among the traffic participants as well as various lighting and weather conditions. We perform comprehensive experimental evaluations on public datasets as well as our Freiburg Street Crossing dataset, which demonstrate that our network achieves state-of-the-art performance for each of the subtasks, as well as for the crossing safety prediction. Moreover, we deploy the proposed architectural framework on a robotic platform and conduct real-world experiments that demonstrate the suitability of the approach for real-time deployment and robustness to various environments.

The Analysis of Unsignalized Intersection Road Performance at Manukan Wetan Surabaya City

Surabaya is the capital of the province of East Java and is the second largest city in Indonesia after Jakarta. As a city that will continue to develop, the mobility of the people is getting higher, as well as the progress of the current means of transportation which is increasing, causing traffic problems, one of which occurs at intersections. One of the intersections that will be reviewed is the Raya Manukan intersection. This study uses the MKJI 1997 guideline, with primary data collection by means of traffic conditions survey and secondary collection, namely data on the number of vehicle growth from the Department of Population and Civil Registry of Surabaya City. This study aims to determine the performance of the unsigned intersection at the Raya Manukan intersection. With this research data, as well as from the results of traffic analysis at the Raya Manukan intersection with the boundary from the Amd Street Intersection to the Buntaran Street Intersection. From the calculation results, it is obtained that the intersection capacity is 3750.9 pcu / hour, with a degree of saturation value of 0.8254. From the results of the analysis, the calculations that have been carried out have not been able to reach the desired degree of saturation value, which is as suggested by MKJI 1997.Therefore then an alternative is made with road widening engineering and produces a saturation degree value of 0.7493, so that the road widening on Raya Manukan Kulon is the best alternative in solving the capacity problem of Intersection Manukan.

Which Way is Up? Orientation and Residential Property Values

Builders have long been cognizant of the importance of the siting of a house on a lot, whether to shelter from prevailing weather, allow better access to natural light, gain a scenic view, or more recently for optimal capture of solar energy. Using 2016–2017 sales data for the County of San Diego, we examine the market valuation of house orientation. San Diego provides a good research area as the topography does not lend itself to a grid-like pattern for the street system. Orientation is divided into eight directions (south, southeast, east, . . .) and hedonic price equations are estimated. In addition, the analysis includes proximity to a cul-de-sac and street intersection as explanatory variables. The results suggest that house orientation is a significant factor in determining house valuation. Houses oriented directly on an east-west axis command a premium over houses oriented on a north-south axis. After correcting for spatial autocorrelation, neither cul-de-sac nor intersection locations are significant factors in determining housing prices. Moreover, a robustness check using a small sample of 241 transactions provides inclusive results regarding a premium for solar panel installation.

Built Environment, Psychosocial Factors and Active Commuting to School in Adolescents: Clustering a Self-Organizing Map Analysis

Although the built environment and certain psychosocial factors are related to adolescents’ active commuting to and from school (ACS), their interrelationships have not been explored in depth. This study describes these interrelationships and behavioral profiles via a self-organizing map (SOM) analysis. The sample comprised 465 adolescents from the IPEN (International Physical Activity and the Environment Network) Adolescent study in Valencia, Spain. ACS, barriers to ACS, physical self-efficacy, social support and sociodemographics were measured by questionnaire. Street-network distance to school, net residential density and street intersection density were calculated from the Geographic Information System. The clustering of the SOM outcomes resulted in eight areas or clusters. The clusters which correspond to the lowest and highest ACS levels were then explored in depth. The lowest ACS levels presented interactions between the less supportive built environments (i.e., low levels of residential density and street connectivity in the neighborhood and greater distances to school) and unfavorable psychosocial variables (i.e., low values of physical self-efficacy and medium social support for ACS) and good access to private motorized transport at home. The adolescents with the lowest ACS values exhibited high ACS environment/safety and planning/psychosocial barrier values. Future interventions should be designed to encourage ACS and change multiple levels of influence, such as individual, psychosocial and environmental factors.