Traffic congestion presents a critical challenge in contemporary urban environments, necessitating the development of effective traffic management systems. Microscopic traffic flow models, which offer detailed insights into individual vehicle dynamics such as car-following and lane-changing behaviors, are pivotal in addressing these challenges. However, a comprehensive review synthesizing the advancements and research trends in this field has been lacking. This paper presents a systematic review of major microscopic traffic flow research from 1950 to 2023. Our extensive search across multiple academic databases identifies significant methodologies and model equations, highlighting notable advancements in the field. The presentation reveals critical trends, including the integration of connected and autonomous vehicles, the application of machine learning techniques, and the increasing reliance on real-time data for traffic management. This paper provides a foundation for future research directions and contributes to the ongoing development of more efficient and sustainable urban traffic management strategies.
In this article, we present a new asymmetric distribution, the Topp-Leone modified Weighted Rayleigh (TLMWR) distribution, which extends the well-known Topp-Leone distribution. We derive several of its properties, including the probability density function, cumulative distribution function, survival function, failure (hazard) rate, moments, generating functions, quantile function, and order statistics. The model parameters are estimated by the method of maximum likelihood, and a simulation study is conducted to examine the finite-sample behavior of the estimators. We summarize key characteristics of the data using graphical displays and diagnostic procedures, including normality assessments and model-selection criteria. These analyses are performed on real-world data to assess the level and direction of skewness and kurtosis. The proposed distribution is then evaluated with a real-life dataset, and its performance is compared with existing and newly proposed distributions. The results support the validity of the proposed model and highlight its effectiveness relative to existing alternatives.
With rapid economic development and urbanization, many cities, particularly in China, face serious PM\(_{2.5}\) pollution issues. In this study, the city of Hefei is selected as the research area to investigate the factors influencing PM\(_{2.5}\) concentrations. Data on electricity consumption of major PM\(_{2.5}\)-emitting industries, meteorological factors (temperature, wind speed, wind direction, relative humidity), and atmospheric pollutant concentrations (NO\(_{2}\),SO\(_{2}\),O\(_{3}\),CO) are utilized to explore PM\(_{2.5}\) concentrations in Hefei from 2020 to 2021 using a generalized additive model (GAM). The aims are to identify the main influencing factors and potential control pathways for particulate matter pollution. Results reveal that CO accounts for 69% of the variation in PM\(_{2.5}\) mass concentration, suggesting it as the dominant factor in Hefei in 2020. Additionally, the major PM\(_{2.5}\)-emitting industries contribute to a 16% change in PM\(_{2.5}\) mass concentration, with a significant impact from smelting industries, which exhibit an increase in electricity consumption associated with an increase in PM\(_{2.5}\) mass concentration. Model fitting indicates that a 50% reduction in electricity consumption within the iron and steel making industries can lead to a 37% decrease in PM\(_{2.5}\) mass concentration compared to pre-reduction levels. Moreover, targeted control measures in winter result in higher reductions in PM\(_{2.5}\) pollution within a 40% reduction compared to consistent emission reductions throughout the year. These findings highlight the effectiveness of more focused control strategies based on localized circumstances. Implementing measures to restrict electricity use by key industries during high pollution seasons and in cities with high pollution levels can effectively address local PM\(_{2.5}\) pollution concerns.
