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Objectives Despite extensive efforts to study individual differences in loneliness and neurocognitive health, little is known about how within-person changes in state loneliness relate to cognitive performance. This study addressed this gap by examining the association between within-person variation in state loneliness and cognitive performance assessed objectively in daily life. Methods Participants were 313 community-dwelling older adults (70–90 years) who reported momentary feelings of loneliness and completed smartphone-based cognitive tests 5 times daily for 14 consecutive days. Mobile cognitive tests assess visual associative memory, processing speed, and spatial memory. Results At the day level, average state loneliness levels were negatively related to cognitive performance on the same day and subsequent day. Consistent with the day-level analysis, momentary assessments of increased loneliness were consistently linked to worse cognitive performance on concurrent assessments. However, moments characterized by lower cognitive performance predicted higher levels of loneliness 3–4 hr later (next occasion), but not vice versa. Discussion The findings suggest a prospective association between loneliness and cognitive performance, with higher daily loneliness negatively associated with cognitive performance on the same day and predicting worse performance the following day. Notably, within a single day, lower cognitive performance at a given moment predicted elevated loneliness later in the day. This highlights a complex, reciprocal relationship—loneliness predicting and being predicted by cognitive performance depending on timescale. [ABSTRACT FROM AUTHOR]

This paper examines the deviation of refueling a hydrogen fuel cell vehicle with limited opportunity provided by the 68 proposed stations in California. A refueling trip is inserted to reported travel patterns in early hydrogen adoption community clusters and the best and worst case insertions are analyzed. Based on these results, the 68 refueling stations provide an average of 2.5 and 9.6 min deviation for the best and the worst cases. These numbers are comparable to currently observed gasoline station deviation, and we conclude that these stations provide sufficient accessibility to residents in the target areas. © 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserver.

Loneliness influences how people experience and respond to stressors, which may account for its role as a risk factor for morbidity and mortality. The present study was motivated by emerging evidence that affective responses to minor daily events have long‐term implications for health and well‐being. Specifically, we evaluated how individual differences in loneliness relate to the frequency of everyday stressors and stressor‐related negative emotions. A diverse community sample of 255 adults (age 25–65 years) completed ecological momentary assessments (EMA), during which they reported recent stressors and current negative affect (NA) five times a day for 14 days. Multilevel logistic analyses indicated that there was a quadratic association between loneliness and likelihood of reporting stressors, controlling for demographics, social isolation, depressive symptoms, and context (current activities, current location). Multilevel regression indicated that loneliness was unrelated to the concurrent effect of stressors on NA but significantly larger lagged stressor effects were observed among individuals in the low and high ranges of loneliness. These findings suggest that individuals with high levels of loneliness are more likely to experience everyday stressors and have prolonged emotional responses following stressors. [ABSTRACT FROM AUTHOR]

This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes. From the demand side, travel demand for non-primary activities is derived from the destination choices that a traveler makes that minimizes travel disutility within the context of considerations of daily scheduling and routing. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix.First, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. Instead of giving a set of pre-fixed activity locations to visit, LSPxviii HAPP chooses the location for certain activity types given a set of candidate locations. A dynamic programming algorithm is adopted and further developed for LSP-HAPP in order to deal with the choices among a sizable number of candidate locations within the HAPP modeling structure. Potential applications of synthetic pattern generation based on LSP-HAPP formulation are also presented.Second, the Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The proposed formulation isolates each vehicle’s routing problem from those of other vehicles and from the master set covering problem. A modified column generation that uses a search method to find a column with a negative reduced price is proposed.Third, the Network Design Problem is integrated with the Household Activity Pattern Problem (NDP-HAPP) as a bilevel optimization problem. The bilevel structure includes an upper level network design while the lower level includes a set of disaggregate household itinerary optimization problems, posed as HAPP or LSP-HAPP. The output of upper level NDP (level-ofservice of the transportation network) becomes input data for the lower level HAPP that generates travel demand which becomes the input for the NDP. This is advantageous over the conventional NDP that outputs the best set of links to invest in, given an assumed OD matrix. Because the proposed NDP-HAPP can output the same best set of links, a new OD matrix and a detailed temporal distribution of activity participation and travel are created. A decomposed xix heuristic solution algorithm that represents each decision makers’ rationale shows optimality gaps of as much as 5% compared to exact solutions when tested with small examples.Utilizing the aforementioned models, two transportation sustainability studies are then conducted for the adoption of Alternative Fuel Vehicles (AFVs). The challenges in adopting AFVs are directly related to the transportation infrastructure problems since the initial AFV refueling locations will need to provide comparable convenient travel experience for the early adopters when compared to the already matured gasoline fuel based transportation infrastructure. This work demonstrates the significance of the integration between travel demand model and infrastructure problems, but also draws insightful policy measurements regarding AFV adoption.The first application study attempts to measure the household inconvenience level of operating AFVs. Two different scenarios are examined from two behavioral assumptions – keeping currently reported pattern and minimizing the inconvenience cost through HAPPR or HAPPC. From these patterns, the personal or household inconvenience level is derived as compared to the original pattern, providing quantified data on how the public sector would compensate for the increases in travel disutility to ultimately encourage the attractiveness of AFVs.From the supply side of the AFV infrastructure, Location-HAPP is applied to the incubation of the minimum refueling infrastructure required to support early adoption of Hydrogen Fuel Cell Vehicles (HFCVs). One of the early adoption communities targeted by auto manufacturers is chosen as the study area, and then three different values of accessibility are tested and measured in terms of tolerances to added travel time. Under optimal conditions, refueling trips are found to be toured with other activities. More importantly, there is evidence xx that excluding such vehicle-infrastructure interactions as well as routing and scheduling interactions can result in over-estimation of minimum facility requirement.

In this paper, an integrated destination choice model based on routing and scheduling considerations of daily activities is proposed. Extending the Household Activity Pattern Problem (HAPP), the Location Selection Problem (LSP-HAPP) demonstrates how location choice is made as a simultaneous decision from interactions both with activities having predetermined locations and those with many candidate locations. A dynamic programming algorithm, developed for PDPTW, is adapted to handle a potentially sizable number of candidate locations. It is shown to be efficient for HAPP and LSP-HAPP applications. The algorithm is extended to keep arrival times as functions for mathematical programming formulations of activity-based travel models that often have time variables in the objective.

This paper examines network design where OD demand is not known a priori, but is the subject of responses in household or user itinerary choices that depend on subject infrastructure improvements. Using simple examples, we show that falsely assuming that household itineraries are not elastic can result in a lack in understanding of certain phenomena; e.g., increasing traffic even without increasing economic activity due to relaxing of space-time prism constraints, or worsening of utility despite infrastructure investments in cases where household objectives may conflict. An activity-based network design problem is proposed using the location routing problem (LRP) as inspiration. The bilevel formulation includes an upper level network design and shortest path problem while the lower level includes a set of disaggregate household itinerary optimization problems, posed as household activity pattern problem (HAPP) (or in the case with location choice, as generalized HAPP) models. As a bilevel problem with an NP-hard lower level problem, there is no algorithm for solving the model exactly. Simple numerical examples show optimality gaps of as much as 5% for a decomposition heuristic algorithm derived from the LRP. A large numerical case study based on Southern California data and setting suggest that even if infrastructure investments do not result in major changes in itineraries the results provide much higher resolution information to a decision-maker. Whereas a conventional model would output the best set of links to invest given an assumed OD matrix, the proposed model can output the same best set of links, the same OD matrix, and a detailed temporal distribution of activity participation and travel, given a set of desired destinations and schedules.

Set Covering problems find the optimal provision of service locations while guaranteeing an acceptable level of accessibility for every demand points in a given area. Other than reliance on static,exogenously-imposed accessibility measures, these problems either exclude substantive infrastructure-vehicle interactions or only include fragmented infrastructure-vehicle interactions related to the routing considerations of households seeking refueling service as a requirement of performing routine, daily activities. Here, we address this problem by coupling a Location-Routing Problem (LRP) that uses the set covering model as a location strategy to the Household Activity Pattern Problem (HAPP) as the mixed integer scheduling and routing model that optimizes households’ participation in out-of-home activities. The problem addressed includes multiple decision makers: the public/private sector as the service provider, and the collection of individual households that make their own routing decisions to perform a given set of “out-of-home activities” together with a visit to one of the service locations. A solution method that does not necessarily require the full information of the coverage matrix is developed to reduce the number of HAPPs that needs to be solved. The performance of the algorithm, as well as comparison of the results to the set covering model, is presented. Although the application is focused on identifying the optimal locations of Hydrogen Fuel Cell Vehicle (HFCV) refueling stations, this proposed formulation can be used as a facility location strategy for any service activity that is generally toured with other activities.

Being threatened or injured with a weapon is a serious form of physical bullying. Little is known about the effects of being threatened or injured with a weapon on substance use and mental health among adolescents. A secondary analysis of 2017 Youth Risk Behavior Survey data assessed the associations between having been threatened or injured with a weapon and substance use (binge drinking, marijuana, tobacco use, prescription medication misuse) and mental health (feeling sad or hopeless, considering suicide). Multiple logistic regression models with complex survey weights were used, controlling for potential confounders. Approximately 6.0% of students reported having been threatened/injured with a weapon. After adjusting for covariates, having been threatened/injured with a weapon was associated with binge drinking (AOR = 2.5; 95% CI: 1.9–3.4), marijuana (AOR = 2.1; 95% CI: 1.6–2.7), tobacco use (AOR = 2.6; 95% CI: 2.0–3.5), and misuse of prescription medication (AOR = 2.2; 95% CI: 1.6–3.0) compared with students who have not been threatened/injured. Having been threatened/injured with a weapon was associated with 1.6 times the odds (95% CI: 1.2–2.2) of feeling sad/hopeless and 1.7 times the odds (95% CI: 1.3–2.3) of considering suicide. Having been threatened or injured with a weapon is an important public health issue associated with negative mental health and substance use. [ABSTRACT FROM AUTHOR]