Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison.

BACKGROUND: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. OBJECTIVE: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question "To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents' health outcomes over time?" METHODS: We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. RESULTS: We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P<.001 in every case) when compared with the alternate approaches. For Norfolk, Virginia (n=31 census tracts), our approach estimated, on average, the percentage of individuals with high blood pressure in the census tract within 1.49% (SD 0.85%), the percentage of individuals with diabetes in the census tract within 1.63% (SD 0.59%), and the percentage of individuals who had >2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). CONCLUSIONS: We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents' health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics.

Criticality assessment for a regional maritime economy.

OBJECTIVE: To identify and assess the criticality for infrastructure assets and better understand their dependencies, interdependencies, and supply chain reliance. DESIGN: This study used a modified mission impact, symbolism, history, accessibility, recognizability, population, and proximity model combined with a deliberative process with regional subject matter experts. SETTING: Hampton roads Virginia maritime area. PARTICIPANTS: Emergency managers, US Corps of Engineers, US Coast Guard, law enforcement, railroad industry, intelligence community, Virginia Department of Emergency Management, Virginia Department of Transportation, and Virginia Port. MAIN OUTCOME: A prioritized list of one-to-n critical assets in the maritime area and identification of up and down-stream dependencies. RESULTS: The final most highly critical grouping included 26 out of 277 assets including especially important road bridges and tunnels, rail bridges and choke points, shipping channels, and marine terminals. CONCLUSIONS: Subject matter experts identified 277 critical infrastructure assets in the Hampton Roads Maritime Area (HRMA). From these, 26 assets that were deemed to be significantly more critical than others. From this reduced list, 12 were further assessed to be most important. The selection process provided significant support to those responsible for providing protection, mitigating potential damage, and planning recovery and allows informed, objective expenditures of limited funding. Additional key findings include the following: Proximity, or the potential for an asset that has been damaged or destroyed to cause direct harm to adjacent assets in the surrounding community, drives criticality in the HRMA more than any other factor in the model. A small group of 26 out of 277 assets exerts an outsized impact in the HRMA-nearly 20 percent of criticality-due to the potential consequences associated with their destruction or disruption. Road transportation, particularly tunnels, represents the primary dependency among critical assets. Major road and maritime transportation assets rely heavily on federal and state organizations to maintain their function. Supply chain: The critical asset group supports or supplies many missions and industries both nationally and internationally, including defense, manufacturing, commercial enterprise, and the movement of raw commodities.

Conceptualizing intragroup and intergroup dynamics within a controlled crowd evacuation.

Social dynamics play a critical role in successful pedestrian evacuations. Crowd modeling research has made progress in capturing the way individual and group dynamics affect evacuations; however, few studies have simultaneously examined how individuals and groups interact with one another during egress. To address this gap, the researchers present a conceptual agent-based model (ABM) designed to study the ways in which autonomous, heterogeneous, decision-making individuals negotiate intragroup and intergroup behavior while exiting a large venue. A key feature of this proposed model is the examination of the dynamics among and between various groupings, where heterogeneity at the individual level dynamically affects group behavior and subsequently group/group interactions. ABM provides a means of representing the important social factors that affect decision making among diverse social groups. Expanding on the 2013 work of Vizzari et al., the researchers focus specifically on social factors and decision making at the individual/group and group/group levels to more realistically portray dynamic crowd systems during a pedestrian evacuation. By developing a model with individual, intragroup, and intergroup interactions, the ABM provides a more representative approximation of real-world crowd egress. The simulation will enable more informed planning by disaster managers, emergency planners, and other decision makers. This pedestrian behavioral concept is one piece of a larger simulation model. Future research will build toward an integrated model capturing decision-making interactions between pedestrians and vehicles that affect evacuation outcomes.