This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model and the other on sequential, mixed multinomial/nested Logit model. These two variants, serving as based models, are further extended with two features: bounded rationality (BR) and information sharing. BR is considered by incorporating the indifference band into the random utility component of the MNL model, forming a BR-based DTD stochastic model. A macroscopic model of travel information sharing is integrated into the DTD dynamics to account for the impact of incomplete information on travelers' SRDT choices. These DTD choice models are combined with within-day dynamics following the Lighthill-Whitham-Richards (LWR) fluid dynamic network loading model. Simulations on large-scale networks (Anaheim) illustrate the interactions between users' adaptive decision making and network conditions (including local disruption) with different levels of information availability and user behavior. Our findings highlight the need for modeling network transient and disequilibriated states, which are often overlooked in equilibrium-constrained network design and optimization.
BackgroundIt is proposed that neuronal damage in diabetes is related to hyperglycaemia and insulin resistance, whilst inflammation and subsequent oxidative stress affects cognitive function in obesity. We investigated whether obesity and diabetes present distinct neuropathological features of neurodegeneration.MethodsTo compare amyloid, tau and cerebral glucose metabolism, we processed 1973 available PET scans for 18F‐AV‐45, 18F‐flortaucipir and 18F‐fludeoxyglucose (FDG) from 971 participants with diabetes (n=194), obesity (n=377) and without diabetes or obesity (n=400) from the Alzheimer’s Disease Neuroimaging Initiative dataset. Independent t‐tests and multivariate analysis were used for regions of interest (ROIs) analysis in participant groups with diabetes vs non‐diabetic/non‐obese and obesity vs non‐diabetic/non‐obese. This was conducted across different cognitive subgroups, namely Alzheimer’s disease, mild cognitive impairment or normal cognition. Amyloid positive and negative subjects were evaluated separately. Voxel‐wise analysis using SPM was also conducted for these groups.ResultsFor diabetes, higher amyloid uptake was found in the medial temporal regions in the cognitively normal diabetic group compared to the cognitively normal (non‐diabetic/non‐obese) group. This was also found in the amyloid positive mild cognitively impaired diabetic groups compared to mild cognitively impaired (non‐diabetic/non‐obese) group. Similarly, higher amyloid deposition was seen in the hippocampus of the cognitively normal obese group compared to the cognitively normal (non‐diabetic/non‐obese) group.Additionally, higher tau uptake was found in the whole brain (global brain value), frontal, parietal and occipital lobes for cognitively normal diabetic and obese groups, compared to cognitively normal (non‐diabetic/non‐obese) groups. Higher tau was further found in the temporal lobe and anterior cingulate of AD obesity participants compared to the AD (non‐diabetic/non‐obese) participants.Discrete cerebral glucose metabolic patterns were found in diabetes and obesity. In the diabetic groups, reduced FDG uptake was found in mild cognitively impaired participants (whole brain, frontal, parietal and occipital lobes) and amyloid positive AD participants (occipital lobe) compared to non‐diabetic/non‐obese participants. In contrast, increased FDG uptake was mainly found in amyloid negative obese participants.ConclusionsGlucose hypometabolism in diabetic participants in contrast to hypermetabolism in obese participants implies that different pathological processes of neurodegeneration occur in these two conditions. This suggests that these two conditions require different therapeutic strategies to prevent neurodegeneration.
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