Correlated parameters in driving behavior models: car-following example and implications for traffic microsimulation

Behavioral parameters in car following and other models of driving behavior are expected to be correlated. An investigation is conducted into the effect of ignoring correlations in three parameters of car-following models on the resulting movement and properties of a simulated heterogeneous vehicle traffic stream. For each model specification, parameters are calibrated for the entire sample of individual drivers with Next Generation Simulation trajectory data. Factor analysis is performed to understand the pattern of relationships between parameters on the basis of calibrated data. Correlation coefficients have been used to show statistically significant correlation between the parameters. Simulation experiments are performed with vehicle parameter sets generated with and without considering such correlation. First, parameter values are sampled from the empirical mass functions, and simulated results show significant difference in output measures when parameter correlation is captured (versus ignored). Next, parameters are sampled under the assumption that they follow the multivariate normal distribution. Results suggest that the use of parametric distribution with known correlation structure may not sufficiently reduce the error due to ignoring correlation if the underlying assumption does not hold for both marginal and joint distributions

Likelihood and duration of flow breakdown: modeling the effect of weather

The effect of rain on freeway flow breakdown behavior is investigated. Three aspects of flow breakdown are analyzed for rain versus no rain (clear) weather conditions. First, the probability of breakdown occurrence is examined by analyzing the distribution of prebreakdown flow rates observed immediately before the onset of traffic breakdown by using a survival analysis approach. At all study sections, a reduction with prebreakdown flow rates is observed under rain conditions compared with distributions under no rain and confirms higher breakdown likelihoods at lower flows. Log likelihood ratio tests confirm the statistical significance of differences in the prebreakdown flow rate distribution parameters under rain compared with clear conditions. Second, breakdown duration is examined by estimating a semiparametric Cox proportional hazard model. With a rain event indicator set as an independent variable, the effect of rain on breakdown duration is observed. Rain during a breakdown episode is found to increase its duration, whereas rain before breakdown does not appear to affect duration. Finally, prebreakdown and postbreakdown flow rates are compared. Overall, while a reduction in prebreakdown flow rates is observed because of rain, the flow drop between prebreakdown and postbreakdown is not much different between rain (3.9% to 12.0%) and no rain (7.8% to 12.7%) conditions

Calibration of traffic flow models under adverse weather and application in mesoscopic network simulation

The weather-sensitive traffic estimation and prediction system (TrEPS) aims for accurate estimation and prediction of the traffic states under inclement weather conditions. Successful application of weather-sensitive TrEPS requires detailed calibration of weather effects on the traffic flow model. In this study, systematic procedures for the entire calibration process were developed, from data collection through model parameter estimation to model validation. After the development of the procedures, a dual-regime modified Greenshields model and weather adjustment factors were calibrated for four metropolitan areas across the United States (Irvine, California; Chicago, Illinois; Salt Lake City, Utah; and Baltimore, Maryland) by using freeway loop detector traffic data and weather data from automated surface-observing systems stations. Observations showed that visibility and precipitation (rain-snow) intensity have significant impacts on the value of some parameters of the traffic flow models, such as free-flow speed and maximum flow rate, while these impacts can be included in weather adjustment factors. The calibrated models were used as input in a weather-integrated simulation system for dynamic traffic assignment. The results show that the calibrated models are capable of capturing the weather effects on traffic flow more realistically than TrEPS without weather integration

Contextual quantum metrology

Quantum metrology promises higher precision measurements than classical methods. Entanglement has been identified as one of quantum resources to enhance metrological precision. However, generating entangled states with high fidelity presents considerable challenges, and thus attaining metrological enhancement through entanglement is generally difficult. Here, we show that contextuality of measurement selection can enhance metrological precision, and this enhancement is attainable with a simple linear optical experiment. We call our methodology "contextual quantum metrology" (coQM). Contextuality is a nonclassical property known as a resource for various quantum information processing tasks. Until now, it has remained an open question whether contextuality can be a resource for quantum metrology. We answer this question in the affirmative by showing that the coQM can elevate precision of an optical polarimetry by a factor of 1.4 to 6.0, much higher than the one by quantum Fisher information, known as the limit of conventional quantum metrology. We achieve the contextuality-enabled enhancement with two polarization measurements which are mutually complementary, whereas, in the conventional method, some optimal measurements to achieve the precision limit are either theoretically difficult to find or experimentally infeasible. These results highlight that the contextuality of measurement selection is applicable in practice for quantum metrology.Comment: 18 pages, 6 figures, companion paper: arXiv:2311.1178

Metrological power of incompatible measurements

We show that measurement incompatibility is a necessary resource to enhance the precision of quantum metrology. To utilize incompatible measurements, we propose a probabilistic method of operational quasiprobability (OQ) consisting of the measuring averages. OQ becomes positive semidefinite for some quantum states. We prove that Fisher information (FI), based on positive OQ, can be larger than the conventional quantum FI. Applying the proof, we show that FI of OQ can be extremely larger than quantum FI, when estimating a parameter encoded onto a qubit state with two mutually unbiased measurements. By adopting maximum likelihood estimator and linear error propagation methods, we illustrate that they achieve the high precision that our model predicts. This approach is expected to be applicable to improve quantum sensors

Understanding Cultural Issues in the Diabetes Self-Management Behaviors of Korean Immigrants

PURPOSE: The purpose of this study was to explore potential factors affecting self-management behaviors in Korean immigrants with type 2 diabetes mellitus (KIT2Ds). METHODS: A qualitative descriptive design guided this study. Semi-structured interviews lasting 45-60 minutes were conducted with 20 KIT2Ds in the participant’s preferred language; in all cases this was Korean. Each interview was audio-taped, transcribed, and analyzed using conventional content analysis. Data analysis was performed in two steps. The data written in Korean were initially analyzed by three bilingual researchers. A qualitative researcher then participated in the analysis to refine the findings for presentation to an English speaking audience while staying true to the data and preserving the nuanced Korean meanings. RESULTS: The mean age of the sample was 64. 5 ± 11.6 years (9 men and 11 women). The mean years of staying in the U. S. and age at diabetes mellitus diagnosis were 23.6 ± 9.7 years and 52.5 ± 12.3 years, respectively. Three major ideas were identified: (a) issues on treatment regimen related to both medications and diet, (b) resources that helped or hindered their ability to manage diabetes, and (c) the physician/patient relationship. CONCLUSIONS: There were important cultural nuances that need to be addressed to better prepare KIT2Ds to manage their diabetes more effectively. A culture specific program should extend beyond a diabetes self-management education delivered in Korean language. Rather, content and education methods need to consider acculturation effects on diabetes management behaviors

Implementation and evaluation of weather-responsive traffic management strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies

Publisher Correction: An engineered human Fc domain that behaves like a pH-toggle switch for ultra-long circulation persistence.

An amendment to this paper has been published and can be accessed via a link at the top of the paper