Exploring Unobserved Heterogeneity in Cyclists’ Occupying Motorized Vehicle Lane Behaviors at Different Bike Facility Configurations

Cyclists occupying motorized vehicle lanes disrupt road traffic order and increase collisions. Exploring the contributing factors could help develop countermeasures to regulate such behaviors. The purpose of this study is to explore the intrinsic features influencing the behavior of cyclists in occupying motorized vehicle lanes at different bicycle facilities. We investigated a total of 34,631 cycling behavior samples in the urban area of Pingdingshan, China. A Bayesian random parameter logit model was used to account for the unobserved heterogeneous effects. The experimental results of all bike facilities demonstrate that the bike type, dividing strip type, bike lane width, temporary on-street parking, and whether it is a working day significantly affect cyclists’ occupying motorized vehicle lane behaviors. Factors associated with unobserved heterogeneity are age, barriers dividing strip, vehicle lane numbers, bike volume, vehicle volume, and daily recording time intervals. Comparing the estimated model of five type bike lane facilities across different dividing strips, we find that cyclists have a significantly different occupying probability and the heterogeneity factors of the various bike facilities also have their focus. When the non-motorized road conditions become more open, the cyclist behavior becomes more random and the heterogeneity factors become broader.

Choice Experiment Performed on the Fresh Black Slavonian Pig’s Meat: A Preliminary Study

Until now, no research has been carried out in Croatia into consumer preferences for a particular agricultural and food product by a choice experiment. Therefore, little data are available about Croatian consumers' preferences for social concerns (sustainability, biodiversity, rural development and animal welfare) with regard to the consumer choice and behavior favoring local pig breeds, in this case the Black Slavonian Pig breed. A survey was the method used to collect the data, and a survey questionnaire was used as an instrument. The survey was performed on a sample of n = 100 Croatian consumers surveyed online using a hypothetical choice experiment. The data were analyzed using the three logit models: a multinomial logit model (MNL), random parameter logit (RPL), and an error component random parameter logit model (RPL-EC) in order to examine the consumers’ heterogeneous preferences for fresh ham meat of the Black Slavonian Pig. The results suggest that the Croatian consumers appreciated a darker red fresh pork meat than the one obtained from the Black Slavonian Pig reared outdoors and semi-outdoor. They also prefer a fresh meat bearing a geographical information label, such as the continental Croatia and continental Croatia + PDO, to a fresh meat without a label.

Mean Residual Lifetime Frailty Models: A Weighted Perspective

The mean residual life frailty model and a subsequent weighted multiplicative mean residual life model that requires weighted multiplicative mean residual lives are considered. The expression and the shape of a mean residual life for some semiparametric models and also for a multiplicative degradation model are given in separate examples. The frailty model represents the lifetime of the population in which the random parameter combines the effects of the subpopulations. We show that for some regular dependencies of the population lifetime on the random parameter, some aging properties of the subpopulations’ lifetimes are preserved for the population lifetime. We indicate that the weighted multiplicative mean residual life model generates positive dependencies of this type. The copula function associated with the model is also derived. Necessary and sufficient conditions for certain aging properties of population lifetimes in the model are determined. Preservation of stochastic orders of two random parameters for the resulting population lifetimes in the model is acquired.

Biases in Maximum Simulated Likelihood Estimation of Bivariate Models

This paper finds that the maximum simulated likelihood (MSL) estimator produces substantial biases when applied to the bivariate normal distribution. A specification of the random parameter bivariate normal model is considered, in which a direct comparison between the MSL and maximum likelihood (ML) estimators is feasible. The analysis shows that MSL produces biased results for the correlation parameter. This paper also finds that the MSL estimator is biased for the bivariate Poisson-lognormal model, developed by Munkin and Trivedi (1999. “Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models, with Application.” The Econometrics Journal 2: 29–48). A simulation study is conducted, which shows that MSL leads to serious inferential biases, especially large when variance parameters in the true data generating process are small. The MSL estimator produces biases in the estimated marginal effects, conditional means and probabilities of count outcomes.

Heterogeneity in Generational Effects: Case Study of Ride-hailing Behavior Among Millennials

This paper presents an investigation of the use and frequency of use of ride-hail services. In particular, we explored the role of generational effects and the heterogeneity involved in Millennials’ decision making when it comes to ride-hail choices. Using an ordered logistic regression structure, different statistical models were developed and tested, including fixed-effects and random parameter models, as well as the inclusion of interaction effects and attitudinal factors. Initial results from the fixed-effects model showed that the younger cohorts, including Millennials and Generation Z, showed a significantly positive preference for more frequent ride-hail use, whereas the older cohorts’ preferences (Generation X, Baby Boomers, and older) did not show any significant effects on ride-hail frequency. In the next step, the presence of heterogeneity among Millennials was tested using random parameters. The results confirmed that Millennials’ usage of ride-hail was heterogeneous, and this was statistically significant at the 90% confidence interval [Formula: see text]. To identify sources of heterogeneity, interaction effects were added to the model. Accordingly, use of ride-hail was more popular among middle-aged Millennials (30 to 34 years old) and Millennials with higher incomes. Likewise, attitudes such as cost sensitivity (toward private vehicle ownership), and being a rational user resulted in higher frequency ride-hail use across Millennials. On the contrary, unemployed Millennials were less likely to utilize ride-hail. The results from this study provide a more transparent picture of current ride-hail market segmentation, which could help predict the future market comprising autonomous vehicles and other emerging mobility options.

Exploring injury severity of pedestrian-vehicle crashes at intersections: unbalanced panel mixed ordered probit model

Although the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes.

Investigating the severity of expressway crash based on the random parameter logit model accounting for unobserved heterogeneity

The present study utilized a random parameter logit (RPL) model to explore the nonlinear relationship between explanatory variables and the likelihood of expressway crash severity. The potential unobserved heterogeneity of data brought by China’s road traffic characteristics was fully considered. A total of 1154 crashes happened on Hang-Jin-Qu Expressway from 2013 to 2018 were analyzed. In addition to the conventional impact factors considered in the past, variables related to road geometry were also introduced, which contributed to expressway accidents significantly. The overall stability of the model estimation was examined by likelihood ratio test. Then, the average elastic coefficient of the significant factors at each severity level was also calculated. Several factors that significantly increase the fatal crash probability were highlighted: rainy/snowy/cloudy weather condition, low visibility (100– m), night without light, wet-skid road surface, being female, aged 41+ years, collision with a rigid barrier and some other obstacles, radius and length of horizontal curve, and longitudinal gradient. The parameters of four factors were random and obeyed normal distribution: night without light, being female, driving experience with 10 + years and with large vehicle responsible. These findings provide insights for better understanding of expressway crash severity. Some countermeasures were proposed about driver education, traffic law enforcement, vehicle and road design, environmental improvement, and so on.

Noise-induced barren plateaus in variational quantum algorithms

Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations on VQA performance. We rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau (i.e., vanishing gradient). Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits n if the depth of the ansatz grows linearly with n. These noise-induced barren plateaus (NIBPs) are conceptually different from noise-free barren plateaus, which are linked to random parameter initialization. Our result is formulated for a generic ansatz that includes as special cases the Quantum Alternating Operator Ansatz and the Unitary Coupled Cluster Ansatz, among others. For the former, our numerical heuristics demonstrate the NIBP phenomenon for a realistic hardware noise model.