The increasing prevalence of electric vehicles (EVs) in urban logistics presents challenges such as route planning, energy constraints, and demand management. EVs’ limited range, charging requirements, and sensitivity to traffic conditions necessitate advanced optimization strategies. Fleet management systems are thus evolving into intelligent, modular platforms that not only plan delivery tasks but also interact with real-time data and respond to dynamic disruptions. Among these, traffic congestion remains a critical factor that can severely affect route reliability and lead to time window violations. In this study, a modular fleet management system architecture is proposed, capable of real-time monitoring, dynamic rerouting, and traffic-aware decision-making. The system introduces a standardized data structure called the Routing Markup Language (RML), which formalizes the communication between components and supports various route outputs including simulation and vehicle-level execution. Adaptive Large Neighborhood Search (ALNS) is applied for route planning using real-world order data from a water distribution company operating in the Büyükdere district of Eskişehir. The system also features a dynamic reassignment mechanism that responds to vehicle failure scenarios, ensuring continued operation with minimal disruption. Traffic scenarios are evaluated through the Simulation of Urban Mobility (SUMO) environment to assess route robustness under varying conditions. The proposed approach integrates routing optimization, dynamic disruption handling, and simulation-supported fleet monitoring into a cohesive system, offering a responsive and data-driven solution for sustainable urban logistics.
This study investigates the effects of velocity slip and convective boundary conditions on heat transfer and entropy generation in steady magnetohydrodynamic flow of a viscous, incompressible, electrically conducting fluid with internal heat generation/absorption, offering conditions relevant to microchannel cooling, porous heat exchangers, and energy system thermal management. The governing equations were transformed into coupled ordinary differential equations and solved analytically using the method of undetermined coefficients. The analytical solutions showed strong agreement with existing results, validating the model. Parametric analyses, supported by MATLAB visualizations, examined the influence of the magnetic field, slip coefficients, Biot number, and other parameters on flow, temperature distribution, and thermodynamic irreversibility. Results indicate that velocity decreases with increasing suction, magnetic intensity, and upper-wall slip, while temperature diminishes with higher Peclet number or injection velocity. Entropy generation is primarily governed by viscous and Joule dissipation, whereas wall convection and slip act as controlling mechanisms. The Bejan number analysis reveals that heat-transfer irreversibility predominates at higher magnetic parameters, while larger slip and Biot numbers enhance viscous effects and lower Bejan values. These findings have the potential to offer practical guidelines for designing efficient porous-channel cooling system components, particularly where control over wall slip and convective heat exchange is critical to minimizing energy loss and enhancing thermal performance.
The world continues to experience rising levels of crime, particularly in regions affected by socioeconomic disparity and structural inequality. To better understand and control these dynamics, we develop a nonlinear dynamical system of ordinary differential equations describing the evolution of crime within a population. The model divides the total population into five interacting compartments: \(\mathcal{S}_1(t)\) (not-at-risk individuals), \(\mathcal{S}_2(t)\) (at-risk individuals), \(\mathcal{C}(t)\) (active criminals), \(\mathcal{H}(t)\) (habitual offenders who are resistant to rehabilitation), and \(\mathcal{R}(t)\) (rehabilitated or reformed individuals). The influence of key behavioural transition parameters—notably the crime initiation rate \((\alpha)\) and the rate of recovery from the at-risk group \((\beta)\)—on the temporal evolution of each compartment is examined using numerical simulations. Line and contour plots demonstrate that increasing \(\alpha\) enhances the recruitment of at-risk individuals into criminal activity, thereby expanding both the criminal \((\mathcal{C})\) and habitual \((\mathcal{H})\) populations. In contrast, higher \(\beta\) values promote reintegration and reduce the size of the at-risk group \((\mathcal{S}_2)\). These results emphasize the significance of prevention-based interventions (reducing \(\alpha\)) and rehabilitation-oriented strategies (enhancing \(\beta\)) in curbing persistent crime. Furthermore, the basic reproduction number \((\mathcal{R}_c)\) is derived using the next-generation matrix approach to serve as a threshold indicator for crime persistence. Analytical and graphical sensitivity analyses reveal that \(\mathcal{R}_c\) is strongly influenced by the crime transmission rate \((\beta)\), the recruitment fraction into the at-risk class \((p)\), the natural exit rate \((\mu)\), the conviction rate \((\sigma)\), and the rate of progression to habitual criminality \((\eta)\). Contour and three-dimensional surface plots identify parameter regimes for which \(\mathcal{R}_c < 1\) (crime eradication) and \(\mathcal{R}_c > 1\) (crime persistence). The study concludes that reducing recruitment into at-risk groups, increasing conviction and natural exit rates, and minimizing habitual offender influence can effectively suppress criminal propagation, providing a quantitative foundation for evidence-based crime mitigation policies.
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.
