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Traffic density is one of the most important parameters to consider in the traffic operation field. Owing to limited data sources, traditional methods cannot extract traffic density directly. In the vehicular ad hoc network (VANET) environment, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction technologies create better conditions for collecting the whole time-space and refined traffic data, which provides a new approach to solving this problem.

On that basis, a real-time traffic density extraction method has been proposed, including lane density, segment density and network density. Meanwhile, using SUMO and OMNet++ as traffic simulator and network simulator, respectively, the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed.

Based on the simulation platform, a simulated intersection in Shanghai was developed to investigate the adaptability of the model.

Most research studies use separate simulation methods, importing trace data obtained by using from the simulation software to the communication simulation software. In this paper, the tight coupling simulation method is applied. Using real-time data and history data, the research focuses on the establishment and validation of the traffic density extraction model.

While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

Background: The COVID-19 pandemic has impacted global food systems and consumer eating habits. The current study explores how country of origin and ethical status information impacts attitudes toward food.

Methods: A within-subjects survey design explored how perceptions of food safety/risk, animal welfare, deliciousness, purchase intention, energy density, carbon footprint of three foods (chicken, pasta, apples) are influenced by country of origin and ethical status information (UK, EU, China, USA, Fairtrade, Organic). Data were collected from 701 UK-based participants using an online survey from the 25-30th March, following the UK lockdown (23 March 2020).

Results: Perceptions of food safety, animal welfare, purchase intention, deliciousness and carbon footprint are influenced by origin and ethical status information. Chicken from the USA and China is perceived to be higher risk and have lower animal welfare standards. Apples from the USA and China are perceived to be higher risk. Pasta from China is perceived to be higher risk. Energy density estimations are not influenced by origin and ethical status information.

Conclusions: Consumer perceptions are influenced by country of origin and ethical information; foods from China are perceived least favourably, followed by foods from the USA; foods from the UK, EU, Organic or Fairtrade are perceived more favourably. The impact of origin and ethical information varies by food type with the perception of some foods appearing less susceptible to influence. These findings have implications for post COVID-19 (and post Brexit) food system, trade policy and public trust, and highlight the need for communication of food safety.

The surveillance equipment is one of the most important parts for current air traffic control systems. It provides aircraft position and other relevant information including flight parameters. However, the existing surveillance equipment has certain position errors between true and detected positions. Operators must understand and account for the characteristics on magnitude and frequency of the position errors in the surveillance systems because these errors can influence the safety of aircraft operation. This study aims to develop the simulation model for analysis of these surveillance position errors to improve the safety of aircrafts in airports.

This study investigates the characterization of the position errors observed in airport surface detection equipment of an airport ground surveillance system and proposes a practical method to numerically reproduce the characteristics of the errors.

The proposed approach represents position errors more accurately than an alternative simple approach. This study also discusses the application of the computational results in a microscopic simulation modeling environment.

The surveillance error is analyzed from the radar trajectory data, and a random generator is configured to implement these data. These data are used in the air transportation simulation through an application programing interface, which can be applied to the aircraft trajectory data in the simulation. Subsequently, additionally built environment data are used in the actual simulation to obtain the results from the simulation engine.

The presented surveillance error analysis and simulation with its implementation plan are expected to be useful for air transportation safety simulations.

This paper aims to explore the development and application of place crowd safety management tools for areas of public assembly and major events, from a practitioner perspective.

The crowd safety risk assessment model is known as design, information, management-ingress, circulation, egress (DIM-ICE) (Still, 2009) is implemented to optimise crowd safety and potentially throughput. Three contrasting case studies represent examples of some of the world’s largest and most challenging crowd safety projects.

The paper provides some insight into how the DIM-ICE model can be used to aid strategic planning at major events, assess potential crowd risks and to avoid potential crowd safety issues.

It provides further clarity to what effective place management practice is. Evidence-based on the case studies demonstrates that the application of the DIM-ICE model is useful for recognising potential place crowd safety issues and identifying areas for require improvement.

Crowd science is an emerging field of research, which is primarily motivated by place crowd safety issues in congested places; the application and reporting of an evidence-based model (i.e. DIM-ICE model) add to this. The paper addresses a research gap related to the implementation of analytic tools in characterising place crowd dynamics.

The purpose of this paper is to analyze a variety of factors that can explain the differences in commercial bank efficiency among 17 countries in Latin America (LatAm).

In a first stage, data envelopment analysis (DEA) and conditional efficiency analysis techniques are used to assess the relative efficiency level of 409 banks for the 2014-2016 period. The conditional efficiency approach considers environmental variables (that are beyond the manager’s control), which could influence the shape and the level of the boundary of the attainable set. In the second stage, the resulting conditional efficiency scores are correlated with internal variables (those that are under the manager’s control), which might affect the distribution of the inefficiencies. For this purpose, an econometric approach developed by Simar and Wilson (2007) is used.

First stage scores reveal the heterogeneity of average efficiency within the region. Regarding the factors that may explain the differences in performance in the LatAm banking sector, the results allow us to state that certain internal variables such as bank size, the ratio of loans to total assets and the ratio of non-performing loans show the expected relationship to efficiency, in line with much of the previous literature.

This is the first time that conditional efficiency and Simar and Wilson (2007) approaches have been applied at the same time to analyse the LatAm banking industry.

This paper aims to study the affecting factors on bird nesting on electronic railway catenary lines and the impact of bird nesting events on railway operation.

First, with one year’s bird nest events in the form of unstructured natural language collected from Shanghai Railway Bureau, the records were structured with the help of python software tool. Second, the method of root cause analysis (RCA) was used to identify all the possible influencing factors which are inclined to affect the probability of bird nesting. Third, the possible factors then were classified into two categories to meet subsequent analysis separately, category one was outside factors (i.e. geographic conditions related factors), the other was inside factors (i.e. railway related factors).

It was observed that factors of city population, geographic position affect nesting observably. Then it was demonstrated that both location and nesting on equipment part have no correlation with delay, while railway type had a significant but low correlation with delay.

This paper discloses the principle of impacts of nest events on railway operation.

The purpose of this paper is to examine the influence of innovation indicators: Internet usage, patent rights, innovation in exporting countries and innovation in the importing country on the export competitiveness of firms in the East African Community (EAC).

The study adopted the structural gravity model and the Poisson Pseudo Maximum Likelihood a nonlinear estimation method that was applied in STATA on balanced panel data from 2007 to 2018. Data were obtained from World Bank International Trade Center and World Bank development indicators.

Results show that innovation in the importing country, innovation in the exporting country and patent rights of exports are positive and significant predictors of export competitiveness in developing countries. While Internet usage is an insignificant predictor in the EAC.

There is a need to examine the complicated nature of the EAC economy to further this study's findings.

Exporting countries need to take deeper reforms as regards structural transformation to enable firms to integrate into the Global Value Chains (GVCs) to enable them to increase their productivity by reviewing the existing policies to match the changes in the market.

This study explains the complex dynamic interactions of technological innovation indicators in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-oriented countries with less harmonization in their trade policies.

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

This study is an original presentation useful for new comers to the field.